In accord with melanism being uncommon in birds, we could find only six published records of completely melanistic Common Murres Uria aalge, one of the most widely and intensively studied of all seabirds. We added to the record by observing a Common Murre in completely dark, melanistic alternate plumage every summer from 2017 to 2021 at Gull Island in Kachemak Bay, Alaska, USA. In 2017, the bird frequented the colony periphery, indicating that it could have been a subadult. Subsequently, it occupied the same narrow rock ledge within the colony every summer from 2018 to 2021, an indication that it may have been attempting to breed. Because we have been conducting long-term monitoring on Gull Island, we are in the unique position to be able to monitor the attendance and reproductive performance of this distinctively marked murre into the future.
","language":"English","publisher":"Marine Ornithology","usgsCitation":"Schoen, S.K., Arimitsu, M.L., Marsteller, C.E., and Heflin, B., 2022, Melanism in a Common Murre Uria aalge in Kachemak Bay, Alaska: Marine Ornithology: Journal of Seabird Research and Conservation, v. 50, no. 2, p. 225-227.","productDescription":"3 p.","startPage":"225","endPage":"227","ipdsId":"IP-144146","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":408488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":408447,"type":{"id":15,"text":"Index Page"},"url":"https://www.marineornithology.org/content/get.cgi?rn=1493"}],"country":"United States","state":"Alaska","otherGeospatial":"Gull Island, Kachemak Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.33068323135376,\n 59.583913819655386\n ],\n [\n -151.32615566253662,\n 59.583913819655386\n ],\n [\n -151.32615566253662,\n 59.585869193228504\n ],\n [\n -151.33068323135376,\n 59.585869193228504\n ],\n [\n -151.33068323135376,\n 59.583913819655386\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":854864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":854865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marsteller, Caitlin Elizabeth 0000-0002-2430-0708","orcid":"https://orcid.org/0000-0002-2430-0708","contributorId":251784,"corporation":false,"usgs":true,"family":"Marsteller","given":"Caitlin","email":"","middleInitial":"Elizabeth","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":854866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heflin, Brielle M.","contributorId":298015,"corporation":false,"usgs":false,"family":"Heflin","given":"Brielle M.","affiliations":[{"id":40349,"text":"USGS Alaska Science Center (former employee)","active":true,"usgs":false}],"preferred":false,"id":854867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237596,"text":"sir20225010 - 2022 - Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water","interactions":[],"lastModifiedDate":"2026-04-08T17:23:29.228484","indexId":"sir20225010","displayToPublicDate":"2022-10-14T12:12:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5010","displayTitle":"Sources and Characteristics of Dissolved Organic Carbon in the McKenzie River, Oregon, Related to the Formation of Disinfection By-Products in Treated Drinking Water","title":"Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water","docAbstract":"This study characterized the concentration and quality of dissolved organic carbon (DOC) in the McKenzie River, a relatively undeveloped watershed in western Oregon, and its link to forming disinfection by-products (DBPs) in treated drinking water. The study aimed to identify the primary source(s) of DOC in source water for the Eugene Water & Electric Board’s (EWEB) conventional treatment plant on the McKenzie River near river mile 11, upstream of Hayden Bridge. The two classes of regulated compounds examined—trihalomethanes (THMs) and haloacetic acids (HAAs)—form when organic carbon in raw source water reacts with chlorine and (or) bromine during water treatment.
The objectives of the study were to:
Eleven storms were sampled synoptically in upstream-to-downstream fashion to provide a “snapshot” of water quality conditions at four sites on the McKenzie River from Frissell Bridge (6 miles downstream from Trail Bridge Reservoir) to the EWEB water treatment plant at Hayden Bridge and nine contributing tributaries. Storms included late summer and early autumn “first flush” events and late autumn, winter, and spring storms spanning a range in streamflows from 3,000 to 26,000 cubic feet per second as measured in the main stem McKenzie River at the EWEB water intake.
Water samples were analyzed for DOC concentrations and optical properties (fluorescence and ultraviolet absorbance [UVA]) across a range of wavelengths to characterize the quantity and quality of dissolved organic matter (DOM) in the McKenzie River at the drinking water intake and upstream locations. Paired sets of source and finished water samples were collected at the EWEB treatment plant to identify DOC quality parameters in raw source water that might predict DBP concentrations in finished drinking water.
DOC concentrations were relatively low in the McKenzie River (0.4–3 milligrams per liter [mg/L]; average 1.5 mg/L) but much higher in the tributaries. The highest DOC concentrations occurred during “first flush” storms in October 2012 and September 2013; the highest value (16 mg/L) was measured at the 52nd Street stormwater outfall. The average DOC concentration in the lower basin-tributaries was 3.8 mg/L; three middle basin tributaries—Quartz, Gate, and Haagen Creeks, which drain private forestland with less coniferous forest compared with other higher elevation tributaries— had slightly lower average DOC concentrations (2.8 mg/L). These middle-basin watersheds may be important sources of DOC and DBP precursors to the McKenzie River, even more so than the lower basin tributaries, depending on their flows (and loads). This is particularly true after the September 2020 Holiday Farm fire, which burned much of this area.
DOC concentrations increased 68 percent in the McKenzie River between the uppermost reference site at Frissell Bridge and Vida; this includes drainage from Quartz Creek, Blue River Lake and Cougar Reservoir, which all contributed DOC to the main stem. In contrast, the lowermost tributaries draining most of the agricultural and urban land did not have a large effect on DOC in the McKenzie River despite their higher DOC concentrations because of their presumed relatively low streamflows and, consequently, DOC loads. Apart from the continuous flow monitors in the McKenzie River and some tributaries (Blue River and South Fork McKenzie River, and streamflow at Hayden Bridge and Vida, Camp Creek and some other locations), streamflow was not assessed during sample collection for this study. This lack of streamflow data precludes a detailed analysis of loads, which is discussed in the future studies section.
All DBP concentrations in finished drinking water were less than EPA maximum contaminant levels (MCLs) of 0.080 mg/L for the four trihalomethanes (THM4) and 0.060 mg/L for five haloacetic acids (HAA5). During the 11 storm sampling events the maximum summed concentrations were about 0.040 mg/L for both THM4 and HAA5. Compliance monitoring samples, collected separately by EWEB, yielded some higher concentrations—0.046 mg/L THM4 and 0.047 HAA5—during the December 2012 storm. The corresponding benchmark quotient (BQ) values, which indicate how close a measured DBP concentration is to the MCL, were 0.58 and 0.78, respectively, for THM4 and HAA5. Compared with a similar 2007–08 McKenzie River study that did not target storm events, concentrations of THM4 and HAA5 in finished water were 68 percent and 33 percent higher, respectively, during the current study.
Due to the high dilution rates in the McKenzie River main stem, many of the individual fluorescence excitation-emission measurements were low (<0.1 Raman units) and approached analytical detection limits. Parallel factor analysis (PARAFAC) resulted in a five-component model (C1–C5) that represents five unique organic fluorophores. Components C1, C2, and C3 represent DOM associated with soil-derived, humic-like, more degraded organic matter. In contrast, components C4 and C5 represent “fresher” DOM, derived from terrestrial and aquatic plants, including algae and cyanobacteria that are common in the McKenzie River and its tributaries and reservoirs. The fluorescence data and PARAFAC modeling suggest that most of the DOC in the McKenzie River originated from terrestrial sources (primarily components C1 and C2). The largest increases in DOC in the main stem occurred in the reach upstream of Vida, from inflows by Quartz Creek, Blue River, South Fork McKenzie River, and other tributaries.
Concentrations of DBPs in EWEB’s finished drinking water were positively correlated with DOC concentrations in raw source water (THM4, p<0.05; HAA5, p<0.01) for paired samples collected 12−24 hours apart. DOC concentrations were significantly positively correlated (p<0.001) with laboratory-based fluorescent dissolved organic matter (fDOM) measurements, suggesting fDOM as a useful parameter for monitoring and predicting DOC concentration in surface water and DBP concentrations in finished water.
Of all the PARAFAC components in surface water, C5 had the highest correlations with DBPs in finished water (rho = 0.77–0.84, p<0.01), followed by components C1 and C2 (rho = 0.75 and 0.71, respectively, p<0.01). This C5 carbon is associated with recently produced DOM, possibly from decomposed terrestrial and aquatic vegetation. Model loadings of these three components were considerably higher in the sampled tributaries relative to the main stem McKenzie River, with most of the observed increases in the main stem apparent at Vida. This points to Quartz Creek or other tributaries in the reach between Frissell Bridge and the sampling site near Vida (South Fork McKenzie and Blue Rivers) as potentially key contributors of DOM source material that leads to the production of DBPs in treated drinking water. A limited load analysis showed that the reservoirs contributed 8–37 percent of the instantaneous DOC loads observed at Vida at the time of sampling, which suggests other sources such as Quartz Creek and other streams in the reach between Frissell Bridge and Vida are more important.
Random forest analyses identified PARAFAC components C1 and C5 and fluorescence peaks A, C, M, T and N as the best predictors for HAA5 concentrations in finished drinking water, explaining 62.5 percent of the variation. The best predictors for THM4 were C1, C4 + C5, and peaks T, A, and N, which explained 33 percent of the variation.
Several land cover and vegetation classes were correlated with DOC concentration and other optical measurements. The percentage of evergreen forest in each of the subwatersheds sampled was negatively correlated (p<0.001) with DOC concentration and many optical indicators of DOM quantity: UVA254, fDOM, and all of the fluorescence peaks. In contrast, mixed (deciduous) forest was positively correlated (p<0.001) with DOC, fDOM, UVA254, and several fluorescence peaks, demonstrating the importance of deciduous leaf fall in generating DOC and DBP precursors.
The high level of human activities in the middle and lower portion of the basin—including timber harvesting and road construction on private forestland, agricultural, rural, industrial, and urban development—have resulted in the greatest loss in native coniferous and mixed deciduous forests in the basin. DOC loading from these tributaries and reservoir releases, which contain DOC from terrestrial and aquatic productivity, both enrich the McKenzie River. Concentrations of DOC increased an average of 71 percent (range 30–120 percent) in the McKenzie River between Frissell Bridge, the upstream reference site, and Vida. PARAFAC components C1, C2, and C5—which were correlated with DBPs in finished water—increased, on average, 109–136 percent (range 20–250 percent) in this same Frissell-to-Vida reach. These increases occur from input of tributaries in the middle basin such as Quartz Creek and others, as noted above.
Future monitoring, field, and lab studies can improve our understanding of seasonal and spatial sources of organic carbon contributing DBP precursors to the McKenzie River and allow detection of long-term trends resulting from the recent Holiday Farm Fire, which burned 173,393 acres of forestland, including riparian areas along the main stem, and numerous structures, homes, and outbuildings in September 2020. Future studies could examine DOC fluxes and flushing of carbon from the watershed, investigate the role of precipitation amount and intensity in mobilizing carbon and sediment, and evaluate impacts to aquatic communities and human health as part of a post-fire assessment. Other areas ripe for study include evaluating the impacts of potential temperature increases on carbon sequestration and decomposition in the burned and unburned forests and identifying practices that foster sequestration of carbon in forest soils.
The use of fluorescence sensors such as fDOM to monitor the concentration and composition of raw water supplies may be improved for detection of specific DBP precursors, to provide continuous and real-time information to treatment plant operators. Future studies that monitor DOM amount and quality, and DBP Formation Potential (FP), particularly during storm events, paired with streamflow measurements, as suggested above, could help identify areas that contribute high DOC loads and thus help managers identify the key areas to focus restoration activities. Other studies could examine treatment options for currently regulated DBPs and potentially unregulated compounds, including advanced biological treatments for their removal.
This study was a collaboration between the U.S. Geological Survey (USGS) and EWEB in Eugene, Oregon, with additional funding provided from USGS Cooperative Matching Funds Program.
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The Lesser Treeshrew, Tupaia minor Günther, 1876, is a small mammal from Southeast Asia with four currently recognized subspecies: T. m. minor from Borneo; T. m. malaccana from the Malay Peninsula; T. m. humeralis from Sumatra; and T. m. sincepis from Singkep Island and Lingga Island. A fifth subspecies, T. m. caedis, was previously synonymized with T. m. minor; it was thought to occur in northern Borneo and on the nearby islands of Banggi and Balambangan. These subspecies were originally differentiated based on pelage color, a plastic feature that has proven to be an unreliable indicator of taxonomic boundaries in treeshrews and other mammals. To explore infraspecific variation among T. minor populations across the Malay Peninsula, Borneo, Sumatra, and smaller islands, we conducted multivariate analyses of morphometric data collected from the hands and skulls of museum specimens. Principal component and discriminant function analyses reveal limited differentiation in manus and skull proportions among populations of T. minor from different islands. We find no morphometric support for the recognition of the four allopatric subspecies and no support for the recognition of T. m. caedis as a separate subspecies on Borneo.
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M.","contributorId":297914,"corporation":false,"usgs":false,"family":"Juman","given":"M. M.","affiliations":[{"id":64455,"text":"Yale University and Peabody Museum","active":true,"usgs":false}],"preferred":false,"id":854634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller-Murthy, A.","contributorId":297915,"corporation":false,"usgs":false,"family":"Miller-Murthy","given":"A.","affiliations":[{"id":64455,"text":"Yale University and Peabody Museum","active":true,"usgs":false}],"preferred":false,"id":854636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olson, Link E. 0000-0002-2481-5701","orcid":"https://orcid.org/0000-0002-2481-5701","contributorId":203887,"corporation":false,"usgs":false,"family":"Olson","given":"Link","email":"","middleInitial":"E.","affiliations":[{"id":36743,"text":"University of Alaska Museum, University of Alaska Fairbanks, Fairbanks, AK 99775, USA","active":true,"usgs":false}],"preferred":false,"id":854637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sargis, E. J.","contributorId":297916,"corporation":false,"usgs":false,"family":"Sargis","given":"E. J.","affiliations":[{"id":64455,"text":"Yale University and Peabody Museum","active":true,"usgs":false}],"preferred":false,"id":854638,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237081,"text":"70237081 - 2022 - Peer review by and for non-native English speakers: Interacting across international limnology societies","interactions":[],"lastModifiedDate":"2022-11-29T16:17:54.096824","indexId":"70237081","displayToPublicDate":"2022-10-14T10:15:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":940,"text":"Bulletin Limnology and Oceanography","onlineIssn":"1539-6088","active":true,"publicationSubtype":{"id":10}},"title":"Peer review by and for non-native English speakers: Interacting across international limnology societies","docAbstract":"Scholarly peer review is critical to the scientific process, yet there are limited resources available for students, postdocs, and other early career researchers (ECRs) to learn how to perform effective and time-efficient review. The ASLO Raelyn Cole Editorial Fellows have developed several peer review training resources, including a webinar (https://www.youtube.com/watch?v=utntl1VGy5g), editorial (Gradoville and Deemer 2022), and review (Falkenberg and Soranno 2018). One key issue identified in our webinar is that English language issues present significant challenges, both for authors and for reviewers. Indeed, the use of English as the primary language of science is a source of inequity (Ramírez-Castañeda 2020) that can lead to disadvantages for English as a second language (ESL) authors and reviewers.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography (ASLO)","doi":"10.1002/lob.10529","usgsCitation":"Gradoville, M.R., Deemer, B., and van Dorst, R.M., 2022, Peer review by and for non-native English speakers: Interacting across international limnology societies: Bulletin Limnology and Oceanography, v. 31, no. 4, p. 127-128, https://doi.org/10.1002/lob.10529.","productDescription":"2 p.","startPage":"127","endPage":"128","ipdsId":"IP-145179","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":409794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-10-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gradoville, Mary R.","contributorId":292580,"corporation":false,"usgs":false,"family":"Gradoville","given":"Mary","email":"","middleInitial":"R.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":853277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Dorst, Renee M.","contributorId":297097,"corporation":false,"usgs":false,"family":"van Dorst","given":"Renee","email":"","middleInitial":"M.","affiliations":[{"id":64290,"text":"Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":853279,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237609,"text":"70237609 - 2022 - Inversions of landslide strength as a proxy for subsurface weathering","interactions":[],"lastModifiedDate":"2022-10-14T15:17:04.758999","indexId":"70237609","displayToPublicDate":"2022-10-14T10:11:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Inversions of landslide strength as a proxy for subsurface weathering","docAbstract":"Distributions of landslide size are hypothesized to reflect hillslope strength, and consequently weathering patterns. However, the association of weathering and critical zone architecture with mechanical strength properties of parent rock and soil are poorly-constrained. Here we use three-dimensional stability to analyze 7330 landslides in western Oregon to infer combinations of strength - friction angles and cohesion - through analysis of both failed and reconstructed landslide terrain. Under a range of conditions, our results demonstrate that the failure envelope that relates shear strength and normal stress in landslide terrain is nonlinear owing to an exchange in strength with landslide thickness. Despite the variability in material strength at large scales, the observed gradient in proportional cohesive strength with landslide thickness may serve as a proxy for subsurface weathering. We posit that the observed relationships between strength and landslide thickness are associated with the coalescence of zones of low shear strength driven by fractures and weathering, which constitutes a first-order control on the mechanical behavior of underlying soil and rock mass.","language":"English","publisher":"Springer","doi":"10.1038/s41467-022-33798-5","usgsCitation":"Alberti, S., Leshchinksy, B., Roering, J., Perkins, J.P., and Olsen, M., 2022, Inversions of landslide strength as a proxy for subsurface weathering: Nature Communications, v. 13, 6049, 12 p., https://doi.org/10.1038/s41467-022-33798-5.","productDescription":"6049, 12 p.","ipdsId":"IP-132488","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446123,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-33798-5","text":"Publisher Index Page"},{"id":408324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range, Western Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.34326171874999,\n 41.97582726102573\n ],\n [\n -121.62963867187499,\n 41.9921602333763\n ],\n [\n -121.827392578125,\n 42.50450285299051\n ],\n [\n -121.86035156249999,\n 43.068887774169625\n ],\n [\n -121.48681640624999,\n 43.65197548731187\n ],\n [\n -121.212158203125,\n 44.38669150215206\n ],\n [\n -120.84960937499999,\n 45.00365115687186\n ],\n [\n -120.9814453125,\n 45.65244828675087\n ],\n [\n -121.81640624999999,\n 45.706179285330855\n ],\n [\n -122.288818359375,\n 45.56021795715051\n ],\n [\n -122.728271484375,\n 45.65244828675087\n ],\n [\n -122.794189453125,\n 45.94351068030587\n ],\n [\n -123.134765625,\n 46.17983040759436\n ],\n [\n -123.28857421875,\n 46.14178273759234\n ],\n [\n -123.46435546875,\n 46.24824991289166\n ],\n [\n -124.04663085937499,\n 46.240651955001695\n ],\n [\n -124.09057617187499,\n 45.30580259943578\n ],\n [\n -124.18945312500001,\n 44.6061127451739\n ],\n [\n -124.20043945312499,\n 43.98491011404692\n ],\n [\n -124.46411132812499,\n 43.30119623257966\n ],\n [\n -124.661865234375,\n 42.85180609584705\n ],\n [\n -124.51904296875,\n 42.35042512243457\n ],\n [\n -124.34326171874999,\n 41.97582726102573\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Alberti, Stefano 0000-0002-3756-0963","orcid":"https://orcid.org/0000-0002-3756-0963","contributorId":297918,"corporation":false,"usgs":false,"family":"Alberti","given":"Stefano","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":854646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leshchinksy, Ben 0000-0003-3890-1368","orcid":"https://orcid.org/0000-0003-3890-1368","contributorId":297919,"corporation":false,"usgs":false,"family":"Leshchinksy","given":"Ben","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":854647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":854648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olsen, Michael","contributorId":215348,"corporation":false,"usgs":false,"family":"Olsen","given":"Michael","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":854650,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242888,"text":"70242888 - 2022 - Survey of fragile geologic features and their quasi-static earthquake ground-motion constraints, southern Oregon","interactions":[],"lastModifiedDate":"2023-04-21T11:55:18.605787","indexId":"70242888","displayToPublicDate":"2022-10-14T06:53:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Survey of fragile geologic features and their quasi-static earthquake ground-motion constraints, southern Oregon","docAbstract":"Fragile geologic features (FGFs), which are extant on the landscape but vulnerable to earthquake ground shaking, may provide geological constraints on the intensity of prior shaking. These empirical constraints are particularly important in regions such as the Pacific Northwest that have not experienced a megathrust earthquake in written history. Here, we describe our field survey of FGFs in southern Oregon. We documented 58 features with fragile geometric characteristics, as determined from field measurements of size and strength, historical photographs, and light detection and ranging point clouds. Among the surveyed FGFs, sea stacks have particular advantages for use as ground‐motion constraints: (1) they are frequently tall and thin; (2) they are widely distributed parallel to the coast, proximal to the trench and the likely megathrust rupture surface; and (3) they are formed by sea cliff retreat, meaning that their ages may be coarsely estimated as a function of distance from the coast. About 40% of the surveyed sea stacks appear to have survived multiple Cascadia megathrust earthquakes. Using a quasi‐static analysis, we estimate the minimum horizontal ground accelerations that could fracture the rock pillars. We provide context for the quasi‐static results by comparing them with predictions from kinematic simulations and ground‐motion prediction equations. Among the sea stacks old enough to have survived multiple megathrust earthquakes (n = 16), eight yield breaking accelerations lower than the predictions, although they generally overlap within uncertainty. FGFs with the lowest breaking accelerations are distributed uniformly over 130 km of coastline. Results for inland features, such as speleothems, are in close agreement with the predictions. We conclude that FGFs show promise for investigating both past earthquake shaking and its spatial variability along the coasts of Oregon and Washington, where sea stacks are often prevalent. Future work can refine our understanding of FGF age and evolution.
Oil spills can inflict mortality and injury on bird populations; many of these deaths involve starvation resulting from thermoregulatory costs incurred by oiling of birds’ feathers. However, the fates and responses of sublethally oiled birds are poorly known. Due to this knowledge gap and the potential for birds to die far from the spill site, resource risk and injury assessors need tools to make informed estimates for delayed deaths and lost reproductive capacity in these birds. Focusing on the thermoregulatory cost of oiled feathers, we present a model addressing one facet of the effects of sublethal oiling on birds. Using mallard-like ducks as a model organism, we combined values from previous laboratory studies of oiled birds with a modified version of an existing temperature-influenced avian migration energetics model. Using this model, we examined the potential effects of oiling on general migration patterns, changes in energetic gains required to compensate for oiling, and starvation. We assessed all metrics across multiple oiling severities; we assessed starvation across both oiling severity and body condition. Median estimates for delays in spring migration were one to two months for trace and lightly oiled birds, and we predicted arrested spring migration in moderately oiled birds. Median estimates of required increases in energetic gains to offset costs of increased thermoregulation ranged from 20.3% to 88.6% depending on severity of oiling. We predicted starvation within four weeks for most combinations of oiling severity and body condition at the median predicted minimum wintering temperature of unoiled birds (-4.9°C). However, at the average winter temperature of the southernmost model latitude (10.8°C), we predicted only moderately oiled birds in less-than-excellent body condition had the potential to starve within a four-week time frame. Due to the potential for even trace oiling to delay spring migration and decrease body condition, the thermoregulatory costs of sublethal oiling during spring migration could reduce a bird's reproductive capacity. Future research integrating this initial energetics-based model into a spatially explicit, population scale migration model could provide additional insight into the potential effects of sublethal oiling on reproduction and survival. Such an integrated model could strengthen risk predictions and injury assessments for birds subjected to sublethal oiling.
The late Pleistocene was a climatically dynamic period, with abrupt shifts between cool-wet and warm-dry conditions. Increased effective precipitation supported large pluvial lakes and long-lived spring ecosystems in valleys and basins throughout the western and southwestern U.S., but the source and seasonality of the increased precipitation are debated. Increases in the proportions of C4/(C4+ C3) grasses in the diets of large grazers have been ascribed both to increases in summer precipitation and lower atmospheric CO2 levels. Here we present stable carbon and oxygen isotope data from tooth enamel of late Pleistocene herbivores recovered from paleowetland deposits at Tule Spring Fossil Beds National Monument in the Las Vegas Valley of southern Nevada, as well as modern herbivores from the surrounding area. We use these data to investigate whether winter or summer precipitation was responsible for driving the relatively wet hydroclimate conditions that prevailed in the region during the late Pleistocene. We also evaluate whether late Pleistocene grass C4/(C4+ C3) was higher than today, and potential drivers of any changes.
Tooth enamel δ18O values for Pleistocene Equus, Bison, and Mammuthus are generally low (average 22.0 ± 0.7‰, 2 s.e., VSMOW) compared to modern equids (27.8 ± 1.5‰), and imply lower water δ18O values (−16.1 ± 0.8‰) than modern precipitation (−10.5‰) or in waters present in active springs and wells in the Las Vegas Valley (−12.9‰), an area dominated by winter precipitation. In contrast, tooth enamel of Camelops (a browser) generally yielded higher δ18O values (23.9 ± 1.1‰), possibly suggesting drought tolerance. Mean δ13C values for the Pleistocene grazers (−6.6 ± 0.7‰, 2 s.e., VPDB) are considerably higher than for modern equids (−9.6 ± 0.4‰) and indicate more consumption of C4 grass (17 ± 5%) than today (4 ± 4%). However, calculated C4 grass consumption in the late Pleistocene is strikingly lower than the proportion of C4 grass taxa currently present in the valley (55–60%). δ13C values in Camelops tooth enamel (−7.7 ± 1.0‰) are interpreted as reflecting moderate consumption (14 ± 8%) of Atriplex (saltbush), a C4 shrub that flourishes in regions with hot, dry summers.
Lower water δ18O values, lower abundance of C4 grasses, and the inferred presence of Atriplex are all consistent with general circulation models for the late Pleistocene that show enhanced delivery of winter precipitation, sourced from the north Pacific, into the interior western U.S. but do not support alternative models that infer enhanced delivery of summer precipitation, sourced from the tropics. In addition, we hypothesize that dietary competition among the diverse and abundant Pleistocene fauna may have driven the grazers analyzed here to feed preferentially on C4 grasses. Dietary partitioning, especially when combined with decreased pCO2 levels during the late Pleistocene, can explain the relatively high δ13C values observed in late Pleistocene grazers in the Las Vegas Valley and elsewhere in the southwestern U.S. without requiring additional summer precipitation. Pleistocene hydroclimate parameters derived from dietary and floral records may need to be reevaluated in the context of the potential effects of dietary preferences and lower pCO2 levels on the stability of C3 vs. C4 plants.
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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
We present a model of the lower seismogenic depth of earthquakes in the western United States (WUS) estimated using the hypocentral depths of events M > 1, a crustal temperature model, and historical earthquake rupture depth models. Locations of earthquakes are from the Advanced National Seismic System Comprehensive Earthquake Catalog from 1980 to 2021 supplemented with seismicity in southern California for event hypocenters that were relocated by Hauksson et al. (2012) to obtain higher precision and better resolution in the model. We calculated the average depth of the deepest 10% of the merged catalog using an adaptive radius of 50 km or more. Along the San Andreas fault, the deepest seismogenic depths are located at 23 km around the Cholame segment, whereas the shallowest depths are located at about 10 km along the Rodgers Creek and Maacama faults. For the WUS outside California, the depth generally varies between 10 and 25 km with an average around 14 km but could extend to 35 km along Cascadia subduction zone. We find good agreement between the small‐magnitude depths and rupture depths derived from coseismic slip of large earthquakes across the region. Our estimates are generally deeper than the previous seismogenic depths determined for the Uniform California Earthquake Rupture Forecast, Version 3 model based on work by Petersen et al. (1996) who used seismicity cross sections along major fault zones in California. Our new seismogenic depth distribution correlates closely with crustal temperature derived from WUS heat flow (Blackwell et al., 2011). This correlation allowed us to develop a map of the brittle–ductile transition that we use to replace seismogenic depths in the model east of the Intermountain West Seismic Belt where the seismicity rate is low. This updated depth model is useful for recalibrating the lower geologic fault rupture depths, and constraining deformation and seismicity source models in updates of the U.S. Geological Survey National Seismic Hazard Model.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220174","usgsCitation":"Zeng, Y., Petersen, M.D., and Boyd, O.S., 2022, Lower seismogenic depth model of western U.S. Earthquakes: Seismological Research Letters, v. 93, no. 6, p. 3186-3204, https://doi.org/10.1785/0220220174.","productDescription":"19 p.","startPage":"3186","endPage":"3204","ipdsId":"IP-142152","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":408265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203125,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 29.84064389983441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":854485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":854486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237484,"text":"sir20225095 - 2022 - Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","interactions":[],"lastModifiedDate":"2024-05-07T20:58:03.278223","indexId":"sir20225095","displayToPublicDate":"2022-10-12T10:35:13","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5095","displayTitle":"Updated Annual and Semimonthly Streamflow Statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Southwestern Idaho, 2021","title":"Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Bureau of Land Management (BLM), continued streamflow data collection in water years 2013–21 to update daily streamflow regressions and annual and semimonthly streamflow statistics initially developed in 2012 for streams designated as “wild,” “scenic,” or “recreational” under the National Wild and Scenic Rivers System in the Owyhee Canyonlands Wilderness in southwestern Idaho. To sustain “outstanding remarkable values” in the Owyhee Canyonlands Wilderness, BLM determined that maintaining specific streamflow conditions in rivers was important for sustaining ecological health, recreational opportunities, and water demands for stock water and irrigation in a region with increased pressure from upstream land development. Streamflow statistics previously developed using regional regressions based on limited number of streamgages and generalized basin characteristics were determined to inaccurately represent hydrologic characteristics in the Owyhee Canyonlands Wilderness.
In this study, updated streamflow regressions and statistics are provided for 11 partial-record sites in the Owyhee Canyonlands Wilderness using 311 additional streamflow measurements. A partial-record Maintenance of Variance Extension, Type 1 (MOVE.1) streamflow regression method was used to relate discrete streamflow measurements collected at partial-record sites with daily mean streamflow at nearby index sites. The updated regressions were used to estimate a synthetic daily mean streamflow record at each partial-record site for the period of record of the selected index site. The computed synthetic streamflow record was then used to determine annual and semimonthly streamflow statistics at each partial-record site. Annual bankfull streamflow statistics were calculated at each partial-record site using the computed bankfull streamflow at the selected index site and the updated streamflow regression.
Additional streamflow measurements representing a larger range of hydrologic conditions since 2012, reevaluation of index site selection, and updated regression techniques improved streamflow statistic estimates in the Owyhee Canyonlands Wilderness. Regression performance was evaluated based on the coefficient of determination (R2) between the partial-record and index sites, percent bias, and similarity of basin characteristics between the selected index site and the partial-record site. Generally, the updated regressions performed well for partial-record sites with an index site located upstream or downstream on the same stream. Regression performance was degraded and less robust for index sites located farther away from the corresponding partial-record site. Additional streamflow measurements at partial-record sites with few measurements over a small range in hydrologic conditions could improve regression performance and reduce prediction intervals. Furthermore, additional index sites in the Owyhee Canyonlands Wilderness could improve the updated streamflow regressions and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225095","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Dudunake, T.J., and Ducar, S.D., 2022, Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021 (ver. 1.1, May 2024): U.S. Geological Survey Scientific Investigations Report 2022–5095, 31 p., https://doi.org/10.3133/sir20225095.","productDescription":"Report: viii, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128129","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":408220,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.XML"},{"id":408218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJA24","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow regressions and annual and semimonthly exceedance probability statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Idaho"},{"id":408217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5095"},{"id":408221,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5095"},{"id":408219,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5095/images"},{"id":428468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5095/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":408216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5095/coverthb2.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Owyhee Canyonlands Wilderness","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02636718749999,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
In Katmai National Park, Alaska, USA, we have seen changes in the number of brown bears and sea otters. The number of animals of a species a habitat can support is called carrying capacity. Even though bears live on land and sea otters live in the ocean, these two mammals share coastal habitats. Bears eat salmon, other fish, plants, clams, and beached whales. Sea otters feed on clams and other marine invertebrates. All these foods are influenced by the ocean. Recently, we have seen fewer bears but more sea otters! What changed? Many things, but several observations point to the ocean. There are fewer salmon, whales, and clams, so bears rely more on plants for food. Fewer clams mean sea otters must work harder to find food. Our studies are helping us to understand how and why carrying capacity for a given species may change over time.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frym.2022.715993","usgsCitation":"Coletti, H., Hilderbrand, G., Bodkin, J., Ballachey, B., Erlenbach, J., Esslinger, G.G., Hannam, M.P., Kloecker, K.A., Mangipane, B., Miller, A., Monson, D., Pister, B., Griffin, K., Bodkin, K., and Smith, T., 2022, Where land and sea meet: Brown bears and sea otters: Frontiers for Young Minds, HTML Document, https://doi.org/10.3389/frym.2022.715993.","productDescription":"HTML Document","ipdsId":"IP-128728","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":446135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frym.2022.715993","text":"Publisher Index Page"},{"id":408326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park","geographicExtents":"{\n 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Service","active":true,"usgs":false}],"preferred":false,"id":854693,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Griffin, K.","contributorId":297945,"corporation":false,"usgs":false,"family":"Griffin","given":"K.","email":"","affiliations":[],"preferred":false,"id":854698,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bodkin, K.","contributorId":297944,"corporation":false,"usgs":false,"family":"Bodkin","given":"K.","email":"","affiliations":[],"preferred":false,"id":854699,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Smith, Tom","contributorId":207440,"corporation":false,"usgs":false,"family":"Smith","given":"Tom","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":854694,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70237388,"text":"70237388 - 2022 - Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","interactions":[],"lastModifiedDate":"2022-10-17T16:42:25.152014","indexId":"70237388","displayToPublicDate":"2022-10-12T09:07:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","docAbstract":"This study investigates the applicability of the Landsat Dynamic Surface Water Extent (DSWE) science product for waterbird habitat modeling in multiple non-canopied habitat types. We compare surface water distribution estimates derived from DSWE to two site-specific survey methods: visual surveys and digitized aerial imagery. These site-specific surveys were conducted on Poplar Island, a restoration island project in the Chesapeake Bay, USA. Visual surveys were collected bimonthly from 2006 – 2013, and digitized aerial imagery was collected annually from 2006 – 2015. As a restoration island, Poplar Island presents a unique opportunity to analyze DSWE in a rapidly changing site. We structure our analysis based on the procedural development of individual sub-island cells developed from unconsolidated dredge material into fully restored wetlands that have independent hydrologic connection to the surrounding bay. Each development status is analyzed using our three DSWE classifications: Open Water (OW), a conservative estimate; Wetland Inclusive (WI), an aggressive estimate; and Development Dependent (DD), a landcover adaptive estimate. The OW classification consistently underestimates surface water coverage especially in the more complex, fully developed cells. The WI classification is better able to capture the tidal channels in these cells, but marginally overestimates surface water coverage in more sparsely vegetated cells. The DD classification does not significantly improve upon the estimations of the WI classification. Our data indicate that DSWE can be a capable alternative to our site-specific survey methods. However, the product is limited by Landsat’s 30 m spatial resolution, especially in more structurally complex wetlands. A recommended classification method for characterizing waterbird habitats would depend on the goals and targeted scale of analysis, for which DSWE may be a viable option.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100845","usgsCitation":"Taylor, J., Sullivan, J.D., Teitelbaum, C.S., Reese, J.G., and Prosser, D., 2022, Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats: Remote Sensing Applications: Society and Environment, v. 28, 100845, 9 p., https://doi.org/10.1016/j.rsase.2022.100845.","productDescription":"100845, 9 p.","ipdsId":"IP-139932","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2022.100845","text":"Publisher Index Page"},{"id":435658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SW505K","text":"USGS data release","linkHelpText":"Surface water estimates for a complex study site derived from traditional and emerging methods"},{"id":408211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Poplar Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.36236190795898,\n 38.74631848708898\n ],\n [\n -76.36373519897461,\n 38.754886481591335\n ],\n [\n -76.36905670166014,\n 38.7564928660758\n ],\n [\n -76.37231826782227,\n 38.754886481591335\n ],\n [\n -76.37884140014648,\n 38.762114927054405\n ],\n [\n -76.37969970703125,\n 38.76519348705099\n ],\n [\n -76.37712478637695,\n 38.76827191422227\n ],\n [\n -76.36356353759766,\n 38.78165483591876\n ],\n [\n -76.35824203491211,\n 38.78580303175893\n ],\n [\n -76.35772705078125,\n 38.793295935073516\n ],\n [\n -76.36510848999022,\n 38.797577240505625\n ],\n [\n -76.36974334716797,\n 38.79650693825768\n ],\n [\n -76.37042999267578,\n 38.79102138696157\n ],\n [\n -76.37128829956055,\n 38.78807774637309\n ],\n [\n -76.37060165405273,\n 38.784331118923134\n ],\n [\n -76.37334823608398,\n 38.78272536117434\n ],\n [\n -76.37695312499999,\n 38.78339443129763\n ],\n [\n -76.38141632080078,\n 38.78138720209366\n ],\n [\n -76.39291763305664,\n 38.767201172010736\n ],\n [\n -76.39120101928711,\n 38.75582354360097\n ],\n [\n -76.38656616210938,\n 38.7500671112174\n ],\n [\n -76.38364791870117,\n 38.743239112884744\n ],\n [\n -76.37678146362305,\n 38.741766321754575\n ],\n [\n -76.36236190795898,\n 38.74631848708898\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, John B.","contributorId":296294,"corporation":false,"usgs":false,"family":"Taylor","given":"John B.","affiliations":[{"id":64012,"text":"Chesapeake Bay Trust","active":true,"usgs":false}],"preferred":false,"id":854370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire S. 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":255382,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reese, Jan G.","contributorId":296295,"corporation":false,"usgs":false,"family":"Reese","given":"Jan","email":"","middleInitial":"G.","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237375,"text":"70237375 - 2022 - Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","interactions":[],"lastModifiedDate":"2022-10-12T14:07:03.951041","indexId":"70237375","displayToPublicDate":"2022-10-12T08:55:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","docAbstract":"Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.13582","usgsCitation":"Shriver, R., Yackulic, C., Bell, D.M., and Bradford, J., 2022, Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions: Global Ecology and Biogeography, v. 31, no. 11, p. 2259-2269, https://doi.org/10.1111/geb.13582.","productDescription":"11 p.","startPage":"2259","endPage":"2269","ipdsId":"IP-143036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435659,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FIGKFM","text":"USGS data release","linkHelpText":"Pinyon-juniper basal area, climate and demographics data from National Forest Inventory plots and projected under future density and climate conditions"},{"id":408210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, Washington, Wyoming","otherGeospatial":"Great Basin, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5751953125,\n 49.03786794532644\n ],\n [\n -119.64111328125,\n 48.38544219115483\n ],\n [\n -118.63037109375,\n 47.79839667295524\n ],\n [\n -117.44384765625,\n 47.78363463526376\n ],\n [\n -116.806640625,\n 47.14489748555398\n ],\n [\n -117.02636718749999,\n 46.118941506107056\n ],\n [\n -116.56494140625001,\n 45.69083283645816\n ],\n [\n -116.91650390625,\n 44.91813929958515\n ],\n [\n -117.1142578125,\n 44.69989765840318\n ],\n [\n -117.18017578125,\n 44.29240108529005\n ],\n [\n -116.45507812500001,\n 43.77109381775651\n ],\n [\n -115.59814453125001,\n 43.18114705939968\n ],\n [\n -112.96142578125,\n 42.13082130188811\n ],\n [\n -111.20361328125,\n 42.032974332441405\n ],\n [\n -111.29150390625,\n 40.730608477796636\n ],\n [\n -112.08251953125,\n 40.111688665595956\n ],\n [\n -112.30224609374999,\n 39.01064750994083\n ],\n [\n -112.54394531249999,\n 38.13455657705411\n ],\n [\n -111.46728515624999,\n 37.996162679728116\n ],\n [\n -110.23681640625,\n 38.993572058209466\n ],\n [\n -110.06103515625,\n 40.43022363450862\n ],\n [\n -108.984375,\n 40.38002840251183\n ],\n [\n -108.78662109375,\n 38.94232097947902\n ],\n [\n -108.19335937499999,\n 37.33522435930639\n ],\n [\n -106.72119140625,\n 37.00255267215955\n ],\n [\n -106.67724609375,\n 36.54494944148322\n ],\n [\n -106.94091796875,\n 35.746512259918504\n ],\n [\n -106.61132812499999,\n 34.831841149828655\n ],\n [\n -106.3916015625,\n 33.669496972795535\n ],\n [\n -106.6552734375,\n 33.119150226768866\n ],\n [\n -106.5673828125,\n 31.50362930577303\n ],\n [\n -105.16113281249999,\n 30.675715404167743\n ],\n [\n -104.6337890625,\n 29.458731185355344\n ],\n [\n -103.0517578125,\n 28.806173508854776\n ],\n [\n -102.26074218749999,\n 29.649868677972304\n ],\n [\n -101.42578124999999,\n 29.649868677972304\n ],\n [\n -99.36035156249999,\n 32.65787573695528\n ],\n [\n -97.6025390625,\n 33.7243396617476\n ],\n [\n -97.2509765625,\n 34.84987503195418\n ],\n [\n -96.8994140625,\n 37.23032838760387\n ],\n [\n -96.591796875,\n 38.89103282648846\n ],\n [\n -96.94335937499999,\n 41.27780646738183\n ],\n [\n -96.8994140625,\n 42.4234565179383\n ],\n [\n -98.5693359375,\n 43.54854811091286\n ],\n [\n -98.7890625,\n 44.37098696297173\n ],\n [\n -98.87695312499999,\n 47.39834920035926\n ],\n [\n -100.37109375,\n 47.81315451752768\n ],\n [\n -101.9970703125,\n 49.03786794532644\n ],\n [\n -119.5751953125,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237723,"text":"70237723 - 2022 - Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock","interactions":[],"lastModifiedDate":"2022-10-21T13:51:58.019829","indexId":"70237723","displayToPublicDate":"2022-10-12T08:48:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12789,"text":"Frontiers in Antibiotics","active":true,"publicationSubtype":{"id":10}},"title":"Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock","docAbstract":"The increasing global expansion of antimicrobial resistant infections warrants the development of effective antibiotic alternative therapies, particularly for use in livestock production, an agricultural sector that is perceived to disproportionately contribute to the antimicrobial resistance (AMR) crisis by consuming nearly two-thirds of the global antibiotic supply. Probiotics and probiotic derived compounds are promising alternative therapies, and their successful use in disease prevention, treatment, and animal performance commands attention. However, insufficient or outdated probiotic screening techniques may unintentionally contribute to this crisis, and few longitudinal studies have been conducted to determine what role probiotics play in AMR dissemination in animal hosts and the surrounding environment. In this review, we briefly summarize the current literature regarding the efficacy, feasibility, and limitations of probiotics, including an evaluation of their impact on the animal microbiome and resistome and their potential to influence AMR in the environment. Probiotic application for livestock is often touted as an ideal alternative therapy that might reduce the need for antibiotic use in agriculture and the negative downstream impacts. However, as detailed in this review, limited research has been conducted linking probiotic usage with reductions in AMR in agricultural or natural environments. Additionally, we discuss the methods, including limitations, of current probiotic screening techniques across the globe, highlighting approaches aimed at reducing antibiotic usage and ensuring safe and effective probiotic mediated health outcomes. Based on this information, we propose economic and logistical considerations for bringing probiotic therapies to market including regulatory roadblocks, future innovations, and the significant gaps in knowledge requiring additional research to ensure probiotics are suitable long-term options for livestock producers as an antibiotic alternative therapy.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frabi.2022.1003912","usgsCitation":"Leistikow, K.R., Beattie, R.E., and Hristova, K.R., 2022, Probiotics beyond the farm: Benefits, costs, and considerations of using antibiotic alternatives in livestock: Frontiers in Antibiotics, v. 1, 1003912, 18 p., https://doi.org/10.3389/frabi.2022.1003912.","productDescription":"1003912, 18 p.","ipdsId":"IP-143496","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frabi.2022.1003912","text":"Publisher Index Page"},{"id":408602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Leistikow, Kyle R.","contributorId":298311,"corporation":false,"usgs":false,"family":"Leistikow","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":855363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beattie, Rachelle Elaine 0000-0002-9648-4948","orcid":"https://orcid.org/0000-0002-9648-4948","contributorId":298312,"corporation":false,"usgs":true,"family":"Beattie","given":"Rachelle","email":"","middleInitial":"Elaine","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hristova, Krassimira R.","contributorId":298313,"corporation":false,"usgs":false,"family":"Hristova","given":"Krassimira","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":855365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237391,"text":"70237391 - 2022 - An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","interactions":[],"lastModifiedDate":"2022-10-12T13:40:56.735739","indexId":"70237391","displayToPublicDate":"2022-10-12T08:20:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","title":"An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America","docAbstract":"We used two datasets to investigate the reliability of plumage for sexing adult Ruddy Turnstones Arenaria interpres of the morinella subspecies during May and early June in Delaware Bay, on the Mid-Atlantic Coast of the United States (39.1202°N, 75.2479°W). We first examined 23 years of data on the capture and recapture of 1,818 individual Ruddy Turnstones to assess the consistency of observers with varying levels of expertise in assigning sex using plumage criteria. Among birds recaptured once, the sex recorded for about 10% differed between captures. This increased to about 16% among birds recaptured more than once. Significantly more birds sexed as females early in the season (during 1–12 May) were later sexed as males than vice versa. This suggests that early-season captures may include birds still in non- (or partial) breeding plumage, which can be confused with female breeding plumage. Second, we compared plumage-based and genetic assessments of sex for 66 Ruddy Turnstones captured in Delaware Bay on 29 May 2016 and 19 May 2017; these individuals were sexed in the hand by an expert on shorebird plumages. Plumage-based and molecular assessments differed in only one case. This suggests that fewer birds will be wrongly sexed on plumage if more care is taken and better instruction is given to observers (including how to distinguish non- breeding plumage from female breeding plumage). We suggest simple procedures to reduce field-sexing errors for Ruddy Turnstones based on plumage.
","language":"English","publisher":"International Wader Study Group","doi":"10.18194/ws.00274","usgsCitation":"Fullagar, P.J., Chesser, R., Sitters, H.P., Davey, C.C., Niles, L., Drovetski, S.V., and Cortes-Rodriguez, M., 2022, An evaluation of the reliability of plumage characters for sexing adult Ruddy Turnstones Arenaria interpres morinella during northward passage in eastern North America: Wader Study, v. 129, no. 2, p. 138-147, https://doi.org/10.18194/ws.00274.","productDescription":"10 p.","startPage":"138","endPage":"147","ipdsId":"IP-133440","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":408208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.09979248046875,\n 38.76479194327964\n ],\n [\n -74.94598388671875,\n 38.94232097947902\n ],\n [\n -74.87457275390625,\n 39.095962936305476\n ],\n [\n -74.85260009765625,\n 39.17478791493289\n ],\n [\n -74.8828125,\n 39.22587043822116\n ],\n [\n -75.13824462890625,\n 39.24288969082635\n ],\n [\n -75.23162841796875,\n 39.31517545076218\n ],\n [\n -75.27557373046875,\n 39.32579941789298\n ],\n [\n -75.311279296875,\n 39.36190883564925\n ],\n [\n -75.3662109375,\n 39.37677199661635\n ],\n [\n -75.44586181640625,\n 39.45316112807394\n ],\n [\n -75.51177978515625,\n 39.48284540453334\n ],\n [\n -75.498046875,\n 39.54217596171196\n ],\n [\n -75.47332763671875,\n 39.58240712203527\n ],\n [\n -75.531005859375,\n 39.63319206567459\n ],\n [\n -75.487060546875,\n 39.688167045890395\n ],\n [\n -75.42938232421875,\n 39.75999140525313\n ],\n [\n -75.311279296875,\n 39.82119422647453\n ],\n [\n -75.22064208984375,\n 39.83595916247957\n ],\n [\n -75.08880615234375,\n 39.86969567045658\n ],\n [\n -75.1519775390625,\n 39.91394967016644\n ],\n [\n -75.3662109375,\n 39.86969567045658\n ],\n [\n -75.53924560546875,\n 39.74521015328692\n ],\n [\n -75.5474853515625,\n 39.71141252523694\n ],\n [\n -75.63812255859375,\n 39.614152077002664\n ],\n [\n -75.6024169921875,\n 39.53370327008705\n ],\n [\n -75.62164306640625,\n 39.45528185347343\n ],\n [\n -75.56121826171875,\n 39.40861097325807\n ],\n [\n -75.5145263671875,\n 39.33854604847979\n ],\n [\n -75.42388916015625,\n 39.20246222588238\n ],\n [\n -75.44036865234375,\n 39.16839998800286\n ],\n [\n -75.42938232421875,\n 39.07677595221322\n ],\n [\n -75.38543701171875,\n 39.036252959636606\n ],\n [\n -75.34149169921875,\n 38.974357249228206\n ],\n [\n -75.34423828125,\n 38.9380483825641\n ],\n [\n -75.2947998046875,\n 38.884619201291905\n ],\n [\n -75.29754638671875,\n 38.86323626888358\n ],\n [\n -75.17669677734375,\n 38.77978137804918\n ],\n [\n -75.09979248046875,\n 38.76479194327964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Fullagar, Peter J.","contributorId":297536,"corporation":false,"usgs":false,"family":"Fullagar","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chesser, R. 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Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sitters, Humphrey P.","contributorId":297537,"corporation":false,"usgs":false,"family":"Sitters","given":"Humphrey","email":"","middleInitial":"P.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davey, Christopher C.","contributorId":297538,"corporation":false,"usgs":false,"family":"Davey","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":64424,"text":"private individual","active":true,"usgs":false}],"preferred":false,"id":854378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niles, Lawrence J.","contributorId":297539,"corporation":false,"usgs":false,"family":"Niles","given":"Lawrence J.","affiliations":[{"id":64426,"text":"Wildlife Restoration Partnerships","active":true,"usgs":false}],"preferred":false,"id":854379,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854380,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cortes-Rodriguez, M. 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Nandadevi","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":854381,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70269055,"text":"70269055 - 2022 - Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2025-07-15T15:43:20.638903","indexId":"70269055","displayToPublicDate":"2022-10-12T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","docAbstract":"It has been nearly a decade since updates to seismic and fault sources in the central and eastern United States (CEUS) were last assessed for the 2012 Central and Eastern United States Seismic Source Characterization for nuclear facilities (CEUS-SSCn) and 2014 United States Geological Survey National Seismic Hazard Model (NSHM) for the conterminous U.S. In advance of the 2023 NSHM update, we created 3 related geospatial databases to summarize and characterize new fault source information for the CEUS. These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220162","usgsCitation":"Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3100-3120, https://doi.org/10.1785/0220220162.","productDescription":"21 p.","startPage":"3100","endPage":"3120","ipdsId":"IP-138939","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.33762311995545,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 51.85857884546667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237374,"text":"70237374 - 2022 - Advances in coral immunity ‘omics in response to disease outbreaks","interactions":[],"lastModifiedDate":"2022-10-12T13:56:05.210143","indexId":"70237374","displayToPublicDate":"2022-10-11T14:09:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Advances in coral immunity ‘omics in response to disease outbreaks","docAbstract":"Coral disease has progressively become one of the most pressing issues affecting coral reef survival. In the last 50 years, several reefs throughout the Caribbean have been severely impacted by increased frequency and intensity of disease outbreaks leading to coral death. A recent example of this is stony coral tissue loss disease which has quickly spread throughout the Caribbean, devastating coral reef ecosystems. Emerging from these disease outbreaks has been a coordinated research response that often integrates ‘omics techniques to better understand the coral immune system. ‘Omics techniques encompass a wide range of technologies used to identify large scale gene, DNA, metabolite, and protein expression. In this review, we discuss what is known about coral immunity and coral disease from an ‘omics perspective. We reflect on the development of biomarkers and discuss ways in which coral disease experiments to test immunity can be improved. Lastly, we consider how existing data can be better leveraged to combat future coral disease outbreaks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.952199","usgsCitation":"Traylor-Knowles, N., Baker, A.C., Beavers, K.M., Garg, N., Guyon, J.R., Hawthorn, A.C., MacKnight, N.J., Medina, M., Mydlarz, L.D., Peters, E.C., Stewart, J.M., Studivan, M.S., and Voss, J.D., 2022, Advances in coral immunity ‘omics in response to disease outbreaks: Frontiers in Marine Science, v. 9, 952199, 26 p., https://doi.org/10.3389/fmars.2022.952199.","productDescription":"952199, 26 p.","ipdsId":"IP-144516","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":446147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.952199","text":"Publisher Index Page"},{"id":408182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Traylor-Knowles, Nikki","contributorId":297502,"corporation":false,"usgs":false,"family":"Traylor-Knowles","given":"Nikki","email":"","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":854320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Andrew C.","contributorId":297503,"corporation":false,"usgs":false,"family":"Baker","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":854321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beavers, Kelsey M.","contributorId":297504,"corporation":false,"usgs":false,"family":"Beavers","given":"Kelsey","email":"","middleInitial":"M.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garg, Neha","contributorId":297505,"corporation":false,"usgs":false,"family":"Garg","given":"Neha","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":854323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guyon, Jeffrey R.","contributorId":297506,"corporation":false,"usgs":false,"family":"Guyon","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":854324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hawthorn, Aine C. 0000-0002-8029-1383","orcid":"https://orcid.org/0000-0002-8029-1383","contributorId":292709,"corporation":false,"usgs":true,"family":"Hawthorn","given":"Aine","email":"","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":854325,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"MacKnight, Nicholas J.","contributorId":297507,"corporation":false,"usgs":false,"family":"MacKnight","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854326,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Medina, Mónica","contributorId":297508,"corporation":false,"usgs":false,"family":"Medina","given":"Mónica","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854327,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mydlarz, Laura D.","contributorId":167562,"corporation":false,"usgs":false,"family":"Mydlarz","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":24751,"text":"University of Texas Arlington","active":true,"usgs":false}],"preferred":false,"id":854328,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peters, Esther C.","contributorId":209975,"corporation":false,"usgs":false,"family":"Peters","given":"Esther","email":"","middleInitial":"C.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":854329,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stewart, Julia Marie","contributorId":297509,"corporation":false,"usgs":false,"family":"Stewart","given":"Julia","email":"","middleInitial":"Marie","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854330,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Studivan, Michael S.","contributorId":297510,"corporation":false,"usgs":false,"family":"Studivan","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":64418,"text":"University of Miami, NOAA","active":true,"usgs":false}],"preferred":false,"id":854331,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Voss, Joshua D.","contributorId":150551,"corporation":false,"usgs":false,"family":"Voss","given":"Joshua","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854332,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70237376,"text":"70237376 - 2022 - Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","interactions":[],"lastModifiedDate":"2022-10-11T19:08:25.114099","indexId":"70237376","displayToPublicDate":"2022-10-11T14:04:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","title":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","docAbstract":"Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2022.102576","usgsCitation":"Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E., Siler, D.L., Karra, S., and Middleton, R.S., 2022, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering: Geothermics, v. 106, 102576, 15 p., https://doi.org/10.1016/j.geothermics.2022.102576.","productDescription":"102576, 15 p.","ipdsId":"IP-132590","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1890937","text":"Publisher Index Page"},{"id":408181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Colorado Plateau, Gila Hot Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 32.008075959291055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":854339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karra, Satish","contributorId":297512,"corporation":false,"usgs":false,"family":"Karra","given":"Satish","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middleton, Richard S.","contributorId":297513,"corporation":false,"usgs":false,"family":"Middleton","given":"Richard","email":"","middleInitial":"S.","affiliations":[{"id":64420,"text":"Carbon Solutions LLC","active":true,"usgs":false}],"preferred":false,"id":854344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237371,"text":"70237371 - 2022 - Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials","interactions":[],"lastModifiedDate":"2022-10-11T19:00:04.483288","indexId":"70237371","displayToPublicDate":"2022-10-11T13:57:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials","docAbstract":"Development of wind energy facilities results in interactions between wildlife and wind turbines. Raptors, including bald and golden eagles, are among the species known to incur mortality from these interactions. Several alerting technologies have been proposed to mitigate this mortality by increasing eagle avoidance of wind energy facilities. However, there has been little attempt to match signals used as alerting stimuli with the sensory capabilities of target species like eagles. One potential approach to tuning signals is to use sensory physiology to determine what stimuli the target eagle species are sensitive to even in the presence of background noise, thereby allowing the development of a maximally stimulating signal. To this end, we measured auditory evoked potentials of bald and golden eagles to determine what types of sounds eagles can process well, especially in noisy conditions. We found that golden eagles are significantly worse than bald eagles at processing rapid frequency changes in sounds, but also that noise effects on hearing in both species are minimal in response to rapidly changing sounds. Our findings therefore suggest that sounds of intermediate complexity may be ideal both for targeting bald and golden eagle hearing and for ensuring high stimulation in noisy field conditions. These results suggest that the sensory physiology of target species is likely an important consideration when selecting auditory alerting sounds and may provide important insight into what sounds have a reasonable probability of success in field applications under variable conditions and background noise.","language":"English","publisher":"Oxford University Press","doi":"10.1093/conphys/coac059","usgsCitation":"Goller, B., Baumhardt, P., Dominguez-Villegas, E., Katzner, T., Fernandez-Juricic, E., and Lucas, J.R., 2022, Selecting auditory alerting stimuli for eagles on the basis of auditory evoked potentials: Conservation Physiology, v. 10, no. 1, coac059, 18 p., https://doi.org/10.1093/conphys/coac059.","productDescription":"coac059, 18 p.","ipdsId":"IP-139387","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446153,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coac059","text":"Publisher Index Page"},{"id":408179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Goller, Benjamin","contributorId":297485,"corporation":false,"usgs":false,"family":"Goller","given":"Benjamin","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baumhardt, Patrice","contributorId":297486,"corporation":false,"usgs":false,"family":"Baumhardt","given":"Patrice","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dominguez-Villegas, Ernesto","contributorId":223077,"corporation":false,"usgs":false,"family":"Dominguez-Villegas","given":"Ernesto","email":"","affiliations":[{"id":37079,"text":"Wildlife Center of Virginia","active":true,"usgs":false}],"preferred":false,"id":854297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":854298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernandez-Juricic, Esteban","contributorId":224607,"corporation":false,"usgs":false,"family":"Fernandez-Juricic","given":"Esteban","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucas, Jeffrey R.","contributorId":297487,"corporation":false,"usgs":false,"family":"Lucas","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":854300,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237354,"text":"70237354 - 2022 - Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","interactions":[],"lastModifiedDate":"2022-10-12T15:04:06.279175","indexId":"70237354","displayToPublicDate":"2022-10-11T12:34:41","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","docAbstract":"This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. The temperature of water in a lake is a fundamental driver of lake biogeochemical processes, and it controls the growth, survival, and reproduction of fishes in the lake.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-17","usgsCitation":"Daw, A., Thomas, R.Q., Carey, C.C., Read, J., Appling, A.P., and Karpatne, A., 2022, Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling, chap. 17 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 399-416, https://doi.org/10.1201/9781003143376-17.","productDescription":"18 p.","startPage":"399","endPage":"416","ipdsId":"IP-131612","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination 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It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. Accurate water temperatures are critical to understanding contemporary change, and for predicting future thermal habitat of economically valuable fish.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-15","usgsCitation":"Daw, A., Karpatne, A., Watkins, W., Read, J., and Kumar, V., 2022, Physics-guided neural networks (PGNN): An application in lake temperature modeling, chap. 15 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 353-372, https://doi.org/10.1201/9781003143376-15.","productDescription":"20 p.","startPage":"353","endPage":"372","ipdsId":"IP-132785","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":446159,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1201/9781003143376-15","text":"External Repository"},{"id":408170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daw, Arka","contributorId":297446,"corporation":false,"usgs":false,"family":"Daw","given":"Arka","email":"","affiliations":[{"id":64394,"text":"Department of Computer Science, Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":854191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, William 0000-0002-7544-0700 wwatkins@usgs.gov","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":178146,"corporation":false,"usgs":true,"family":"Watkins","given":"William","email":"wwatkins@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854195,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237336,"text":"70237336 - 2022 - Physics-guided recurrent neural networks for predicting lake water temperature","interactions":[],"lastModifiedDate":"2022-10-12T15:25:40.706808","indexId":"70237336","displayToPublicDate":"2022-10-11T12:12:46","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"16","title":"Physics-guided recurrent neural networks for predicting lake water temperature","docAbstract":"This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-16","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2022, Physics-guided recurrent neural networks for predicting lake water temperature, chap. 16 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 373-398, https://doi.org/10.1201/9781003143376-16.","productDescription":"26 p.","startPage":"373","endPage":"398","ipdsId":"IP-132700","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854186,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237347,"text":"70237347 - 2022 - Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene","interactions":[],"lastModifiedDate":"2022-10-11T17:10:30.606714","indexId":"70237347","displayToPublicDate":"2022-10-11T11:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2998,"text":"Palaeontology","active":true,"publicationSubtype":{"id":10}},"title":"Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene","docAbstract":"We examine three distinctive biostratigraphic signatures associated with: hunting and gathering, landscape domestication, and globalisation. All three signatures have significant fossil records of regional importance that can be correlated inter-regionally and help describe the developing pattern of human expansion and appropriation of resources. While none have individual first or last appearances that provide a globally isochronous marker, all three signatures overlap stratigraphically, in that they are part of a continuum of change, with complex regional patterns. Here we show that during the later stages of globalisation, late 19th to 20th century records of species translocations can be used to build an interconnected web of palaeontological correlation with decadal or sub-decadal precision that dovetails with other stratigraphic markers for the Anthropocene. This palaeontological web is also a proxy for accelerating species extinction and of a state shift in the biosphere in the 20th century.","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/pala.12618","usgsCitation":"Williams, M., Leinfelder, R., Barnosky, A.D., Head, M., McCarthy, F.M., Cearreta. Alejandro, Himson, S.J., Holmes, R., Waters, C.N., Zalasiewicz, J., Turner, S., McGann, M., Hadly, E.A., Stegner, M.A., Pilkington, P.M., Kaiser, J., Berrio, J.C., Wilkinson, I.P., Zinke, J., and DeLong, K., 2022, Planetary-scale change to the biosphere signalled by global species translocations can be used to identify the Anthropocene: Palaeontology, v. 65, no. 4, e12618, 25 p., https://doi.org/10.1111/pala.12618.","productDescription":"e12618, 25 p.","ipdsId":"IP-135084","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/geoanth_pubs/499","text":"Publisher Index Page"},{"id":408168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"65","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Mark","contributorId":214696,"corporation":false,"usgs":false,"family":"Williams","given":"Mark","affiliations":[],"preferred":false,"id":854211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leinfelder, Reinhold","contributorId":297457,"corporation":false,"usgs":false,"family":"Leinfelder","given":"Reinhold","email":"","affiliations":[{"id":64399,"text":"Freie University, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":854212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnosky, Anthony D.","contributorId":197553,"corporation":false,"usgs":false,"family":"Barnosky","given":"Anthony","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854213,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Head, Martin J","contributorId":297458,"corporation":false,"usgs":false,"family":"Head","given":"Martin J","affiliations":[{"id":64401,"text":"Brock University, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854214,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarthy, Francine M G","contributorId":297459,"corporation":false,"usgs":false,"family":"McCarthy","given":"Francine","email":"","middleInitial":"M G","affiliations":[{"id":64401,"text":"Brock University, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854215,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cearreta. 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Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared error was 1.61°C. This dataset increases the number of lakes with daily temperature predictions when compared to existing datasets, as well as substantially improves predictive accuracy compared to a prior empirical model and a debiased process-based approach (2.01°C and 1.79°C median error, respectively).
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