The San Juan Mountains in southwestern Colorado have long been known as a site of exceptionally voluminous mid-Tertiary volcanism, including at least 22 major ignimbrite sheets (each 150–5,000 km³) and associated caldera structures active at 34–23 Ma. Recent volcanologic and petrologic studies in the San Juan region have focused mainly on several ignimbrite-caldera systems: the southeastern area (Platoro complex), western calderas (Uncompahgre-Silverton-Lake City), and the central cluster (La Garita-Creede calderas).
Far less studied has been the northeastern San Juan region, which occupies a transition between earlier volcanism in central Colorado and large-volume younger ignimbrite-caldera foci farther south and west. This map is based on new field coverage of volcanic rocks in thirteen 7.5' quadrangles in northeastern parts of the volcanic field, high-resolution age determinations for 130 sites, and petrologic studies involving several hundred new chemical analyses. This mapping and the accompanying lab results (1) document volcanic evolution of the deeply eroded Bonanza caldera that exposes unique features not previously described from ignimbrite calderas elsewhere, as well as the previously unstudied Marshall Pass caldera; (2) provide unique cross-sectional exposures of the steeply resurgent Bonanza caldera, from volcanic floor and underlying basement rocks through a complete 3.5-km-thick section of intracaldera ignimbrite and overlying compositionally diverse caldera-filling lavas; (3) document timing of caldera collapse concurrently with eruption of about 1,000 km3 of ignimbrite that oscillated in composition from mafic dacite to rhyolite; (4) quantify the regional time-space-volume progression from the earlier Sawatch magmatic trend southward into the San Juan region; and (5) permit more rigorous comparison between the broad mid-Tertiary magmatic belt in the western U.S. Cordillera and the type continental-margin arc volcanism of the central Andes in South America.
Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
Tiger beetles inhabiting sandy beaches and cliffs along the east coast of the United States are facing increasing habitat loss due to erosion, urbanization, and sea level rise. The northeastern beach tiger beetle Cicindela dorsalis dorsalis and Puritan tiger beetle Cicindela puritana are both listed as threatened under the Endangered Species Act of 1973, while the white beach tiger beetle Cicindela dorsalis media is not listed but has been declining. Extirpation of these beetles, in some cases from entire states, has isolated many populations reducing gene flow and elevating the risk for the loss of genetic variation. To facilitate investigations of population genetic structure, we developed suites of microsatellite loci for conservation genetic studies.
Shotgun genomic sequencing of all species identified thousands of candidate microsatellite loci, among which 17 loci were optimized and verified to cross-amplify within C. d. media and C. d. dorsalis, and eight separate loci were optimized for C. puritana. Most loci conformed to Hardy–Weinberg equilibrium, showed no evidence of linkage disequilibrium or null alleles, and revealed population genetic characteristics informative for natural resource managers among the populations tested.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13104-020-04985-8","usgsCitation":"Aunins, A.W., Eackles, M.S., Kazyak, D., Drummond, M., and King, T.L., 2020, Development of microsatellite markers for three at risk tiger beetles Cicindela dorsalis dorsalis, C. d. media, and C. puritana: BMC Research Notes, v. 13, no. 1, 171, 10 p., https://doi.org/10.1186/s13104-020-04985-8.","productDescription":"171, 10 p.","ipdsId":"IP-111048","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457289,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-020-04985-8","text":"Publisher Index Page"},{"id":376950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drummond, Michael","contributorId":236902,"corporation":false,"usgs":false,"family":"Drummond","given":"Michael","email":"","affiliations":[],"preferred":false,"id":794652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794653,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210347,"text":"70210347 - 2020 - Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA","interactions":[],"lastModifiedDate":"2020-06-09T20:42:07.632544","indexId":"70210347","displayToPublicDate":"2020-03-21T16:16:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA","docAbstract":"Analytical methods that incorporate genetic data are increasingly used in monitoring and assessment programs for important rate functions of fish populations (e.g., recruitment). Because gear types vary in efficiencies and effective sampling areas, results from genetic‐based assessments likely differ depending on the sampling gear used to collect genotyped individuals; consequently, management decisions may also be affected by sampling gear. In this study, genetic pedigree analysis conducted on egg and larval Lake Sturgeon Acipenser fulvescens collected from the St. Clair–Detroit River system using three gear types was used to estimate and evaluate gear‐specific differences in the number of spawning adults that produced the eggs and larvae sampled (Ns), the effective number of breeding adults (Nb), and individual reproductive success. Combined across locations and sampling years, pooled estimates were 330 (Ns; point estimate) and 317 (Nb; 95% CI = 271–372). Mean reproductive success was 4.35 with a variance of 5.33 individuals/spawner. Mean ± SE estimated numbers of unique parents per genotyped egg or larva (i.e., adult detection rate) from 2015 samples were 1.140 ± 0.003 for vertically stratified conical nets, 0.836 ± 0.002 for D‐frame nets, and 0.870 ± 0.002 for egg mats. Using samples from 2016, adult detection rates were 0.823 ± 0.001 for D‐frame nets and 0.708 ± 0.001 for egg mat collections. Coancestry values were negatively correlated with adult detection rate. Although genetic pedigree analyses can improve the understanding of recruitment in fish populations, this study demonstrates that estimates from genetic analyses can vary with the targeted life stage (a biologically informative outcome) and sampling methodology. This study also highlights the influence of sampling methods on the interpretation of genetic pedigree analysis results when multiple gear types are used to collect individuals. Development of standardization approaches may facilitate spatial and temporal comparisons of genetic‐based assessment results.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10333","usgsCitation":"Hunter, R., Roseman, E., Sard, N.M., Hayes, D., Brenden, T.O., DeBruyne, R., and Scribner, K.T., 2020, Egg and larval collection methods affect spawning adult numbers inferred by pedigree analysis: North American Journal of Fisheries Management, v. 40, no. 2, p. 307-319, https://doi.org/10.1002/nafm.10333.","productDescription":"13 p.","startPage":"307","endPage":"319","ipdsId":"IP-111101","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457297,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10333","text":"Publisher Index Page"},{"id":382466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St Clair-Detroit River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.29833984375,\n 41.99624282178583\n ],\n [\n -82.232666015625,\n 41.99624282178583\n ],\n [\n -82.232666015625,\n 43.06086137134326\n ],\n [\n -83.29833984375,\n 43.06086137134326\n ],\n [\n -83.29833984375,\n 41.99624282178583\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunter, Robert D.","contributorId":237766,"corporation":false,"usgs":false,"family":"Hunter","given":"Robert D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":808654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":808655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sard, Nick M.","contributorId":237767,"corporation":false,"usgs":false,"family":"Sard","given":"Nick","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":808656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Daniel B.","contributorId":248252,"corporation":false,"usgs":false,"family":"Hayes","given":"Daniel B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":808657,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":808658,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeBruyne, Robin L.","contributorId":139752,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin L.","affiliations":[{"id":12902,"text":"MI State UNiversity","active":true,"usgs":false}],"preferred":false,"id":808659,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scribner, Kim T.","contributorId":95434,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":808660,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209094,"text":"ofr20201026 - 2020 - Evaluating dewatering approaches to protect larval Pacific lamprey","interactions":[],"lastModifiedDate":"2020-03-23T12:10:11","indexId":"ofr20201026","displayToPublicDate":"2020-03-20T14:19:59","publicationYear":"2020","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":"2020-1026","displayTitle":"Evaluating Dewatering Approaches to Protect Larval Pacific Lamprey","title":"Evaluating dewatering approaches to protect larval Pacific lamprey","docAbstract":"Larval Pacific lamprey live for several years burrowed in nearshore sediments where they filter feed on detritus and organic matter. Dewatering of larval habitat can occur as a result of flow-management practices, construction projects, or seasonal closures of irrigation diversions. Effective management of dewatering events requires guidance on approaches to protect lamprey, such as dewatering rates and light conditions (day or night) that allow lamprey the best opportunity to relocate water and avoid being stranded. We conducted controlled laboratory experiments comparing five dewatering rates (1, 1.8, 4, 8, and 16 inches per hour [in/h]) and two light conditions (light and dark) to evaluate their effectiveness in protecting larval lamprey. We used a tank with a simulated shoreline at a 10-percent slope filled with river sediment and manipulated the outflow to control the rate of dewatering until water was covering only the sediment in the lowest tank section, at the bottom of the slope. Following dewatering, larvae were classified as either stranded (in or on the substrate outside the watered area) or safe (relocated to the wetted area at the lower end of the tank). All study groups experienced high rates of stranding. The lowest stranding rates were for 1 in/h, in both light (77 percent) and dark (80 percent). Faster dewatering rates generally produced higher percentages of stranded fish, and both the dark and light trials at 16 in/h stranded all larvae. At each of the five dewatering rates, trials conducted in the dark stranded the same or higher proportions of fish than the corresponding trial conducted in the light, so there was no clear advantage to dewatering during dark conditions. The largest contribution to stranding rates for all study groups was the high number of larvae (50–80 percent) that did not initiate movement in response to dewatering and remained in the uppermost tank section where they were stocked at the start of the trials. The proportion of larvae that emerged from the sediment during dewatering trials was approximately 30 percent, and fish that emerged were consistently smaller than those that remained burrowed. Combining all dewatering rates, emergence was 31.3 percent for groups under dark conditions and 30.7 percent for groups under light conditions. We recorded the timing of emergence for 58 larvae and their median time to emerge (after the surface of the sediment in the uppermost tank section was dewatered) was 0.62 hour (h) (range 0–4.5 h). We measured larval movement rates and found that large fish moved faster than small fish. Differences in larval movement rate based on light condition were significant only for large fish, which had a significantly faster rate during light conditions. Larval lamprey moved, over short distances, at rates that exceeded the fastest dewatering rate we tested. The mean movement rates for groups ranged from 19.0 to 44.4 centimeters per minute [cm/min]) and the fastest dewatering rate (16 in/h) is equivalent to less than 1 cm/min. Only the slowest movement rate measured, 6.6 cm/min for one individual lamprey, was slower than the fastest dewatering rate.
We also investigated lamprey responses to a series of dewatering and rewatering events. Individual larvae were held in cylinders and exposed to four cycles of dewatering and rewatering using dewatering rates of 1 and 16 in/h and a rewatering rate of 2 in/h. Each dewatering rate was tested under both dark and light conditions. The location of fish, either on the surface of the sediment or burrowed, was recorded after each dewatering event for four rounds. The most common individual fish response for all study groups was to remain burrowed through all four rounds, and there were large differences in response between small and large larvae. Overall for small larvae, combining all groups, 14 of 28 fish emerged, and of those, 8 died and 1 was lethargic. The 1-in/h rate had 7 of the 8 mortalities, split about equally between the dark (3 fish) and light (4 fish) trials. All but one fish that died emerged from the sediment at some point during the four rounds of dewatering. Large larvae predominantly remained burrowed in all four rounds and did not experience any mortality. None of the large fish emerged for more than a single round, and emergence occurred only in the first and second rounds. Larvae emerged more quickly as the number of dewatering events increased. The mean time to emerge after the surface of the sediment in the tube was dewatered, combing all four groups, was 42 minutes (min) in round 1 (14 fish), 16 min in round 2 (5 fish), 11 min in round 3 (3 fish), and 8 minutes in round 4 (3 fish). When all groups and rounds of dewatering were combined, the overall mean time to emerge was 29 min (25 fish) and ranged from 1 min to 2 hours after the surface of the sediment was dewatered. Larvae burrowed deeper during the 1-in/h trials than the 16-in/h trials, and few fish were deeper than about 23 centimeters (cm). Large larvae burrowed deeper than small larvae. Small larvae were most concentrated from 0 to 7.6 cm (83.7 percent), and large fish were concentrated from 15.2 to 22.8 cm (43.3 percent). The second dewatering event resulted in greater mean burrowing depth than the first event, but trends after the second event were less clear.
Larval size played a role in lamprey responses to dewatering, having a significant effect on emergence, movement rate, and vertical distribution. The sediment used for laboratory testing or occupied by lamprey in the field appears to affect lamprey response to dewatering and deserves greater attention in future studies. Larvae were more active in the dark, but darkness did not consistently provide better outcomes (e.g., more emergence or reduced stranding) compared to daylight. An improved understanding of the cues that prompt larvae to emerge from the sediment, combined with the ability to manage dewatering rates, would be useful to guide future dewatering events to minimize negative effects to lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201026","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Fish and Wildlife Office, Portland, Oregon; and Columbia River Fish and Wildlife Conservation Office, Vancouver, Washington","usgsCitation":"Liedtke, T.L., Weiland, L.K., Skalicky. J.J., and Gray, A.E., 2020, Evaluating dewatering approaches to protect larval Pacific lamprey: U.S. Geological Survey Open-File Report 2020–1026, 32 p., https://doi.org/10.3133/ofr20201026.","productDescription":"iv, 32 p.","onlineOnly":"Y","ipdsId":"IP-113959","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1026/coverthb.jpg"},{"id":373418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1026/ofr20201026.pdf","text":"Report","size":"992 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1026"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The U.S. Geological Survey (USGS) determined the physical and chemical properties of more than 260 feed coal and coal combustion byproducts from two coal-fired power plants. These plants utilized a low-sulfur (0.23-0.47 wt. % S) and low ash (4.9-6.3 wt. % ash) subbituminous coal from the Wyodak-Anderson coal zone in the Tongue River Member of the Paleocene Fort Union Formation, Powder River Basin, Wyoming. Fifty-three samples of bituminous coal were collected and analyzed from a Kentucky power plant, which used several sources of bituminous coals from the Appalachian and Illinois Basins.
Based on scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses of feed coal samples collected and analyzed from 1996 through the late 2000s, two mineral suites were identified: (1) a primary or detrital suite consisting of quartz (including beta-form grains), biotite, feldspar, and minor zircon; and (2) a secondary authigenic mineral suite containing alumino-phosphates (crandallite and gorceixite), kaolinite, carbonates (calcite and dolomite), quartz, anatase, barite, and pyrite. The detrital mineral suite is interpreted, in part, to be of volcanic origin, whereas the authigenic mineral suite is interpreted, in part, to be the result of the alteration of the volcanic minerals. The mineral suites have contributed to the higher amounts of barium, calcium, magnesium, phosphorus, sodium, strontium, and titanium in the Powder River Basin feed coals in comparison to eastern US coals.
XRD analysis indicates that (1) fly ash is mostly aluminate glass, perovskite, lime, gehlenite, quartz, and phosphates with minor amounts of periclase, anhydrite, hematite, and spinel group minerals; and (2) bottom ash is predominantly quartz, plagioclase (albite and anorthite), pyroxene (augite and fassaite), rhodonite, and akermanite, and spinel group minerals. Microprobe and SEM analyses of fly ash samples revealed quartz, zircon, and monazite, euhedral laths of corundum with merrillite, hematite, dendritic spinels/ferrites, wollastonite, and periclase. The abundant calcium and magnesium mineral phases in the fly ash are attributed to the alteration of carbonate, clay, and phosphate minerals in the feed coal during combustion.
The calcium- and magnesium-rich and alumino-phosphate mineral phases in the coal combustion byproducts can be attributed to volcanic minerals deposited in peat-forming mires. Dissolution and alteration of these detrital volcanic minerals occurred either in the peat-forming stage or during coalification and diagenesis, resulting in the authigenic mineral suite.
The presence of free lime (CaO) in fly ash produced from Wyodak-Anderson coal acts as a self-contained “scrubber” for SO3, where CaO + SO3 form anhydrite either during combustion or in the upper parts of the boiler. Considering the high lime content in the fly ash and the resulting hydration reactions after its contact with water, there is little evidence that major amounts of leachable metals are mobilized in the disposal or utilization of this fly ash.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.31582/rmag.mg.57.3.199","usgsCitation":"Brownfield, M.E., 2020, Characterization of feed coals and coal combustion byproducts from the Wyodak-Anderson coal zone, Powder River Basin, Wyoming: Mountain Geologist, v. 57, no. 3, p. 199-240, https://doi.org/10.31582/rmag.mg.57.3.199.","productDescription":"42 p.","startPage":"199","endPage":"240","ipdsId":"IP-112921","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":386034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Campbell County","otherGeospatial":"Powder River basin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-105.0874,45.0001],[-105.0855,44.9688],[-105.0861,44.8811],[-105.0764,44.8818],[-105.0773,44.8014],[-105.0773,44.7868],[-105.0863,44.787],[-105.0869,44.7574],[-105.0869,44.7277],[-105.0869,44.699],[-105.0863,44.6533],[-105.0869,44.6387],[-105.0861,44.6118],[-105.0859,44.5283],[-105.0853,44.5118],[-105.0853,44.4977],[-105.086,44.4826],[-105.0817,44.1793],[-105.076,44.1791],[-105.0776,44.1409],[-105.0776,44.1263],[-105.0804,44.0033],[-105.0842,44.0029],[-105.0849,43.9414],[-105.0849,43.9268],[-105.0848,43.9154],[-105.0851,43.8936],[-105.0848,43.8411],[-105.0847,43.8275],[-105.0809,43.8269],[-105.0821,43.7395],[-105.0821,43.7103],[-105.0821,43.6807],[-105.0822,43.6652],[-105.0821,43.6511],[-105.0822,43.6356],[-105.0821,43.6211],[-105.082,43.5942],[-105.082,43.5646],[-105.082,43.55],[-105.082,43.5341],[-105.082,43.5195],[-105.0817,43.4981],[-105.242,43.4984],[-105.2616,43.4979],[-105.2818,43.4978],[-105.302,43.4978],[-105.3216,43.4977],[-105.3418,43.4981],[-105.362,43.4981],[-105.4018,43.498],[-105.5028,43.4977],[-105.5236,43.4976],[-105.6833,43.4973],[-106.0204,43.4946],[-106.0197,43.7619],[-106.0198,43.822],[-106.0084,43.8223],[-106.0082,43.8501],[-106.0084,43.8647],[-106.008,43.8792],[-106.0082,43.8938],[-106.0078,43.9958],[-106.0076,44.0227],[-106.0078,44.0373],[-106.008,44.0524],[-106.0082,44.0665],[-106.0078,44.082],[-106.0087,44.0961],[-106.0089,44.1107],[-106.0091,44.1253],[-106.0093,44.1403],[-106.0095,44.1545],[-106.0097,44.1695],[-106.0199,44.1697],[-106.0204,44.1966],[-106.0206,44.2112],[-106.0208,44.2257],[-106.0218,44.2996],[-106.0203,44.3748],[-106.0205,44.3894],[-106.0207,44.404],[-106.0203,44.4191],[-106.0194,44.4478],[-106.0196,44.4642],[-106.0198,44.4783],[-106.02,44.4934],[-106.0203,44.5066],[-106.0205,44.5208],[-106.0115,44.5211],[-106.0115,44.5653],[-106.0078,44.8423],[-106.0076,44.8715],[-106.0166,44.8716],[-106.0164,44.8999],[-106.0171,44.9437],[-106.0165,44.962],[-106.0168,44.9968],[-106.0007,44.9967],[-105.9331,44.9973],[-105.9226,45.0007],[-105.9196,45.0017],[-105.897,45.0017],[-105.7601,45.0016],[-105.6974,45.0017],[-105.694,45.0016],[-105.6768,45.0016],[-105.5876,45.0013],[-105.2874,45.0009],[-105.2834,45.0009],[-105.2674,45.0009],[-105.2468,45.0009],[-105.2381,45.0009],[-105.2268,45.0009],[-105.0874,45.0001]]]},\"properties\":{\"name\":\"Campbell\",\"state\":\"WY\"}}]}","volume":"57","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Brownfield, Michael E. 0000-0003-3633-1138 mbrownfield@usgs.gov","orcid":"https://orcid.org/0000-0003-3633-1138","contributorId":1548,"corporation":false,"usgs":true,"family":"Brownfield","given":"Michael","email":"mbrownfield@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":816670,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209418,"text":"70209418 - 2020 - Economic valuation of health benefits from using geologic data to communicate radon risk potential","interactions":[],"lastModifiedDate":"2023-12-01T21:15:36.519346","indexId":"70209418","displayToPublicDate":"2020-03-20T09:45:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5522,"text":"Environmental Health","onlineIssn":"1476-069X","active":true,"publicationSubtype":{"id":10}},"title":"Economic valuation of health benefits from using geologic data to communicate radon risk potential","docAbstract":"Background: Radon exposure is the second leading cause of lung cancer worldwide and represents a major health concern within and outside the United States. Mitigating exposure to radon is especially critical in places with high rates of tobacco smoking (e.g., Kentucky, USA), as radon-induced lung cancer is markedly greater among people exposed to tobacco smoke. Despite homes being a common source of radon exposure, convincing homeowners to test and mitigate for radon remains a challenge. A new communication strategy to increase radon testing among Kentucky homeowners utilizes fine-scale geologic map data to create detailed radon risk potential maps. We assessed the health benefits of this strategy via avoided lung cancer and associated premature mortality and quantified the economic value of these benefits to indicate the potential utility of using geologic map data in radon communication strategies. Methods: We estimated the change in radon testing among all 120 counties in Kentucky following a new communication strategy reliant on geologic maps. We approximated the resultant potential change in radon mitigation rates and subsequent expected lung cancer cases and mortality avoided among smokers and non-smokers exposed to ≥4 pCi/L of radon in the home. We then applied the value of a statistical life to derive the economic value of the expected avoided mortality. Results: The new communication strategy is estimated to help 75 Kentucky residents in one year avoid exposure to harmful radon levels via increased testing and mitigation rates. This equated to the potential avoidance of approximately one premature death due to lung cancer, with a net present value of \\$3.4 to \\$8.5 million (2016 USD). Conclusions: Our analysis illustrates the potential economic value of health benefits associated with geologic map data used as part of a communication strategy conveying radon risk to the public. Geologic map data are freely available in varying resolutions throughout the United States, suggesting Kentucky’s radon communication strategy using geologic maps can be employed in other states to educate the public about radon. As this is only a single application, in a single state, the economic and health benefits of geologic map data in educating the public about radon are likely to exceed our estimates.
","language":"English","publisher":"Springer","doi":"10.1186/s12940-020-00589-8","usgsCitation":"Chiavacci, S.J., Shapiro, C.D., Pindilli, E., Casey, C.F., Rayens, M.K., Wiggins, A.T., Andrews, W.M., and Hahn, E.J., 2020, Economic valuation of health benefits from using geologic data to communicate radon risk potential: Environmental Health, v. 19, 36, 9 p., https://doi.org/10.1186/s12940-020-00589-8.","productDescription":"36, 9 p.","ipdsId":"IP-110968","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":457301,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12940-020-00589-8","text":"Publisher Index Page"},{"id":373860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0001-8465-8763","orcid":"https://orcid.org/0000-0001-8465-8763","contributorId":223855,"corporation":false,"usgs":false,"family":"Rayens","given":"Mary","email":"","middleInitial":"Kay","affiliations":[{"id":40779,"text":"University of Kentucky College of Nursing","active":true,"usgs":false}],"preferred":false,"id":786435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiggins, Amanda T.","contributorId":223881,"corporation":false,"usgs":false,"family":"Wiggins","given":"Amanda","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":786543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Andrews, William M. Jr.","contributorId":51406,"corporation":false,"usgs":true,"family":"Andrews","given":"William","suffix":"Jr.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":786544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hahn, Ellen J.","contributorId":223882,"corporation":false,"usgs":false,"family":"Hahn","given":"Ellen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":786545,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228430,"text":"70228430 - 2020 - Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes","interactions":[],"lastModifiedDate":"2022-02-10T15:05:58.812086","indexId":"70228430","displayToPublicDate":"2020-03-20T08:58:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes","docAbstract":"Boat electrofishing is often used to sample age-0 Muskellunge Esox masquinongy for indexing recruitment or evaluating stocking success. However, electrofishing samples typically result in low CPUE, prompting concerns regarding whether catch rates reflect actual abundance or whether boat electrofishing is generally ineffective for capturing age-0 Muskellunge (i.e., if fish are not being encountered by the gear). To address these concerns, we used radiotelemetry to evaluate the probability of encountering stocked age-0 Muskellunge (230–350 mm TL) during standardized fall electrofishing surveys in three Wisconsin lakes. Our approach also allowed us to evaluate short-term survival and dispersal from stocking locations. Despite limited dispersal (<2.5 km) from the stocking locations and relatively high short-term survival (75–94%) of radio-tagged fish, few age-0 Muskellunge were located within the path of the electrofishing boat (7–30%). Furthermore, the probability of encounter by boat electrofishing varied by as much as 6.3 times among lakes. Differences in encounter probability among lakes appeared to be related to lake basin and habitat characteristics. Overlays of electrofishing sampling effort and fish locations revealed that traditional shoreline electrofishing may not be an effective way of estimating age-0 Muskellunge CPUE. Modifications to electrofishing protocols, including increased effort in offshore areas and consideration of basin characteristics and habitat, may be needed to increase encounter probabilities and the utility of boat electrofishing for sampling age-0 Muskellunge.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10418","usgsCitation":"Dembkowski, D., Kerns, J.A., Easterly, E.G., and Isermann, D.A., 2020, Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes: North American Journal of Fisheries Management, v. 40, no. 2, p. 383-393, https://doi.org/10.1002/nafm.10418.","productDescription":"11 p.","startPage":"383","endPage":"393","ipdsId":"IP-111289","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Stella Lake, Twin Valley Lake, Upper Gresham Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.21207427978516,\n 45.70557989372282\n ],\n [\n -89.17963027954102,\n 45.70557989372282\n ],\n [\n -89.17963027954102,\n 45.72236042939562\n ],\n [\n -89.21207427978516,\n 45.72236042939562\n ],\n [\n -89.21207427978516,\n 45.70557989372282\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.75199222564697,\n 46.06087385306044\n ],\n [\n -89.723,\n 46.06087385306044\n ],\n [\n -89.723,\n 46.07721993221842\n ],\n [\n -89.75199222564697,\n 46.07721993221842\n ],\n [\n -89.75199222564697,\n 46.06087385306044\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.10273933410645,\n 43.01569327500512\n ],\n [\n -90.08136749267578,\n 43.01569327500512\n ],\n [\n -90.08136749267578,\n 43.03708953184211\n ],\n [\n -90.10273933410645,\n 43.03708953184211\n ],\n [\n -90.10273933410645,\n 43.01569327500512\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Dembkowski, Daniel J.","contributorId":275781,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kerns, Janice A.","contributorId":275782,"corporation":false,"usgs":false,"family":"Kerns","given":"Janice","email":"","middleInitial":"A.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":275785,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma","email":"","middleInitial":"G.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834282,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210265,"text":"70210265 - 2020 - 40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan","interactions":[],"lastModifiedDate":"2020-05-27T13:53:26.508555","indexId":"70210265","displayToPublicDate":"2020-03-20T08:50:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan","docAbstract":"A spectacular feature of the Arabian-Nubian Shield (ANS) is the abundance of well-exposed and extensive Neoproterozoic dike swarms of multiple generations. These dikes are generally categorized into metamorphosed and unmetamorphosed post-orogenic dike swarms. The unmetamorphosed dikes in the northern ANS can be grouped into an old and young generations. We dated three dikes from the old generation of the unmetamorphosed dikes: a composite dike with latite margins and rhyolite core (607 ± 6 Ma, U-Pb), a biotite rhyolite dike (600 ± 4 Ma, 40Ar/39Ar age of biotite) and an andesite dike (594 ± 3, 40Ar/39Ar age of amphibole). We propose that these dikes representing the old generation were emplaced at different episodes extending approximately between 607 and 590 Ma. Time and composition equivalent plutonic rocks are common in Jordan and the northern ANS. These dikes crosscut the late to post-collisional granitoids and display a subduction-related character as evidenced from the Nb-Ta anomaly, suggesting a transitional magmatic activity from the orogenic to extensional environment. This generation of dikes is absent in the alkali feldspar A-type granite dated at 586 ± 5 Ma in Jordan and equivalent rocks in the northern ANS, which are crosscut only by the dolerite dikes which has an approximate crystallization age of ~579 Ma (40Ar/39Ar whole rock total gas age). Their within-plate character is supported by the absence of the Nb-Ta anomaly and the high field strength elements tectonic discrimination plots. We propose that these dolerite dikes represent the last Neoproterozoic igneous activity in the northern ANS, i.e. the magmatic activity was terminated ~50 m.y. before the estimated age of the Ram Unconformity at ~530 Ma. This age is in agreement with a previously suggested model of mantle lithosphere delamination from below the northern ANS after a significant crust-mantle thickening caused by the East African Orogeny. The thickening triggered exceptionally rapid uplift, followed by erosional unroofing of the ANS rocks, some lateral extension, and post-orogenic magmatic activity. This was followed by thermal relaxation and subsidence and the gradual denudation, erosion, and peneplanation that gradually developed until the approximate age of the unconformity at ~530 Ma.","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105714","usgsCitation":"Ghanem, H., McAleer, R.J., Jarrar, G.H., Al Hseinat, M., and Whitehouse, M., 2020, 40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan: Precambrian Research, v. 434, 105714, 21 p., https://doi.org/10.1016/j.precamres.2020.105714.","productDescription":"105714, 21 p.","ipdsId":"IP-112209","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":375071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Red Sea, Sinai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 34.541015625,\n 29.99300228455108\n ],\n [\n 34.27734375,\n 31.12819929911196\n ],\n [\n 31.289062500000004,\n 31.57853542647338\n ],\n [\n 30.41015625,\n 28.07198030177986\n ],\n [\n 32.16796875,\n 16.88865978738161\n ],\n [\n 42.1875,\n 9.709057068618208\n ],\n [\n 48.427734375,\n 8.494104537551882\n ],\n [\n 48.515625,\n 14.349547837185362\n ],\n [\n 41.484375,\n 24.686952411999155\n ],\n [\n 34.541015625,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"434","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ghanem, Hind","contributorId":189107,"corporation":false,"usgs":false,"family":"Ghanem","given":"Hind","email":"","affiliations":[],"preferred":false,"id":789842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":789843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarrar, Ghaleb H. 0000-0003-3424-3337","orcid":"https://orcid.org/0000-0003-3424-3337","contributorId":224974,"corporation":false,"usgs":false,"family":"Jarrar","given":"Ghaleb","middleInitial":"H.","affiliations":[{"id":35514,"text":"University of Jordan","active":true,"usgs":false}],"preferred":false,"id":789844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Al Hseinat, Mu’ayyad 0000-0003-3269-1144","orcid":"https://orcid.org/0000-0003-3269-1144","contributorId":224975,"corporation":false,"usgs":false,"family":"Al Hseinat","given":"Mu’ayyad","email":"","affiliations":[{"id":35514,"text":"University of Jordan","active":true,"usgs":false}],"preferred":false,"id":789845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitehouse, Martin 0000-0003-2227-577X","orcid":"https://orcid.org/0000-0003-2227-577X","contributorId":224976,"corporation":false,"usgs":false,"family":"Whitehouse","given":"Martin","email":"","affiliations":[{"id":39794,"text":"Swedish Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":789846,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70213189,"text":"70213189 - 2020 - Altered climate leads to positive density‐dependent feedbacks in a tropical wet forest","interactions":[],"lastModifiedDate":"2020-09-15T15:58:10.209633","indexId":"70213189","displayToPublicDate":"2020-03-20T08:45:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Altered climate leads to positive density‐dependent feedbacks in a tropical wet forest","docAbstract":"Climate change is predicted to result in warmer and drier Neotropical forests relative to current conditions. Negative density‐dependent feedbacks, mediated by natural enemies, are key to maintaining the high diversity of tree species found in the tropics, yet we have little understanding of how projected changes in climate are likely to affect these critical controls. Over 3 years, we evaluated the effects of a natural drought and in situ experimental warming on density‐dependent feedbacks on seedling demography in a wet tropical forest in Puerto Rico. In the +4°C warming treatment, we found that seedling survival increased with increasing density of the same species (conspecific). These positive density‐dependent feedbacks were not associated with a decrease in aboveground natural enemy pressure. If positive density‐dependent feedbacks are not transient, the diversity of tropical wet forests, which may rely on negative density dependence to drive diversity, could decline in a future warmer, drier world.
We present the first description of summer stream thermal regimes in Alaska using metrics that represent the magnitude, variability, frequency, duration, and timing of temperature events related to salmon life histories. We used cluster analysis to characterize thermal regimes present in the Matanuska-Susitna (Mat-Su) Basin based on 10 nonredundant temperature metrics and identified the most important drivers of our thermal regimes using random forests. Our results indicated four distinct thermal regimes among the 248 site-years in the Mat-Su Basin. Over 41% of site-years had cold-stable temperatures. An additional 22% of site-years had cold-variable temperatures and the latest timing of maximum stream temperatures. Twenty-eight percent of site-years had warm-variable temperatures that remained above 13°C for approximately two months. The remaining 9% of site-years had the warmest and most variable daily maximum temperatures, exceeding 18°C for almost one month, indicating potential impacts to spawning and rearing salmon. Climate and landscape drivers differentiating thermal regimes included spring and summer air temperatures, spring snowpack, summer precipitation, wetlands, and lakes. Climate change projections for 2050–2069 indicated a future shift toward warm thermal regimes and a reduced portfolio of thermal diversity. These results portend negative impacts to some salmon populations and stress the importance of prioritizing actions that maintain thermal regime diversity.
Since 2004, seven spawning reefs have been constructed in the St. Clair–Detroit River system to remediate lost spawning habitat and increase recruitment of Lake Sturgeon Acipenser fulvescens . Assessment of management actions by collecting and enumerating eggs and larvae provided evidence of spawning Lake Sturgeon and survival of eggs until larval dispersal at constructed reef sites. However, the number of spawners contributing sampled offspring (N s ), effective number of breeders (N b ), and extent of larval dispersal was unknown. Genetic reconstruction of familial relationships assigned eggs and larvae (n = 725) collected in 2015 and 2016 to full‐ and half‐sibling groups and estimated N s , N b , and genetic connectivity. We used a modified COLONY simulation module to simulate and convert 18 microsatellite loci (13 disomic and 5 polysomic) to 205 dominant present/absent markers to increase marker number and familial assignment accuracy in family reconstruction analysis. We assessed COLONY's ability to accurately infer familial relationships across small (n = 50), moderate (n = 125), and large (n = 750) larval sample sizes using two assumed allele frequency distributions for polysomic loci. We found that with fewer offspring sampled, COLONY underestimated N s and with large sample sizes overestimated N s . However, estimates were usually within 12–16% of the simulated true N s . Across reefs, estimates of N s were 151 in 2015 and 208 in 2016, and N b was similar (158 in 2015 and 198 in 2016). Evidence of full‐ and half‐sibling larvae collected at multiple locations indicated that individual Lake Sturgeon spawned at multiple locations within years and larvae dispersed considerable distances. Estimating N s , N b , larval dispersal, and inferred genetic connectivity between locations provides managers with population demographic parameters to assess habitat remediation projects. Continued monitoring, including genetic family reconstruction, may provide insight into the long‐term effects of constructed spawning habitat on recruitment and population‐level genetic diversity.
Nonlethal tools (plasma sex steroid concentrations and ultrasound) for assigning sex and reproductive condition in Burbot Lota lota from Lake Roosevelt, Washington, were assessed. Gonadal tissue, blood plasma, and gonadal sonograms were collected monthly from November 2016 to March 2018. Gametogenesis was described by gonadal histology during an entire reproductive cycle to confirm sex and reproductive condition. Plasma testosterone (T) and estradiol-17β (E2) concentrations were measured by radioimmunoassay. Plasma 11-ketotestosterone (11-KT) concentrations were measured by liquid chromatography–mass spectrometry. Plasma sex steroid profiles, gonadosomatic index, and ovarian follicle diameter were also described during an entire reproductive cycle. Plasma 11-KT concentration was used to assign sex with 82% accuracy during the entire reproductive cycle, and plasma 11-KT and E2 concentrations were used to assign sex with 98% accuracy when fish were reproductive (i.e., November–March in Lake Roosevelt). Plasma T and E2 concentrations were used to assign reproductive condition in females with 98% accuracy, and plasma T concentration was used to assign reproductive condition in males with 90% accuracy. Ultrasound was used to assign sex with 96% accuracy but was not useful for assigning reproductive condition. Nonlethal tools to assign sex and reproductive condition will enable fisheries biologists to assess reproductive indices of the Burbot population in Lake Roosevelt to inform management decisions.
The American sand lance (Ammodytes americanus, Ammodytidae) and the Northern sand lance (A. dubius, Ammodytidae) are small forage fishes that play an important functional role in the Northwest Atlantic Ocean (NWA). The NWA is a highly dynamic ecosystem currently facing increased risks from climate change, fishing and energy development. We need a better understanding of the biology, population dynamics and ecosystem role of Ammodytes to inform relevant management, climate adaptation and conservation efforts. To meet this need, we synthesized available data on the (a) life history, behaviour and distribution; (b) trophic ecology; (c) threats and vulnerabilities; and (d) ecosystem services role of Ammodytes in the NWA. Overall, 72 regional predators including 45 species of fishes, two squids, 16 seabirds and nine marine mammals were found to consume Ammodytes. Priority research needs identified during this effort include basic information on the patterns and drivers in abundance and distribution of Ammodytes, improved assessments of reproductive biology schedules and investigations of regional sensitivity and resilience to climate change, fishing and habitat disturbance. Food web studies are also needed to evaluate trophic linkages and to assess the consequences of inconsistent zooplankton prey and predator fields on energy flow within the NWA ecosystem. Synthesis results represent the first comprehensive assessment of Ammodytes in the NWA and are intended to inform new research and support regional ecosystem‐based management approaches.
","language":"English","publisher":"Wiley","doi":"10.1111/faf.12445","usgsCitation":"Staudinger, M., Goyert, H., Suca, J., Coleman, K., Welch, L., Llopiz, J., Wiley, D., Altman, I., Applegate, A., Auster, P., Baumann, H., Beaty, J., Boelke, D., Kaufman, L., Loring, P., Moxley, J., Paton, S., Powers, K., Richardson, D., Robbins, J., Runge, J., Smith, B., Spiegel, C., and Steinmetz, H., 2020, The role of sand lances (Ammodytes sp.) in the Northwest Atlantic Ecosystem: A synthesis of current knowledge with implications for conservation and management: Fish and Fisheries, v. 21, no. 3, p. 522-556, https://doi.org/10.1111/faf.12445.","productDescription":"35 p.","startPage":"522","endPage":"556","ipdsId":"IP-112301","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":457308,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/faf.12445","text":"Publisher Index 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Ultimately, these insights can lead to groups of local stakeholders taking action to build their resilience to a changing climate. Using CE, decision-makers can visualize decade-by-decade changes in climate conditions in their county and the magnitude of changes projected for the end of this century under two plausible emissions pathways. They can also check how projected changes relate to user-defined thresholds that represent points at which valued assets may become stressed, damaged, or destroyed. By providing easy access to authoritative information in an elegant interface, the Climate Explorer can help communities recognize—and prepare to avoid or respond to—emerging climate hazards. Another important step in the evolution of CE builds on the purposeful alignment of the CRT with the U.S. Global Change Research Program’s (USGCRP) National Climate Assessment (NCA). By closely linking these two authoritative resources, we envision that users can easily transition from static maps and graphs within NCA reports to dynamic, interactive versions of the same data within CE and other resources within the CRT, which they can explore at higher spatial scales or customize for their own purposes. The provision of consistent climate data and information—a result of collaboration among USGCRP’s federal agencies—will assist decision-making by other governmental entities, nongovernmental organizations, businesses, and individuals.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0298.1","collaboration":"","usgsCitation":"Lipschultz, F., Herring, D., Ray, A.J., Alder, J.R., Dahlman, L., DeGaetano, A., Fox, J.F., Gardiner, E., Herring, J., Hicks, J., Melton, F., Morefield, P.E., and Sweet, W., 2020, Climate explorer: Improved access to local climate projections, v. 101, no. 3, p. e265-e273, https://doi.org/10.1175/BAMS-D-18-0298.1.","productDescription":"9 p.","startPage":"e265","endPage":"e273","ipdsId":"IP-091613","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Implementation of a surface water extent model in Cambodia using cloud-based remote sensing","docAbstract":"Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.","language":"English","publisher":"MDPI","doi":"10.3390/rs12060984","usgsCitation":"Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a surface water extent model in Cambodia using cloud-based remote sensing: Remote Sensing, v. 12, no. 6, 984, https://doi.org/10.3390/rs12060984.","productDescription":"984","ipdsId":"IP-115688","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":457313,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12060984","text":"Publisher Index Page"},{"id":437053,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LH9YYF","text":"USGS data release","linkHelpText":"Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps"},{"id":373394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cambodia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[103.49728,10.63256],[103.09069,11.15366],[102.58493,12.18659],[102.3481,13.39425],[102.98842,14.22572],[104.28142,14.41674],[105.21878,14.27321],[106.04395,13.88109],[106.49637,14.57058],[107.38273,14.20244],[107.61455,13.53553],[107.4914,12.33721],[105.81052,11.56761],[106.24967,10.96181],[105.19991,10.88931],[104.33433,10.48654],[103.49728,10.63256]]]},\"properties\":{\"name\":\"Cambodia\"}}]}","volume":"12","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E. 0000-0001-8932-077X","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":219707,"corporation":false,"usgs":false,"family":"Petrakis","given":"Roy","email":"","middleInitial":"E.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":785152,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209164,"text":"70209164 - 2020 - Quantifying interregional flows of multiple ecosystem services – A case study for Germany","interactions":[],"lastModifiedDate":"2020-03-20T06:39:18","indexId":"70209164","displayToPublicDate":"2020-03-19T18:54:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1841,"text":"Global Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying interregional flows of multiple ecosystem services – A case study for Germany","docAbstract":"Despite a growing number of national-scale ecosystem service (ES) assessments, few studies consider the impacts of ES use and consumption beyond national or regional boundaries. Interregional ES flows – ecosystem services “imported” from and “exported” to other countries – are rarely analyzed and their importance for global sustainability is little known. Here, we provide a first multi-ES quantification of a nation's use of ES from abroad. We focus on ES flows that benefit the population in Germany but are supplied outside German territory. We employ a conceptual framework recently developed to systematically quantify interregional ES flows. We address four types of interregional ES flows with: (i) biophysical flows of traded goods: cocoa import for consumption; (ii) flows mediated by migratory species: migration of birds providing pest control; (iii) passive biophysical flows: flood control along transboundary watersheds; and (iv) information flows: China's giant panda loan to the Berlin Zoo. We determined that: (i) Ivory Coast and Ghana alone supply around 53% of Germany's cocoa while major negative consequences for biodiversity occurred in Cameroon and Ecuador; (ii) Africa´s humid and sub-humid climate zones are important habitats for the majority of migratory bird species that provide natural pest control services in agricultural areas in Germany; (iii) Upstream watersheds outside the country add an additional 64% flood regulation services nationally, while Germany exports 40% of flood regulation services in neighboring, downstream countries; (iv) Information flows transported by the pandas were mainly related to political aspects and - contrary to our expectations - considerably less on biological and natural aspects. We discuss the implications of these results for international resource management policy and governance.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloenvcha.2020.102051","usgsCitation":"Kleeman, J., Schroter, M., Bagstad, K.J., Kuhlicke, C., Kastner, T., Fridman, D., Schulp, C.J., Wolff, S., Martinez-Lopez, J., Koellner, T., Arnhold, S., Martin-Lopez, B., Marques, A., Lopez-Hoffman, L., Liu, J., Kissinger, M., Guerra, C., and Bonn, A., 2020, Quantifying interregional flows of multiple ecosystem services – A case study for Germany: Global Environmental Change, v. 61, 102051, https://doi.org/10.1016/j.gloenvcha.2020.102051.","productDescription":"102051","ipdsId":"IP-104288","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloenvcha.2020.102051","text":"Publisher Index Page"},{"id":373393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Germany","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[9.92191,54.9831],[9.93958,54.59664],[10.95011,54.36361],[10.93947,54.00869],[11.95625,54.19649],[12.51844,54.47037],[13.64747,54.07551],[14.11969,53.75703],[14.35332,53.24817],[14.07452,52.98126],[14.4376,52.62485],[14.68503,52.08995],[14.6071,51.74519],[15.017,51.10667],[14.57072,51.00234],[14.30701,51.11727],[14.05623,50.92692],[13.33813,50.73323],[12.96684,50.48408],[12.24011,50.26634],[12.41519,49.96912],[12.52102,49.54742],[13.03133,49.30707],[13.59595,48.87717],[13.24336,48.41611],[12.8841,48.28915],[13.02585,47.63758],[12.93263,47.46765],[12.62076,47.67239],[12.14136,47.70308],[11.42641,47.52377],[10.5445,47.5664],[10.40208,47.30249],[9.89607,47.5802],[9.59423,47.52506],[8.52261,47.83083],[8.3173,47.61358],[7.46676,47.62058],[7.59368,48.33302],[8.09928,49.01778],[6.65823,49.20196],[6.18632,49.4638],[6.24275,49.90223],[6.04307,50.12805],[6.15666,50.80372],[5.98866,51.85162],[6.5894,51.85203],[6.84287,52.22844],[7.09205,53.14404],[6.90514,53.48216],[7.10042,53.69393],[7.93624,53.7483],[8.12171,53.52779],[8.80073,54.02079],[8.57212,54.39565],[8.52623,54.96274],[9.28205,54.83087],[9.92191,54.9831]]]},\"properties\":{\"name\":\"Germany\"}}]}","volume":"61","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kleeman, Janina","contributorId":215954,"corporation":false,"usgs":false,"family":"Kleeman","given":"Janina","email":"","affiliations":[{"id":39336,"text":"Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":785177,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroter, Matthias 0000-0003-0207-7311","orcid":"https://orcid.org/0000-0003-0207-7311","contributorId":202612,"corporation":false,"usgs":false,"family":"Schroter","given":"Matthias","email":"","affiliations":[{"id":36494,"text":"UFZ – Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":785178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":785179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuhlicke, Christian 0000-0002-1193-228X","orcid":"https://orcid.org/0000-0002-1193-228X","contributorId":215955,"corporation":false,"usgs":false,"family":"Kuhlicke","given":"Christian","email":"","affiliations":[{"id":39336,"text":"Helmholtz Centre for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":785180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kastner, Thomas","contributorId":202618,"corporation":false,"usgs":false,"family":"Kastner","given":"Thomas","email":"","affiliations":[{"id":27439,"text":"Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":785181,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fridman, Dor 0000-0003-3908-3571","orcid":"https://orcid.org/0000-0003-3908-3571","contributorId":223486,"corporation":false,"usgs":false,"family":"Fridman","given":"Dor","email":"","affiliations":[{"id":36498,"text":"Ben-Gurion University of the Negev","active":true,"usgs":false}],"preferred":false,"id":785182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schulp, Catharina J. 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2020 - Reconnaissance of surface water estrogenicity and the prevalence of intersex in smallmouth bass (Micropterus dolomieu) inhabiting New Jersey","interactions":[],"lastModifiedDate":"2020-03-20T06:40:39","indexId":"70209145","displayToPublicDate":"2020-03-19T18:45:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2041,"text":"International Journal of Environmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Reconnaissance of surface water estrogenicity and the prevalence of intersex in smallmouth bass (Micropterus dolomieu) inhabiting New Jersey","docAbstract":"The observation of testicular oocytes in male fishes has been utilized as a biomarker of estrogenic endocrine disruption. A reconnaissance project led in the Northeastern United States (US) during the period of 2008–2010 identified a high prevalence of intersex smallmouth bass on or near US Fish & Wildlife Service National Wildlife Refuges that included the observation of 100% prevalence in smallmouth bass males collected from the Wallkill River, NJ, USA. To better assess the prevalence of intersex smallmouth bass across the state of New Jersey, a tiered reconnaissance approach was initiated during the fall of 2016. Surface water samples were collected from 101 (85 river, 16 lake/reservoir) sites across the state at base-flow conditions for estrogenicity bioassay screening. Detectable estrogenicity was observed at 90% of the sites and 64% were above the US Environmental Protection Agency trigger level of 1 ng/L. Median surface water estrogenicity was 1.8 ng/L and a maximum of 6.9 ng/L E2EqBLYES was observed. Adult smallmouth bass were collected from nine sites, pre-spawn during the spring of 2017. Intersex was identified in fish at all sites, and the composite intersex prevalence was 93.8%. Prevalence across sites ranged from 70.6% to 100%. In addition to intersex, there was detectable plasma vitellogenin in males at all sites. Total estrogenicity in surface water was determined at these fish collection sites, and notable change over time was observed. Correlation analysis indicated significant positive correlations between land use (altered land; urban + agriculture) and surface water estrogenicity. There were no clear associations between land use and organismal metrics of estrogenic endocrine disruption (intersex or vitellogenin). This work establishes a baseline prevalence of intersex in male smallmouth bass in the state of New Jersey at a limited number of locations and identifies a number of waterbodies with estrogenic activity above an effects-based threshold.","language":"English","publisher":"MDPI","doi":"10.3390/ijerph17062024","usgsCitation":"Iwanowicz, L., Smalling, K., Blazer, V., Braham, R.P., Sanders, L., Boetsma, A., Procopio, N., Goodrow, S., Buchanan, G., Millemann, D., Ruppel, B., Vile, J., Henning, B., and Abatemarco, J., 2020, Reconnaissance of surface water estrogenicity and the prevalence of intersex in smallmouth bass (Micropterus dolomieu) inhabiting New Jersey: International Journal of Environmental Research and Public Health, v. 17, no. 6, 2024, https://doi.org/10.3390/ijerph17062024.","productDescription":"2024","ipdsId":"IP-110398","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457318,"rank":0,"type":{"id":40,"text":"Open Access 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Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785108,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buchanan, Gary","contributorId":223463,"corporation":false,"usgs":false,"family":"Buchanan","given":"Gary","email":"","affiliations":[{"id":40718,"text":"New Jersey Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785109,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Millemann, Daniel","contributorId":223464,"corporation":false,"usgs":false,"family":"Millemann","given":"Daniel","email":"","affiliations":[{"id":40718,"text":"New Jersey Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785110,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ruppel, Bruce","contributorId":223465,"corporation":false,"usgs":false,"family":"Ruppel","given":"Bruce","email":"","affiliations":[{"id":40718,"text":"New Jersey Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785111,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vile, John","contributorId":223466,"corporation":false,"usgs":false,"family":"Vile","given":"John","email":"","affiliations":[{"id":40718,"text":"New Jersey Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785112,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Henning, Brian","contributorId":223467,"corporation":false,"usgs":false,"family":"Henning","given":"Brian","email":"","affiliations":[{"id":40718,"text":"New Jersey Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":785113,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Abatemarco, John","contributorId":223487,"corporation":false,"usgs":false,"family":"Abatemarco","given":"John","email":"","affiliations":[],"preferred":false,"id":785200,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70211978,"text":"70211978 - 2020 - A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health","interactions":[],"lastModifiedDate":"2020-08-12T22:51:49.456224","indexId":"70211978","displayToPublicDate":"2020-03-19T17:41:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1566,"text":"Environmental Science: Processes and Impacts","active":true,"publicationSubtype":{"id":10}},"title":"A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health","docAbstract":"Neonicotinoid insecticides are widely used in both urban and agricultural settings around the world. Historically, neonicotinoid insecticides have been viewed as ideal replacements for more toxic compounds, like organophosphates, due in part to their perceived limited potential to affect the environment and human health. This critical review investigates the environmental fate and toxicity of neonicotinoids and their metabolites and the potential risks associated with exposure. Neonicotinoids are found to be ubiquitous in the environment, drinking water, and food, with low-level exposure commonly documented below acceptable daily intake standards. Available toxicological data from animal studies indicate possible genotoxicity, cytotoxicity, impaired immune function, and reduced growth and reproductive success at low concentrations, while limited data from ecological or cross-sectional epidemiological studies have identified acute and chronic health effects ranging from acute respiratory, cardiovascular, and neurological symptoms to oxidative genetic damage and birth defects. Due to the heavy use of neonicotinoids and potential for cumulative chronic exposure, these insecticides represent novel risks and necessitate further study to fully understand their risks to humans.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/C9EM00586B","usgsCitation":"Lehmler, H., Kolpin, D.W., Hladik, M., Vargo, J.D., Schilling, K.E., LeFevre, G.H., Peeples, T.L., Poch, M.C., LaDuca, L.E., Cwiertny, D.M., and Field, R.W., 2020, A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health: Environmental Science: Processes and Impacts, v. 22, p. 1315-1346, https://doi.org/10.1039/C9EM00586B.","productDescription":"32 p.","startPage":"1315","endPage":"1346","ipdsId":"IP-116942","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":487009,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11755762","text":"External Repository"},{"id":377457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lehmler, Hans-Joachim","contributorId":238108,"corporation":false,"usgs":false,"family":"Lehmler","given":"Hans-Joachim","email":"","affiliations":[],"preferred":false,"id":796064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":201293,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vargo, John D.","contributorId":238109,"corporation":false,"usgs":false,"family":"Vargo","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":796067,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schilling, Keith E.","contributorId":106429,"corporation":false,"usgs":false,"family":"Schilling","given":"Keith","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796068,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":796069,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peeples, Tonya L.","contributorId":238110,"corporation":false,"usgs":false,"family":"Peeples","given":"Tonya","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":796070,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Poch, Matthew C.","contributorId":238111,"corporation":false,"usgs":false,"family":"Poch","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":796071,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"LaDuca, Lauren E.","contributorId":238112,"corporation":false,"usgs":false,"family":"LaDuca","given":"Lauren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796072,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":796073,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Field, R. William","contributorId":238113,"corporation":false,"usgs":false,"family":"Field","given":"R.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":796074,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70209339,"text":"70209339 - 2020 - Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining","interactions":[],"lastModifiedDate":"2020-05-05T17:16:53.604546","indexId":"70209339","displayToPublicDate":"2020-03-19T15:27:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining","docAbstract":"Selenium is highly elevated in Appalachian streams and stream organisms that receive alkaline mine drainage from mountaintop removal coal mining compared to unimpacted streams in the region. Adult aquatic insects can be important vectors of waterborne contaminants to riparian food webs, yet pathways of Se transport and exposure of riparian organisms are poorly characterized. We investigated Se concentrations in stream and riparian organisms to determine whether mining extent increased Se uptake in stream biofilms and insects and if these insects were effective Se biovectors to riparian spiders. Biofilm Se concentration increased (p = 0.006) with mining extent, reaching a maximum value of 16.5 μg/g of dw. Insect and spider Se increased with biofilm Se (p = 0.004, p = 0.003), reaching 95 and 26 μg/g of dw, respectively, in mining-impacted streams. Adult insect biomass was not related to mining extent or Se concentrations in biofilm. Even though Se concentrations in aquatic insects were significantly and positively related to mining extent, aquatic insect Se flux was not associated with mining extent because the mass of emerging insects did not change appreciably over the mining gradient. Insect and spider Se concentrations were among the highest reported in the literature, regularly exceeding the bird Se dietary risk threshold of 5 μg/g of dw. Risks of Se exposure and toxicity related to mining are thus not constrained to aquatic systems but extend to terrestrial habitats and food webs.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b05907","usgsCitation":"Naslund, L.C., Gerson, J.R., Brooks, A.C., Walters, D., and Bernhardt, E.S., 2020, Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining: Environmental Science & Technology, v. 54, no. 7, p. 3951-3959, https://doi.org/10.1021/acs.est.9b05907.","productDescription":"9 p.","startPage":"3951","endPage":"3959","ipdsId":"IP-112482","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":457323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.9b05907","text":"Publisher Index Page"},{"id":373727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Naslund, Laura C.","contributorId":223770,"corporation":false,"usgs":false,"family":"Naslund","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":786206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gerson, Jacqueline R.","contributorId":198378,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","email":"","middleInitial":"R.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false},{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":786207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, Alexander C.","contributorId":223771,"corporation":false,"usgs":false,"family":"Brooks","given":"Alexander","email":"","middleInitial":"C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":786208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":786205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernhardt, Emily S.","contributorId":173736,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":27285,"text":"Duke Univerisity","active":true,"usgs":false}],"preferred":false,"id":786209,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208050,"text":"sim3447 - 2020 - Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","interactions":[],"lastModifiedDate":"2022-04-22T20:02:50.44033","indexId":"sim3447","displayToPublicDate":"2020-03-19T13:23:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3447","displayTitle":"Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico","title":"Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","docAbstract":"This geologic map depicts and briefly describes geologic units underlying Petroglyph National Monument and immediately adjacent areas in Bernalillo County, New Mexico. The Monument is underlain dominantly by Quaternary basalts of the Albuquerque Volcanoes volcanic field, a series of basin-filling volcanic flows and associated vents from a monogenetic volcanic highland along the eastern margin of the Llano de Albuquerque. This compilation builds on data of previously published geologic maps and reports but includes new interpretive synthesis of volcanic stratigraphy and a unified representation of Quaternary surficial deposits overlying volcanic deposits within the Monument and areas immediately adjacent. This geologic map emphasizes the distribution of Quaternary volcanic vent areas and lava flow deposits which were incompletely mapped on previous publications. Surficial deposits are simplified, but uniformly mapped and described in contrast to varying map unit distributions, names and descriptions presented in the references above. Underlying deposits of the upper Santa Fe Group are exposed in the western part of the map area and described briefly.
North-trending, syn- and post-eruption faulting is well preserved in the volcanic field and reflected in the subsurface models of aeromagnetic data. These faults are dominated by dip-slip displacement and are interpreted as extensional faults of the central Albuquerque Basin of the northern Rio Grande rift. Elongate distribution of vents for most of the volcanic deposits are spatially associated with the easternmost of these faults and are interpreted to reflect eruptions from fissures paralleling the regional extensional fault trends of the rift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3447","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Thompson, R.A., Chan, C.F., Gilmer, A.K., and Shroba, R.R., 2020, Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3447, scale 1:24,000, https://doi.org/10.3133/sim3447.","productDescription":"2 Sheets: 50.50 inches x 40.00 inches; Data Release; ReadMe","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102605","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":373216,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LW817K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico"},{"id":373215,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447_georeferenced.pdf","text":"Sheet—Georeferenced geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"},{"id":399520,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109803.htm"},{"id":373213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3447/coverthb.jpg"},{"id":373222,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3447/ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3447 Read Me"},{"id":373214,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447.pdf","text":"Sheet—Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"}],"scale":"24000","country":"United States","state":"New Mexico","county":"Bernalillo County","otherGeospatial":"Petroglyph National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.79946899414062,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.097439809364204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Lakewide acoustic (AT) and bottom trawl (BT) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m and include 70 fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species (i.e., alewife, bloater, rainbow smelt, deepwater sculpin, slimy sculpin, round goby, ninespine stickleback) as well as for age-0 yellow perch and large burbot. The AT survey has been conducted each late summer/early fall since 2004, and the 2019 survey consisted of 26 transects [513 km total (319 miles)] covering bottom depths ranging from 15 to 235 m and 30 midwater trawl tows covering bottom depths ranging 27 to 204 m; this survey estimates densities of three prey fish species (i.e., alewife, bloater, and rainbow smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks.
For the BT survey, total biomass density of prey fish equaled only 1.77 kg/ha, the 2nd lowest estimate of the time series and well below the long-term average total biomass of 35.7 kg/ha. For the AT survey, total biomass density of prey fish equaled 4.71 kg/ha, just above the long-term average total biomass of 4.25 kg/ha. Both surveys reported bloater to be the dominant species (by biomass) among prey fishes. Mean biomass of yearling and older (YAO) alewives in 2019 was 1.56 kg/ha in the AT survey and 0.07 kg/ha in the BT survey. Comparing the acoustic estimate to previous years, YAO alewife biomass was 76% lower than the 2018 estimate and less than the average from 2004-2019. Numeric density of age-0 alewife from the AT survey was only 35.1/ha in 2019, which is indicative of a poor year-class and only the fourth since 2004 with a density less than 100/ha. The alewife age distribution remained truncated, with age-2 fish dominating the population and only three alewife (out of 525 aged) that were older than age 3. Biomass density of YAO bloater was 3.08 kg/ha in the AT survey and 0.78 kg/ha in the BT survey- each at least an order of magnitude lower than what was estimated by the BT survey between 1981 and 1998. Numeric density of age-0 bloater was the lowest ever measured for each survey: 0/ha for the AT survey and 0.12/ha for the BT survey. Biomass density of YAO rainbow smelt was 0.03 kg/ha in the AT survey and 0.04 kg/ha in the BT survey, continuing the low rainbow smelt biomass that has been observed since 2001. Numeric density of age-0 rainbow smelt was 1.33/ha in the AT survey and 0.99 in the BT survey, indicating a weak year-class that follows three year-classes that exceeded 41/ha between 2016 and 2018. All four prey fish species sampled only by the BT survey indicated below average biomass densities. Deepwater sculpin was estimated at 0.47 kg/ha, which makes 9 of the past 10 years when biomass was <1 kg/ha. Slimy sculpin was estimated at 0.02 kg/ha, the second lowest density ever measured. Round goby was estimated at 0.39 kg/ha, which was below the average biomass of 0.96 kg/ha since 2008. Ninespine stickleback were only caught in one tow, and not surprisingly was estimated at a record low biomass. Burbot biomass remained near record low levels, and no age-0 yellow perch were caught, indicating a weak yellow perch year-class in 2019.
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0000-0003-4939-5368","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":217346,"corporation":false,"usgs":true,"family":"Warner","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turschak, Ben","contributorId":257454,"corporation":false,"usgs":false,"family":"Turschak","given":"Ben","email":"","affiliations":[],"preferred":false,"id":920366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dieter, Patricia M. 0000-0003-1686-2679","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":217345,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Desorcie, Tim 0000-0002-9965-1668","orcid":"https://orcid.org/0000-0002-9965-1668","contributorId":346953,"corporation":false,"usgs":false,"family":"Desorcie","given":"Tim","affiliations":[],"preferred":false,"id":920368,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210163,"text":"70210163 - 2020 - A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","interactions":[],"lastModifiedDate":"2020-05-19T15:05:04.146927","indexId":"70210163","displayToPublicDate":"2020-03-19T09:58:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","docAbstract":"Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111752","usgsCitation":"Gao, F., Anderson, M., Daughtry, C.S., Karnieli, A., Hively, W.D., and Kustas, W.P., 2020, A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery: Remote Sensing of Environment, v. 242, 111752, 19 p., https://doi.org/10.1016/j.rse.2020.111752.","productDescription":"111752, 19 p.","ipdsId":"IP-113523","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111752","text":"Publisher Index Page"},{"id":374923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Beltsville Agricultural Research Center (BARC), Choptank River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.94412231445312,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 38.756225137839074\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng","contributorId":197297,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","affiliations":[],"preferred":false,"id":789358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":789359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daughtry, Craig S. T.","contributorId":211093,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S. T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":789360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karnieli, Arnon 0000-0001-8065-9793","orcid":"https://orcid.org/0000-0001-8065-9793","contributorId":224743,"corporation":false,"usgs":false,"family":"Karnieli","given":"Arnon","email":"","affiliations":[{"id":40930,"text":"Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel","active":true,"usgs":false}],"preferred":false,"id":789361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":789362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":789363,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211314,"text":"70211314 - 2020 - Mechanics of near-field deformation during co- and post-seismic shallow fault slip","interactions":[],"lastModifiedDate":"2020-07-23T20:28:56.833088","indexId":"70211314","displayToPublicDate":"2020-03-19T09:33:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Mechanics of near-field deformation during co- and post-seismic shallow fault slip","docAbstract":"Poor knowledge of how faults slip and distribute deformation in the shallow crust hinders efforts to mitigate hazards where faults increasingly intersect with the expanding global population at Earth’s surface. Here we analyze two study sites along the 2014 M 6.0 South Napa, California, earthquake rupture, each dominated by either co- or post-seismic shallow fault slip. We combine mobile laser scanning (MLS), active-source seismic tomography, and finite element modeling to investigate how deformation rate and mechanical properties of the shallow crust affect fault behavior. Despite four orders-of-magnitude difference in the rupture velocities, MLS-derived shear strain fields are remarkably similar at the two sites and suggest deceleration of the co-seismic rupture near Earth’s surface. Constrained by the MLS and seismic data, finite element models indicate shallow faulting is more sensitive to lithologic layering and plastic yielding than to the presence of fault compliant zones (i.e., regions surrounding faults with reduced stiffness). Although both elastic and elastoplastic models can reproduce the observed surface displacement fields within the uncertainty of MLS data, elastoplastic models likely provide the most reliable representations of subsurface fault behavior, as they produce geologically reasonable stress states and are consistent with field, geodetic, and seismological observations.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-61400-9","usgsCitation":"Nevitt, J., Brooks, B.A., Catchings, R.D., Goldman, M., Ericksen, T., and Glennie, C.L., 2020, Mechanics of near-field deformation during co- and post-seismic shallow fault slip: Scientific Reports, v. 10, 5031, 13 p., https://doi.org/10.1038/s41598-020-61400-9.","productDescription":"5031, 13 p.","ipdsId":"IP-099149","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":457326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-61400-9","text":"Publisher Index Page"},{"id":376665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Napa Fault Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65274047851562,\n 38.22199865889175\n ],\n [\n -122.21328735351562,\n 38.22199865889175\n ],\n [\n -122.21328735351562,\n 38.6897975322717\n ],\n [\n -122.65274047851562,\n 38.6897975322717\n ],\n [\n -122.65274047851562,\n 38.22199865889175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":793735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ericksen, Todd 0000-0001-9340-575X tericksen@usgs.gov","orcid":"https://orcid.org/0000-0001-9340-575X","contributorId":198145,"corporation":false,"usgs":true,"family":"Ericksen","given":"Todd","email":"tericksen@usgs.gov","affiliations":[],"preferred":true,"id":793749,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glennie, Craig L.","contributorId":198143,"corporation":false,"usgs":false,"family":"Glennie","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":793737,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249570,"text":"70249570 - 2020 - Detecting commonality in multidimensional fish movement histories using sequence analysis","interactions":[],"lastModifiedDate":"2023-10-17T12:00:36.066173","indexId":"70249570","displayToPublicDate":"2020-03-19T06:57:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Detecting commonality in multidimensional fish movement histories using sequence analysis","docAbstract":"Acoustic telemetry, for tracking fish movement histories, is multidimensional capturing both spatial and temporal domains. Oftentimes, analyses of such data are limited to a single domain, one domain nested within the other, or ad hoc approaches that simultaneously consider both domains. Sequence analysis, on the other hand, offers a repeatable statistical framework that uses a sequence alignment algorithm to calculate pairwise dissimilarities among individual movement histories and then hierarchical agglomerative clustering to identify groups of fish with similar movement histories. The objective of this paper is to explore how acoustic telemetry data can be fit to this statistical framework and used to identify commonalities in the movement histories of acoustic-tagged sea lamprey during upstream migration through the St. Clair-Detroit River System.
Five significant clusters were identified among individual fish. Clusters represented differences in timing of movements (short vs long duration in the Detroit R. and Lake St. Clair); extent of upstream migration (ceased migration in Lake St. Clair, lower St. Clair R., or upper St. Clair R.), and occurrence of fallback (return to Lake St. Clair after ceasing migration in the St. Clair R.). Inferences about sea lamprey distribution and behavior from these results were similar to those reached in a previous analysis using ad-hoc analysis methods.
The repeatable statistical framework outlined here can be used to group sea lamprey movement histories based on shared sequence characteristics (i.e., chronological order of “states” occupied). Further, this framework is flexible and allows researchers to define a priori the movement aspect (e.g., order, timing, duration) that is important for identifying both common or previously undetected movement histories. As such, we do not view sequence analysis as a panacea but as a useful complement to other modelling approaches (i.e., exploratory tool for informing hypothesis development) or a stand-alone semi-quantitative method for generating a simplified, temporally and spatially structured view of complex acoustic telemetry data and hypothesis testing when observed patterns warrant further investigation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-020-00195-y","usgsCitation":"Lowe, M.R., Holbrook, C., and Hondorp, D.W., 2020, Detecting commonality in multidimensional fish movement histories using sequence analysis: Animal Biotelemetry, v. 8, 10, 14 p., https://doi.org/10.1186/s40317-020-00195-y.","productDescription":"10, 14 p.","ipdsId":"IP-114379","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00195-y","text":"Publisher Index Page"},{"id":421938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan","otherGeospatial":"St. Clair River Detroit River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.33021246125514,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 42.011299379305854\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":886255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886257,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}