{"pageNumber":"347","pageRowStart":"8650","pageSize":"25","recordCount":41079,"records":[{"id":70202730,"text":"70202730 - 2019 - Stream metabolism increases with drainage area and peaks asynchronously across a stream network","interactions":[],"lastModifiedDate":"2019-03-26T08:16:07","indexId":"70202730","displayToPublicDate":"2019-03-21T16:36:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Stream metabolism increases with drainage area and peaks asynchronously across a stream network","docAbstract":"<p><span>Quantifying the spatial and temporal dynamics of stream metabolism across stream networks is key to understanding carbon cycling and stream food web ecology. To better understand intra-annual temporal patterns of gross primary production (GPP) and ecosystem respiration (ER) and their variability across space, we continuously measured dissolved oxygen and modeled stream metabolism for an entire year at ten sites across a temperate river network in Washington State, USA. We expected GPP and ER to increase with stream size and peak during summer and autumn months due to warmer temperatures and higher light availability. We found that GPP and ER increased with drainage area and that only four sites adhered to our expectations of summer peaks in GPP and autumn peaks in ER while the rest either peaked in winter, spring or remained relatively constant. Our results suggest the spatial arrangement and temporal patterns of discharge, temperature, light and nutrients within watersheds may result in asynchronies in GPP and ER, despite similar regional climatic conditions. These findings shed light on how temporal dynamics of stream metabolism can shift across a river network, which likely influence the dynamics of carbon cycling and stream food webs at larger scales.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00027-018-0606-z","usgsCitation":"Mejia, F.H., Fremier, A.K., Benjamin, J.R., Bellmore, J., Grimm, A.Z., Watson, G., and Newsom, M., 2019, Stream metabolism increases with drainage area and peaks asynchronously across a stream network: Aquatic Sciences, v. 81, p. 1-17, https://doi.org/10.1007/s00027-018-0606-z.","productDescription":"Article 9, 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-086489","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Methow River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.38818359375,\n              48.11476663187632\n            ],\n            [\n              -119.79080200195311,\n              48.11476663187632\n            ],\n            [\n              -119.79080200195311,\n              48.539341045937974\n            ],\n            [\n              -120.38818359375,\n              48.539341045937974\n            ],\n            [\n              -120.38818359375,\n              48.11476663187632\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"81","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":759692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fremier, Alexander K.","contributorId":214346,"corporation":false,"usgs":false,"family":"Fremier","given":"Alexander","email":"","middleInitial":"K.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":759693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":759694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bellmore, J. Ryan jbellmore@usgs.gov","contributorId":4527,"corporation":false,"usgs":true,"family":"Bellmore","given":"J. Ryan","email":"jbellmore@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":759695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grimm, Adrianne Z.","contributorId":214347,"corporation":false,"usgs":false,"family":"Grimm","given":"Adrianne","email":"","middleInitial":"Z.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":759696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watson, Grace A.","contributorId":214348,"corporation":false,"usgs":false,"family":"Watson","given":"Grace A.","affiliations":[{"id":39012,"text":"Methow Salmon Recovery Foundation","active":true,"usgs":false}],"preferred":false,"id":759697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Newsom, Michael","contributorId":178562,"corporation":false,"usgs":false,"family":"Newsom","given":"Michael","affiliations":[],"preferred":false,"id":759698,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70202725,"text":"70202725 - 2019 - Evidence for non-steady-state carbon emissions from snow-scoured alpine tundra","interactions":[],"lastModifiedDate":"2019-03-21T14:47:38","indexId":"70202725","displayToPublicDate":"2019-03-21T14:11:10","publicationYear":"2019","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":"Evidence for non-steady-state carbon emissions from snow-scoured alpine tundra","docAbstract":"<p><span>High-latitude warming is capable of accelerating permafrost degradation and the decomposition of previously frozen carbon. The existence of an analogous high-altitude feedback, however, has yet to be directly evaluated. We address this knowledge gap by coupling a radiocarbon-based model to 7 years (2008–2014) of continuous eddy covariance data from a snow-scoured alpine tundra meadow in Colorado, USA, where solifluction lobes are associated with discontinuous permafrost. On average, the ecosystem was a net annual source of 232 ± 54 g C m</span><sup>−2</sup><span>&nbsp;(mean ± 1 standard deviation) to the atmosphere, and respiration of relatively radiocarbon-depleted (i.e., older) substrate contributes to carbon emissions during the winter. Given that alpine soils with permafrost occupy 3.6 × 10</span><sup>6</sup><span>&nbsp;km</span><sup>2</sup><span>&nbsp;land area and are estimated to contain 66.3 Pg of soil organic carbon (4.5% of the global pool), this scenario has global implications for the mountain carbon balance and corresponding resource allocation to lower elevations.</span></p>","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41467-019-09149-2","usgsCitation":"Knowles, J.F., Blanken, P.D., Lawrence, C., and Williams, M.W., 2019, Evidence for non-steady-state carbon emissions from snow-scoured alpine tundra: Nature Communications, v. 10, Article number: 1306; 9 p., https://doi.org/10.1038/s41467-019-09149-2.","productDescription":"Article number: 1306; 9 p.","ipdsId":"IP-101783","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":460433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-09149-2","text":"Publisher Index Page"},{"id":362250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Niwot Ridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.62410354614256,\n              40.052322006146916\n            ],\n            [\n              -105.56985855102538,\n              40.052322006146916\n            ],\n            [\n              -105.56985855102538,\n              40.07045271464657\n            ],\n            [\n              -105.62410354614256,\n              40.07045271464657\n            ],\n            [\n              -105.62410354614256,\n              40.052322006146916\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Knowles, John F.","contributorId":203853,"corporation":false,"usgs":false,"family":"Knowles","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":759656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blanken, Peter D.","contributorId":189305,"corporation":false,"usgs":false,"family":"Blanken","given":"Peter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":759657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Corey 0000-0002-2179-2436","orcid":"https://orcid.org/0000-0002-2179-2436","contributorId":214331,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":759655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Mark W.","contributorId":214082,"corporation":false,"usgs":false,"family":"Williams","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":38977,"text":"University of Colorado at Boulder","active":true,"usgs":false}],"preferred":false,"id":759658,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204977,"text":"70204977 - 2019 - A cautionary tale of topography and tilt from Kilauea Caldera","interactions":[],"lastModifiedDate":"2019-08-28T09:18:23","indexId":"70204977","displayToPublicDate":"2019-03-21T13:52:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A cautionary tale of topography and tilt from Kilauea Caldera","docAbstract":"We conduct finite element analysis to investigate the effect of sharp topography on surface ground deformation caused by pressure changes in a magma reservoir. Tilt data express the horizontal gradient of vertical displacement and therefore can emphasize small variations in deformation that go unnoticed using other methods. We find that the vertical displacement profile at a surface with a cliff can be thought of as the superposition of the deformation from shallow and deeper sources. This combination can create a small peak in vertical displacement that acts as a pseudo‐source, creating a reversal of the deformation gradient and therefore anomalous tilt magnitude and a rotation of up to 180°. We apply these models to Kīlauea Caldera and find that surface geometry creates a tilt rotation of ∼10°, partially explaining anomalous tilt that has been observed. Our analysis highlights the importance of considering topography when assessing tilt measurements at active volcanoes.","language":"English","publisher":"Wiley","doi":"10.1029/2018GL081757","usgsCitation":"Johnson, J.A., Poland, M.P., Anderson, K.R., and Biggs, J., 2019, A cautionary tale of topography and tilt from Kilauea Caldera: Geophysical Research Letters, v. 46, no. 8, p. 4221-4229, https://doi.org/10.1029/2018GL081757.","productDescription":"9 p.","startPage":"4221","endPage":"4229","ipdsId":"IP-104361","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467789,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2018gl081757","text":"External Repository"},{"id":366969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Caldera","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.3360366821289,\n              19.34969555223576\n            ],\n            [\n              -155.20179748535153,\n              19.34969555223576\n            ],\n            [\n              -155.20179748535153,\n              19.449111649832837\n            ],\n            [\n              -155.3360366821289,\n              19.449111649832837\n            ],\n            [\n              -155.3360366821289,\n              19.34969555223576\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"46","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Jessica A.","contributorId":149712,"corporation":false,"usgs":false,"family":"Johnson","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":769381,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":769380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":769382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biggs, Juliet","contributorId":206389,"corporation":false,"usgs":false,"family":"Biggs","given":"Juliet","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":769383,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70202793,"text":"70202793 - 2019 - Relative abundance and molecular evolution of Lake Sinai Virus (Sinaivirus) clades","interactions":[],"lastModifiedDate":"2019-03-28T10:36:45","indexId":"70202793","displayToPublicDate":"2019-03-21T11:08:33","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Relative abundance and molecular evolution of Lake Sinai Virus (Sinaivirus) clades","docAbstract":"<div class=\"abstract\"><p><span>Lake Sinai Viruses (Sinaivirus) are commonly detected in honey bees (</span><i>Apis mellifera</i><span>) but no disease phenotypes or fitness consequences have yet been demonstrated. This viral group is genetically diverse, lacks obvious geographic structure, and multiple lineages can co-infect individual bees. While phylogenetic analyses have been performed, the molecular evolution of LSV has not been studied extensively. Here, I use LSV isolates from GenBank as well as contigs assembled from honey bee Sequence Read Archive (SRA) accessions to better understand the evolutionary history of these viruses. For each ORF, substitution rate variation, codon usage, and tests of positive selection were evaluated. Outlier regions of high or low diversity were sought with sliding window analysis and the role of recombination in creating LSV diversity was explored. Phylogenetic analysis consistently identified two large clusters of sequences that correspond to the current LSV1 and LSV2 nomenclature, however lineages sister to LSV1 were the most frequently detected in honey bee SRA accessions. Different expression levels among ORFs suggested the occurrence of subgenomic transcripts. ORF1 and RNA-dependent RNA polymerase had higher evolutionary rates than the capsid and ORF4. A hypervariable region of the ORF1 protein-coding sequence was identified that had reduced selective constraint, but a site-based model of positive selection was not significantly more likely than a neutral model for any ORF. The only significant recombination signals detected between LSV1 and LSV2 initiated within this hypervariable region, but assumptions of the test (single-frame coding and independence of substitution rate by site) were violated. LSV codon usage differed strikingly from that of honey bees and other common honey-bee viruses, suggesting LSV is not strongly co-evolved with that host. LSV codon usage was significantly correlated with that of&nbsp;</span><i>Varroa destructor</i><span>, however, despite the relatively weak codon bias exhibited by the latter. While codon usage between the LSV1 and LSV2 clusters was similar for three ORFs, ORF4 codon usage was uncorrelated between these clades, implying rapid divergence of codon use for this ORF only. Phylogenetic placement and relative abundance of LSV isolates reconstructed from SRA accessions suggest that detection biases may be over-representing LSV1 and LSV2 in public databases relative to their sister lineages.</span></p></div>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.6305","usgsCitation":"Cornman, R.S., 2019, Relative abundance and molecular evolution of Lake Sinai Virus (Sinaivirus) clades: PeerJ, v. 7, e6305; 19 p., https://doi.org/10.7717/peerj.6305.","productDescription":"e6305; 19 p.","ipdsId":"IP-102658","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467792,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.6305","text":"Publisher Index Page"},{"id":437533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F4YR6P","text":"USGS data release","linkHelpText":"Metagenomic detection and reconstruction of Lake Sinai Virus from honey bee sequence data"},{"id":362332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":760027,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227919,"text":"70227919 - 2019 - Validating the performance of occupancy models for estimating habitat use and predicting the distribution of highly-mobile species: A case study using the American black bear","interactions":[],"lastModifiedDate":"2022-02-02T16:21:43.226849","indexId":"70227919","displayToPublicDate":"2019-03-21T10:04:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Validating the performance of occupancy models for estimating habitat use and predicting the distribution of highly-mobile species: A case study using the American black bear","docAbstract":"<p><span>Occupancy models have become a valuable tool for estimating wildlife-habitat relationships and for predicting species distributions. Highly-mobile species often violate the assumption that sampling units are geographically closed shifting the probability of occupancy to be interpreted as the probability of use. We used occupancy models, in conjunction with noninvasive sampling, to estimate habitat use and predict the distribution of a highly-mobile carnivore, the&nbsp;American black bear&nbsp;(</span><i>Ursus americanus</i><span>) in New Mexico, USA. The top model indicated that black bears use areas with higher primary productivity and fewer roads. The predictive performance of such models is rarely validated with independent data, so we validated our model predictions with 2-independent datasets. We first assessed the correlation between predicted and observed habitat use for 28 telemetry-collared bears in the Jemez Mountains. Predicted habitat use was positively correlated with observed use for all 3 years (2012: ρ = 0.81; 2013: ρ = 0.87; 2014: ρ = 0.90). We then predicted the probability of use within a cell where a bear mortality was documented using 2043 mortality locations from sport harvest, depredation, and vehicle collisions. The probability of habitat use at a mortality location was also positively correlated with observed use by the species (2012: ρ = 0.74; 2013: ρ = 0.89; 2014: ρ = 0.93). Our validation procedure supports the notion that occupancy models can be an effective tool for estimating habitat use and predicting the distribution of highly-mobile species when the assumption of geographic closure has been violated. Our findings may be of interest to studies that are estimating habitat use for highly-mobile species that are secretive or rare, difficult to capture, or expensive to monitor with other more intensive methods.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.03.010","usgsCitation":"Gould, M.J., Gould, W., Cain, J.W., and Roemer, G.W., 2019, Validating the performance of occupancy models for estimating habitat use and predicting the distribution of highly-mobile species: A case study using the American black bear: Biological Conservation, v. 234, p. 28-36, https://doi.org/10.1016/j.biocon.2019.03.010.","productDescription":"9 p.","startPage":"28","endPage":"36","ipdsId":"IP-099292","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Sangre de Cristo, Sacramento, and Jemez Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.479248046875,\n              32.37996146435729\n            ],\n            [\n              -104.83154296875,\n              32.37996146435729\n            ],\n            [\n              -104.83154296875,\n              36.712467243386264\n            ],\n            [\n              -107.479248046875,\n              36.712467243386264\n            ],\n            [\n              -107.479248046875,\n              32.37996146435729\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William R.","contributorId":244516,"corporation":false,"usgs":false,"family":"Gould","given":"William R.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":832574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roemer, Gary W.","contributorId":273109,"corporation":false,"usgs":false,"family":"Roemer","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":832576,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70216029,"text":"70216029 - 2019 - Quantitative coseismic and precipitation-induced landslide risk mapping for the country of Lebanon","interactions":[],"lastModifiedDate":"2020-11-05T13:01:03.14155","indexId":"70216029","displayToPublicDate":"2019-03-21T07:44:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Quantitative coseismic and precipitation-induced landslide risk mapping for the country of Lebanon","docAbstract":"<div id=\"texttabcontent\" class=\"tab-pane no-scroll show-content left-sided\" aria-labelledby=\"texttab\"><div class=\"NLM_sec NLM_sec_level_1 hlFld-Abstract\"><p>Quantitative landslide risk assessment is a key step in creating appropriate land use policies. The forced migration of those displaced by recent events in Syria has highlighted the need for studies to guide humanitarian aid and resettlement policies. In 2011, armed conflict in the region precipitated the largest refugee crisis in a generation. Over 1.5 million displaced Syrians now reside in Lebanon, rapidly changing the population distribution in geomorphically-active areas of the country. We use a multi-step process to quantitatively assess the landslide risk profile of Lebanon throughout the ongoing Syrian conflict. First, mode-specific geotechnical models are utilized to assess the individual hazard contributions of a suite of triggering scenarios and types of landslides appropriate to the varied terrain of Lebanon. Second, vulnerability estimates and population data from the United Nations High Commissioner for Refugees (UNHCR) are combined to produce scenario-specific risk. Finally, risk data is aggregated to create a comprehensive landslide risk profile for Syrian refugees in Lebanon and compared to that of the pre-conflict Lebanese population.</p></div></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geo-Congress 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eighth International Conference on Case Histories in Geotechnical Engineering","conferenceDate":"March 24–27, 2019","conferenceLocation":"Philadelphia, Pennsylvania","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/9780784482155.013","usgsCitation":"Pollock, W., Wartman, J., Abou-Jaoude, G., and Grant, A.R., 2019, Quantitative coseismic and precipitation-induced landslide risk mapping for the country of Lebanon, <i>in</i> Geo-Congress 2019, Philadelphia, Pennsylvania, March 24–27, 2019, https://doi.org/10.1061/9780784482155.013.","ipdsId":"IP-101086","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":380122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]},\"properties\":{\"name\":\"Lebanon\"}}]}","noUsgsAuthors":false,"publicationDate":"2019-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollock, William","contributorId":244344,"corporation":false,"usgs":false,"family":"Pollock","given":"William","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":803816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wartman, Joseph 0000-0001-7659-7198","orcid":"https://orcid.org/0000-0001-7659-7198","contributorId":241918,"corporation":false,"usgs":false,"family":"Wartman","given":"Joseph","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":803817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abou-Jaoude, Grace 0000-0003-1992-1611","orcid":"https://orcid.org/0000-0003-1992-1611","contributorId":244345,"corporation":false,"usgs":false,"family":"Abou-Jaoude","given":"Grace","email":"","affiliations":[{"id":48898,"text":"Lebanese American University","active":true,"usgs":false}],"preferred":false,"id":803818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":803819,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204353,"text":"70204353 - 2019 - Relatedness within and among Myotis septentrionalis colonies at a local scale","interactions":[],"lastModifiedDate":"2019-07-19T07:42:19","indexId":"70204353","displayToPublicDate":"2019-03-21T07:40:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1176,"text":"Canadian Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Relatedness within and among Myotis septentrionalis colonies at a local scale","docAbstract":"Abstract:  We assessed parentage within and among maternity colonies of the northern long-eared bat (Myotis septentrionalis Troessart 1897) in north-central Kentucky from 2011–2013 to better understand colony social structure, formation, and membership dynamics. We intensively sampled colonies in close and remote (> 10 km) spatial proximity both before and after targeted day-roost removal. Colonies were not necessarily comprised of closely related individuals, but natal philopatry was common. Adjacent colonies often contained maternally related individuals, indicating that some pups did disperse, albeit not far from their natal home range. Lack of apparent overlap among maternity colonies, along with no observed individual movements between colonies, suggests that colonies may be relatively closed once established in the maternity season.  Whereas some young on site had been sired by males collected on site that by chance had dispersed to the same summering grounds, most had not, as would be expected since the species mates in the fall swarms near hibernacula. The number of parentages that we inferred among colonies, however, suggests that outside the maternity season, social groups may be relatively flexible and open, with individuals moving among groups close to their natal area. Analysis of microsatellite DNA data showed a low FST (= 0.011) and best fit to a model of one multilocus genotypic cluster across the study area.  We observed high turnover in colony membership between years in all colonies, regardless of roost removal treatment. Our results suggest that female northern long-eared bats exhibit fidelity to a general geographic area rather than individual colonies between years, and indicate presence of a complex and dynamic social-genetic structure. Greater understanding of colony dynamics, including formation, dissolution, and dispersal patterns, may contribute to conservation and management of this threatened species.","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjz-2018-0229","usgsCitation":"Ford, W., Olivera-Hyde, M., Alexander Silvis, Eric M. 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Hallerman","contributorId":217271,"corporation":false,"usgs":false,"family":"Eric M. Hallerman","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":766486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britzke, Eric R.","contributorId":171586,"corporation":false,"usgs":false,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":766487,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70202709,"text":"70202709 - 2019 - Defining the limits of spectrally based bathymetric mapping on a large river","interactions":[],"lastModifiedDate":"2019-03-20T14:45:46","indexId":"70202709","displayToPublicDate":"2019-03-20T14:45:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Defining the limits of spectrally based bathymetric mapping on a large river","docAbstract":"<p><span>Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth,&nbsp;</span><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"semantics\"><span id=\"MathJax-Span-4\" class=\"msub\"><span id=\"MathJax-Span-5\" class=\"mi\">d</span><span id=\"MathJax-Span-6\" class=\"mrow\"><span id=\"MathJax-Span-7\" class=\"mi\">m</span><span id=\"MathJax-Span-8\" class=\"mi\">a</span><span id=\"MathJax-Span-9\" class=\"mi\">x</span></span></span></span></span></span></span></span><span>&nbsp;</span><span>, directly from an image and identifying optically deep areas that exceed&nbsp;</span><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math>\"><span id=\"MathJax-Span-10\" class=\"math\"><span><span id=\"MathJax-Span-11\" class=\"mrow\"><span id=\"MathJax-Span-12\" class=\"semantics\"><span id=\"MathJax-Span-13\" class=\"msub\"><span id=\"MathJax-Span-14\" class=\"mi\">d</span><span id=\"MathJax-Span-15\" class=\"mrow\"><span id=\"MathJax-Span-16\" class=\"mi\">m</span><span id=\"MathJax-Span-17\" class=\"mi\">a</span><span id=\"MathJax-Span-18\" class=\"mi\">x</span></span></span></span></span></span></span></span><span>&nbsp;</span><span>. Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of&nbsp;</span><span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math>\"><span id=\"MathJax-Span-19\" class=\"math\"><span><span id=\"MathJax-Span-20\" class=\"mrow\"><span id=\"MathJax-Span-21\" class=\"semantics\"><span id=\"MathJax-Span-22\" class=\"msub\"><span id=\"MathJax-Span-23\" class=\"mi\">d</span><span id=\"MathJax-Span-24\" class=\"mrow\"><span id=\"MathJax-Span-25\" class=\"mi\">m</span><span id=\"MathJax-Span-26\" class=\"mi\">a</span><span id=\"MathJax-Span-27\" class=\"mi\">x</span></span></span></span></span></span></span></span><span>&nbsp;</span><span>by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep (</span><span>&nbsp;</span><span id=\"MathJax-Element-4-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><mrow><mi>d</mi><mo>&amp;gt;</mo><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math>\"><span id=\"MathJax-Span-28\" class=\"math\"><span><span id=\"MathJax-Span-29\" class=\"mrow\"><span id=\"MathJax-Span-30\" class=\"semantics\"><span id=\"MathJax-Span-31\" class=\"mrow\"><span id=\"MathJax-Span-32\" class=\"mi\">d</span><span id=\"MathJax-Span-33\" class=\"mo\">&gt;</span><span id=\"MathJax-Span-34\" class=\"msub\"><span id=\"MathJax-Span-35\" class=\"mi\">d</span><span id=\"MathJax-Span-36\" class=\"mrow\"><span id=\"MathJax-Span-37\" class=\"mi\">m</span><span id=\"MathJax-Span-38\" class=\"mi\">a</span><span id=\"MathJax-Span-39\" class=\"mi\">x</span></span></span></span></span></span></span></span></span><span>&nbsp;</span><span>) classes and fit a logistic regression model to estimate the probability of optically deep water,&nbsp;</span><span id=\"MathJax-Element-5-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><mrow><mi>P</mi><mi>r</mi><mo>(</mo><mi>O</mi><mi>D</mi><mo>)</mo></mrow></semantics></math>\"><span id=\"MathJax-Span-40\" class=\"math\"><span><span id=\"MathJax-Span-41\" class=\"mrow\"><span id=\"MathJax-Span-42\" class=\"semantics\"><span id=\"MathJax-Span-43\" class=\"mrow\"><span id=\"MathJax-Span-44\" class=\"mi\">P</span><span id=\"MathJax-Span-45\" class=\"mi\">r</span><span id=\"MathJax-Span-46\" class=\"mo\">(</span><span id=\"MathJax-Span-47\" class=\"mi\">O</span><span id=\"MathJax-Span-48\" class=\"mi\">D</span><span id=\"MathJax-Span-49\" class=\"mo\">)</span></span></span></span></span></span></span><span>&nbsp;</span><span>. Applying a&nbsp;</span><span id=\"MathJax-Element-6-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><mrow><mi>P</mi><mi>r</mi><mo>(</mo><mi>O</mi><mi>D</mi><mo>)</mo></mrow></semantics></math>\"><span id=\"MathJax-Span-50\" class=\"math\"><span><span id=\"MathJax-Span-51\" class=\"mrow\"><span id=\"MathJax-Span-52\" class=\"semantics\"><span id=\"MathJax-Span-53\" class=\"mrow\"><span id=\"MathJax-Span-54\" class=\"mi\">P</span><span id=\"MathJax-Span-55\" class=\"mi\">r</span><span id=\"MathJax-Span-56\" class=\"mo\">(</span><span id=\"MathJax-Span-57\" class=\"mi\">O</span><span id=\"MathJax-Span-58\" class=\"mi\">D</span><span id=\"MathJax-Span-59\" class=\"mo\">)</span></span></span></span></span></span></span><span>&nbsp;</span><span>threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River,&nbsp;</span><span id=\"MathJax-Element-7-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math>\"><span id=\"MathJax-Span-60\" class=\"math\"><span><span id=\"MathJax-Span-61\" class=\"mrow\"><span id=\"MathJax-Span-62\" class=\"semantics\"><span id=\"MathJax-Span-63\" class=\"msub\"><span id=\"MathJax-Span-64\" class=\"mi\">d</span><span id=\"MathJax-Span-65\" class=\"mrow\"><span id=\"MathJax-Span-66\" class=\"mi\">m</span><span id=\"MathJax-Span-67\" class=\"mi\">a</span><span id=\"MathJax-Span-68\" class=\"mi\">x</span></span></span></span></span></span></span></span><span>&nbsp;</span><span>reached as high as 9.5 m at one site, with accurate depth retrieval (</span><span>&nbsp;</span><span id=\"MathJax-Element-8-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.94</mn></mrow></semantics></math>\"><span id=\"MathJax-Span-69\" class=\"math\"><span><span id=\"MathJax-Span-70\" class=\"mrow\"><span id=\"MathJax-Span-71\" class=\"semantics\"><span id=\"MathJax-Span-72\" class=\"mrow\"><span id=\"MathJax-Span-73\" class=\"msup\"><span id=\"MathJax-Span-74\" class=\"mi\">R</span><span id=\"MathJax-Span-75\" class=\"mn\">2</span></span><span id=\"MathJax-Span-76\" class=\"mo\">=</span><span id=\"MathJax-Span-77\" class=\"mn\">0.94</span></span></span></span></span></span></span><span>&nbsp;</span><span>) in areas with&nbsp;</span><span id=\"MathJax-Element-9-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; display=&quot;inline&quot;><semantics><mrow><mi>d</mi><mo>&amp;lt;</mo><msub><mi>d</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math>\"><span id=\"MathJax-Span-78\" class=\"math\"><span><span id=\"MathJax-Span-79\" class=\"mrow\"><span id=\"MathJax-Span-80\" class=\"semantics\"><span id=\"MathJax-Span-81\" class=\"mrow\"><span id=\"MathJax-Span-82\" class=\"mi\">d</span><span id=\"MathJax-Span-83\" class=\"mo\">&lt;</span><span id=\"MathJax-Span-84\" class=\"msub\"><span id=\"MathJax-Span-85\" class=\"mi\">d</span><span id=\"MathJax-Span-86\" class=\"mrow\"><span id=\"MathJax-Span-87\" class=\"mi\">m</span><span id=\"MathJax-Span-88\" class=\"mi\">a</span><span id=\"MathJax-Span-89\" class=\"mi\">x</span></span></span></span></span></span></span></span></span><span>&nbsp;</span><span>. As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs11060665","usgsCitation":"Legleiter, C.J., and Fosness, R.L., 2019, Defining the limits of spectrally based bathymetric mapping on a large river: Remote Sensing, v. 11, no. 6, p. 1-29, https://doi.org/10.3390/rs11060665.","productDescription":"Article 665; 29 p.","startPage":"1","endPage":"29","ipdsId":"IP-104066","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467796,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11060665","text":"Publisher Index Page"},{"id":437535,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K54WDL","text":"USGS data release","linkHelpText":"Hyperspectral image data and multibeam echosounder surveys used for bathymetric mapping of the Kootenai River in northern Idaho, September 26-27, 2017"},{"id":362210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Kootenai River","volume":"11","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":759601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fosness, Ryan L. 0000-0003-4089-2704 rfosness@usgs.gov","orcid":"https://orcid.org/0000-0003-4089-2704","contributorId":2703,"corporation":false,"usgs":true,"family":"Fosness","given":"Ryan","email":"rfosness@usgs.gov","middleInitial":"L.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759602,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70202094,"text":"sir20195005 - 2019 - Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018","interactions":[],"lastModifiedDate":"2019-03-21T09:42:58","indexId":"sir20195005","displayToPublicDate":"2019-03-20T14:45:00","publicationYear":"2019","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":"2019-5005","displayTitle":"Regression Models for Estimating Sediment and Nutrient Concentrations and Loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018","title":"Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018","docAbstract":"<p>The Kankakee River in northern Indiana flows through the area once known as the Grand Marsh. Beginning in the 1860s, anthropogenic changes to the river within Indiana resulted in downstream flooding and additional transport of sediment and nutrients. In 2015, the U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, upgraded the gaging station Kankakee River at Shelby, Indiana, to include the collection of water-quality data. By relating continuously monitored water-quality data to discrete data collected from December 2015 through May 2018, linear regression was used to develop models for estimating concentrations of suspended sediment, total nitrogen, and total phosphorus. Developed regression models indicated a strong correlation between turbidity and specific conductance with suspended-sediment concentration (adjusted coefficient of determination equals 0.92, predicted residual error sum of squares equals 0.151), nitrate plus nitrite and specific conductance with total nitrogen (adjusted coefficient of determination equals 0.95, predicted residual error sum of squares equals 0.0248), and turbidity with total phosphorus (adjusted coefficient of determination equals 0.89, predicted residual error sum of squares equals 0.0103).</p><p>Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. Rloadest models were used to compute daily loads of each constituent during gaps in regression model loads. For 2016 and 2017, the estimated annual suspended-sediment loads were 105,000 and 91,000 tons; estimated total nitrogen loads were 8,690 and 8,890 tons; and estimated total phosphorus loads were 265 and 236 tons, respectively.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195005","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Lathrop, T.R., Bunch, A.R., and Downhour, M.S., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018: U.S. Geological Survey Scientific Investigation Report 2019–5005, 13 p., https://doi.org/10.3133/sir20195005.","productDescription":"Report: v, 13 p.; 2  Data Releases","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101520","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":362192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5005/coverthb.jpg"},{"id":362193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5005/sir20195005.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5005"},{"id":362194,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PE9PTD","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and rloadest models for suspended sediment, total nitrogen, and total phosphorus for Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"},{"id":362195,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EKU6X","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and Surrogate Models for Suspended Sediment, Total Nitrogen, and Total Phosphorus for the Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"}],"country":"United States","state":"Indiana","city":"Shelby","otherGeospatial":"Kankakee River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.81396484375,\n              41.34897943069752\n            ],\n            [\n              -86.08337402343749,\n              41.34588656996287\n            ],\n            [\n              -86.08337402343749,\n              41.76721469421018\n            ],\n            [\n              -86.81259155273438,\n              41.76823896512856\n            ],\n            [\n              -86.81396484375,\n              41.34897943069752\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"dc_in@usgs.gov\" data-mce-href=\"dc_in@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>5957 Lakeside Boulevard<br>Indianapolis, IN 46278</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Approach and Methods</li><li>Results of Data Collection—Discrete and Continuous Water-Quality Data</li><li>Regression Models</li><li>Constituent Load Models</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-03-20","noUsgsAuthors":false,"publicationDate":"2019-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Lathrop, Timothy R. 0000-0002-3568-1286 trlathro@usgs.gov","orcid":"https://orcid.org/0000-0002-3568-1286","contributorId":213061,"corporation":false,"usgs":true,"family":"Lathrop","given":"Timothy","email":"trlathro@usgs.gov","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunch, Aubrey R. 0000-0002-2453-3624 aurbunch@usgs.gov","orcid":"https://orcid.org/0000-0002-2453-3624","contributorId":4351,"corporation":false,"usgs":true,"family":"Bunch","given":"Aubrey","email":"aurbunch@usgs.gov","middleInitial":"R.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downhour, Myles S. 0000-0001-6677-412X","orcid":"https://orcid.org/0000-0001-6677-412X","contributorId":213062,"corporation":false,"usgs":true,"family":"Downhour","given":"Myles S.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":false,"id":756862,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70202705,"text":"70202705 - 2019 - State-space analysis of power to detect regional brook trout population trends over time","interactions":[],"lastModifiedDate":"2019-10-28T09:48:18","indexId":"70202705","displayToPublicDate":"2019-03-20T14:42:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"State-space analysis of power to detect regional brook trout population trends over time","docAbstract":"<p><span>Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs, and temporal and spatial variation in abundance. We compiled a regional dataset of brook trout Salvelinus fontinalis counts across their southern range representing 326 sites from eight states between 1982-2014, and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater inter-annual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0241","usgsCitation":"Pregler, K.C., Hanks, R.D., Childress, E., Hitt, N.P., Hocking, D.J., Letcher, B., and Kanno, Y., 2019, State-space analysis of power to detect regional brook trout population trends over time: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 11, p. 2145-2155, https://doi.org/10.1139/cjfas-2018-0241.","productDescription":"11 p.","startPage":"2145","endPage":"2155","ipdsId":"IP-098679","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":362209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"11","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pregler, Kasey C.","contributorId":149616,"corporation":false,"usgs":false,"family":"Pregler","given":"Kasey","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, R. Daniel","contributorId":214286,"corporation":false,"usgs":false,"family":"Hanks","given":"R.","email":"","middleInitial":"Daniel","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":759567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":759564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hocking, Daniel J.","contributorId":214288,"corporation":false,"usgs":false,"family":"Hocking","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":39006,"text":"Frostburg State University","active":true,"usgs":false}],"preferred":false,"id":759568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":759569,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":759570,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70202439,"text":"tm7C22 - 2019 - User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","interactions":[],"lastModifiedDate":"2019-07-26T12:05:14","indexId":"tm7C22","displayToPublicDate":"2019-03-20T11:25:22","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C22","displayTitle":"User’s Manual for the Draper Climate-Distribution Software Suite with Data-Evaluation Tools","title":"User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","docAbstract":"<p>Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual.</p><p>The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application.</p><p>Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C22","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Donovan, J.M., and Koczot, K.M., 2019, User’s manual for the Draper climate-distribution software suite with data‑evaluation tools: U.S. Geological Survey Techniques and Methods 7-C22, 55 p., https://doi.org/10.3133/tm7C22. ","productDescription":"viii, 55 p","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-086388","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":362190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c22/coverthb.jpg"},{"id":362191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c22/tm7c22.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C22"},{"id":365983,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/cawsc/draper","text":"Source code and executables","linkHelpText":"- Users are required to create an account to access the distribution"}],"contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br>U.S. Geological Survey<br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Implementation</li><li>Evaluating and Improving Results</li><li>Iterative Processing for Best Results</li><li>References Cited</li><li>Glossary</li><li>Appendixes 1—8</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2019-03-20","noUsgsAuthors":false,"publicationDate":"2019-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Donovan, John M. 0000-0002-7957-5397 jmd@usgs.gov","orcid":"https://orcid.org/0000-0002-7957-5397","contributorId":1255,"corporation":false,"usgs":true,"family":"Donovan","given":"John","email":"jmd@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koczot, Kathryn M. 0000-0001-5728-9798 kmkoczot@usgs.gov","orcid":"https://orcid.org/0000-0001-5728-9798","contributorId":2039,"corporation":false,"usgs":true,"family":"Koczot","given":"Kathryn","email":"kmkoczot@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758539,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70216766,"text":"70216766 - 2019 - Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","interactions":[],"lastModifiedDate":"2020-12-04T22:03:41.306725","indexId":"70216766","displayToPublicDate":"2019-03-19T15:59:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water","docAbstract":"<p><span>Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free‐ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free‐ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears&nbsp;</span><i>Ursus maritimus</i><span>&nbsp;and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free‐ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer‐based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer‐based measures of energy expenditure strongly correlated (</span><i>r</i><sup>2</sup><span>&nbsp;=&nbsp;0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free‐ranging animals.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5053","usgsCitation":"Pagano, A.M., and Williams, T.M., 2019, Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water: Ecology and Evolution, v. 9, no. 7, p. 4210-4219, https://doi.org/10.1002/ece3.5053.","productDescription":"10 p.","startPage":"4210","endPage":"4219","ipdsId":"IP-101615","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":467799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5053","text":"Publisher Index Page"},{"id":381005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Northwest Territories, Yukon","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -127.35351562499999,\n              70.19999407534661\n            ],\n            [\n              -125.859375,\n              72.97118902284586\n            ],\n            [\n              -157.58789062499997,\n              73.17589717422607\n            ],\n            [\n              -156.62109374999997,\n              71.30079291637452\n            ],\n            [\n              -148.798828125,\n              70.25945200030638\n            ],\n            [\n              -145.1953125,\n              70.05059634999759\n            ],\n            [\n              -139.5703125,\n              69.47296854140573\n            ],\n            [\n              -135.703125,\n              68.65655498475735\n            ],\n            [\n              -127.35351562499999,\n              70.19999407534661\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","issue":"7","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":806132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Terrie M.","contributorId":191735,"corporation":false,"usgs":false,"family":"Williams","given":"Terrie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":806133,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217076,"text":"70217076 - 2019 - Characteristics and spatial variability of wind noise on near-surface broadband seismometers","interactions":[],"lastModifiedDate":"2021-01-04T17:25:38.395765","indexId":"70217076","displayToPublicDate":"2019-03-19T11:18:03","publicationYear":"2019","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":"Characteristics and spatial variability of wind noise on near-surface broadband seismometers","docAbstract":"<p>By coupling with the ground, wind causes ground motion that appears on seismic records as noise across a wide bandwidth. This wind-generated noise can drown out important features such as small earthquakes and prevent observation of normal modes from large earthquakes. Because the wind field is heterogeneous at local scales due to structures, diurnal heating, and topography, wind-induced seismic noise may be different on seismometers installed just meters apart. We have investigated the spatial variability of wind-induced noise using two weather sensors separated by approximately ~100 m and co-located with one deep borehole and four near-surface broadband seismometers. We found that at longer periods (&gt;5 s), increasing wind speed causes increases in noise on the horizontal components of seismometers. While this has been previously observed, we also measured a γ2-coherence of less than 0.2 between the wind speed, wind direction, and the pressure recorded by our weather stations. We further observed a loss of coherence between the vertical components of our seismometers from 8 s to 20 s period. The amplitude of the drop-in coherence appears to depend on the substrate surrounding the seismometer. Based on two previously-developed theoretical models, we found that the local material surrounding the sensor could be amplifying the wind-generated noise. We also investigated the frequency dependence of wind-induced noise and found that the dominant source of high-frequency seismic noise at some sites could be anthropogenic rather than induced by wind. Additionally, we estimated the linear relationship between the root mean squares (RMS) of wind speed and RMS seismic velocity for all sensors, finding substantial variability between different installments. A more detailed understanding of the complex processes by which wind-induced noise is generated can inform the installation of sensors and the development of methods for mitigation of these effects, thus improving the overall quality of seismic data.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180227","usgsCitation":"Dybing, S., Ringler, A.T., Wilson, D.C., and Anthony, R.E., 2019, Characteristics and spatial variability of wind noise on near-surface broadband seismometers: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1082-1098, https://doi.org/10.1785/0120180227.","productDescription":"17 p.","startPage":"1082","endPage":"1098","ipdsId":"IP-103523","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":381855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Alburquerque","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.84478759765624,\n              34.95799531086792\n            ],\n            [\n              -106.46575927734375,\n              34.95799531086792\n            ],\n            [\n              -106.46575927734375,\n              35.240011164750456\n            ],\n            [\n              -106.84478759765624,\n              35.240011164750456\n            ],\n            [\n              -106.84478759765624,\n              34.95799531086792\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"109","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, S. N.","contributorId":246021,"corporation":false,"usgs":false,"family":"Dybing","given":"S. N.","affiliations":[{"id":35028,"text":"Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":807510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807513,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70263728,"text":"70263728 - 2019 - Comparison of site dominant frequency from earthquake and microseismic data in California","interactions":[],"lastModifiedDate":"2025-02-21T14:33:51.778077","indexId":"70263728","displayToPublicDate":"2019-03-19T10:21:12","publicationYear":"2019","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":"Comparison of site dominant frequency from earthquake and microseismic data in California","docAbstract":"<p><span>An important predictive variable for site amplification is the site dominant frequency (</span><span class=\"inline-formula no-formula-id\">⁠ƒ<sub>d</sub>⁠</span><span>). At seismic monitoring stations, ƒ</span><sub><span class=\"inline-formula no-formula-id\">d</span></sub><span>&nbsp;can be calculated from the peak of the horizontal‐to‐vertical spectral ratios (HVSRs) obtained from earthquake recordings (eHVSR). For other sites, ƒ</span><sub><span class=\"inline-formula no-formula-id\">d</span></sub><span>&nbsp;can be estimated from microseismic (mHVSR) observations. We compare the ƒ</span><sub><span class=\"inline-formula no-formula-id\">d</span></sub><span>&nbsp;values derived from eHVSR (5% damped response spectra from the Next Generation Attenuation‐West2 [NGA‐West2] database;&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf2\">Ancheta<span>&nbsp;</span><i>et&nbsp;al.</i>, 2014</a><span>) with those derived from mHVSR (Fourier spectra from&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf50\">Yong<span>&nbsp;</span><i>et&nbsp;al.</i>, 2013</a><span>) for seismic stations in California. We show that the logarithm of eHVSR ƒ</span><sub><span class=\"inline-formula no-formula-id\">d</span></sub><span>&nbsp;scales linearly with the logarithm of mHVSR ƒ</span><span class=\"inline-formula no-formula-id\"><sub>d</sub>⁠</span><span>, with a standard deviation of&nbsp;</span><span class=\"inline-formula no-formula-id\">0.14log<sub>10</sub></span><span>&nbsp;units for mHVSR ƒ</span><sub><span class=\"inline-formula no-formula-id\">d</span></sub><span>&nbsp;larger than 0.2&nbsp;Hz. The relationship holds for microseismic surveys at distances up to 300&nbsp;m away from the seismic stations. The results of this study have beneficial implications for the characterization of site response in modern ground‐motion models as well as in building codes.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180267","usgsCitation":"Behzad Hassani, Yong, A., Gail M. Atkinson, Feng, T., and Meng, L., 2019, Comparison of site dominant frequency from earthquake and microseismic data in California: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1034-1040, https://doi.org/10.1785/0120180267.","productDescription":"7 p.","startPage":"1034","endPage":"1040","ipdsId":"IP-100202","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70205205,"text":"70205205 - 2019 - Relationships between diatom metrics based on species nutrient traits and agricultural land use","interactions":[],"lastModifiedDate":"2019-09-06T10:11:09","indexId":"70205205","displayToPublicDate":"2019-03-19T09:55:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between diatom metrics based on species nutrient traits and agricultural land use","docAbstract":"We assessed how diatom metrics were related to different ranges of agricultural land use. Diatom assemblage composition, nutrients, and landscape characteristics were determined at 232 sites in eight agriculturally dominated study areas of the continental United States. Two regional groups based on differences in diatom relations to human disturbance were determined. Changes in diatom species composition were related to nutrients,pH,and conductivity in the eastern study areas (due to more wetlands) and more exclusively to nutrients in the west-central study areas. Homogenization of diatom flora among streams was related to high agricultural disturbance at this transcontinental scale. Species traits were developed separately for the east and west central study groups and calculated two ways: indicator species analysis for taxa in low and high TN or TP conditions and weighted average partial least squares models of TN and TP concentration. These diatom metrics were significantly related to many indicators of agricultural land use in watersheds, especially percent row crops. Further analysis was conducted on only the west-central region due to its larger sample size.Overall, diatom metrics using species responses to N gradients were better related to agricultural land use than were species responses to P gradients. Most nutrient-based diatom metrics changed greatly in response to low ranges of percent row crops, but only a few high N diatom metrics responded to high row crop conditions. The greater response of diatoms to changes in low agriculture conditions may be due to past diatom evolution occurring when most waters had low nutrient conditions.","language":"English","publisher":"Springer","doi":"10.1007/s10661-019-7357-8","usgsCitation":"Pillsbury, R., Stevenson, R.J., Munn, M., and Waite, I.R., 2019, Relationships between diatom metrics based on species nutrient traits and agricultural land use: Environmental Monitoring and Assessment, v. 191, 228, 28 p., https://doi.org/10.1007/s10661-019-7357-8.","productDescription":"228, 28 p.","ipdsId":"IP-098519","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":367251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Delaware, Florida, Georgia, Idaho, Indiana, Maryland, Minnesota, Mississippi, 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Jan","contributorId":139110,"corporation":false,"usgs":false,"family":"Stevenson","given":"R.","email":"","middleInitial":"Jan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":770352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munn, Mark D. 0000-0002-7154-7252","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":205360,"corporation":false,"usgs":true,"family":"Munn","given":"Mark D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770351,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206912,"text":"70206912 - 2019 - Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","interactions":[],"lastModifiedDate":"2019-11-27T08:18:04","indexId":"70206912","displayToPublicDate":"2019-03-19T07:55:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed","docAbstract":"Reuse of municipal and industrial wastewater treatment plant (WWTP) effluent is an important component in augmenting global freshwater supplies. The Shenandoah River Watershed was selected to conduct on-site exposure experiments to assess endocrine disrupting characteristics of different source waters. This investigation of the Shenandoah River Watershed integrates WWTP wastewater reuse modeling, hydrological and chemical characterization, and in vivo endocrine disruption bioassessment to assess contaminant sources, exposure pathways, and biological effects. The percentage of accumulated WWTP effluent in each river reach (ACCWW) was used to predict environmental concentrations for consumer product chemicals (boron), pharmaceutical compounds (carbamazepine), and steroidal estrogens (estrone, 17-beta-estradiol, estriol, and 17-alpha-ethinylestradiol). Fish endocrine disruption was evaluated using vitellogenin induction in male or juvenile fathead minnows. Water samples were analyzed for >500 inorganic and organic constituents to characterize the complex contaminant mixtures. Municipal ACCWW at drinking water treatment plant surface-water intakes ranged from <0.01 to 2.1 % under mean-annual streamflow and up to 4.7 % under August streamflow. Measured and predicted environmental concentrations resulted in 17-beta-estradiol equivalency quotients ranging from <0.05 to 5.1 ng L-1 indicating low-to-moderate risk of fish endocrine disruption. Results from the fish exposure experiments also showed limited estrogenic effects as indicated by the low (0.5- to 3.2-fold) vitellogenin induction.","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b05655","usgsCitation":"Barber, L., Krstolic, J.L., Kandel, C., Keefe, S.H., Rice, J., Westerhoff, P., Bertolatus, D., and Vajda, A.M., 2019, Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed: Environmental Science & Technology, v. 53, no. 7, p. 3429-3440, https://doi.org/10.1021/acs.est.8b05655.","productDescription":"12 p.","startPage":"3429","endPage":"3440","ipdsId":"IP-099041","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437536,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8S22","text":"USGS data release","linkHelpText":"Assessment of Endocrine Disruption in the Shenandoah River Watershed - Chemical and Biological Data from Mobile Laboratory Fish Exposures and Other Experiments Conducted during 2014, 2015, and 2016"},{"id":369690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Shenandoah River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.8828125,\n              41.64007838467894\n            ],\n            [\n              -77.255859375,\n              42.00032514831621\n            ],\n            [\n              -78.75,\n              40.27952566881291\n            ],\n            [\n              -81.650390625,\n              36.491973470593685\n            ],\n            [\n              -80.2880859375,\n              36.59788913307022\n            ],\n            [\n              -76.5087890625,\n              36.491973470593685\n            ],\n            [\n              -75.234375,\n              39.232253141714885\n            ],\n            [\n              -74.8828125,\n              41.64007838467894\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"53","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":776234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krstolic, Jennifer L. 0000-0003-2253-9886 jkrstoli@usgs.gov","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":3677,"corporation":false,"usgs":true,"family":"Krstolic","given":"Jennifer","email":"jkrstoli@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kandel, Chintamani 0000-0002-3932-9247 ckandel@usgs.gov","orcid":"https://orcid.org/0000-0002-3932-9247","contributorId":197343,"corporation":false,"usgs":true,"family":"Kandel","given":"Chintamani","email":"ckandel@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keefe, Steffanie H. 0000-0002-3805-6101 shkeefe@usgs.gov","orcid":"https://orcid.org/0000-0002-3805-6101","contributorId":2843,"corporation":false,"usgs":true,"family":"Keefe","given":"Steffanie","email":"shkeefe@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":776237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Jacelyn","contributorId":204155,"corporation":false,"usgs":false,"family":"Rice","given":"Jacelyn","email":"","affiliations":[{"id":36866,"text":"University of North Carolina Charlotte","active":true,"usgs":false}],"preferred":false,"id":776238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Westerhoff, Paul","contributorId":204153,"corporation":false,"usgs":false,"family":"Westerhoff","given":"Paul","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":776239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bertolatus, David 0000-0002-6829-9454","orcid":"https://orcid.org/0000-0002-6829-9454","contributorId":220848,"corporation":false,"usgs":false,"family":"Bertolatus","given":"David","email":"","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":776240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vajda, Alan M.","contributorId":179189,"corporation":false,"usgs":false,"family":"Vajda","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":776241,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70202692,"text":"70202692 - 2019 - Dynamic flood modeling essential to assess the coastal impacts of climate change","interactions":[],"lastModifiedDate":"2019-03-18T16:41:02","indexId":"70202692","displayToPublicDate":"2019-03-18T16:40:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic flood modeling essential to assess the coastal impacts of climate change","docAbstract":"<p><span>Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world’s 5</span><sup>th</sup><span>&nbsp;largest economy, over $150 billion of property equating to more than 6% of the state’s GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-40742-z","usgsCitation":"Barnard, P., Erikson, L.H., Foxgrover, A.C., Finzi Hart, J., Limber, P.W., O'Neill, A., van Ormondt, M., Vitousek, S., Wood, N.J., Hayden, M.K., and Jones, J.M., 2019, Dynamic flood modeling essential to assess the coastal impacts of climate change: Scientific Reports, v. 9, p. 1-13, https://doi.org/10.1038/s41598-019-40742-z.","productDescription":"Article number: 4309; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-092817","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467802,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-40742-z","text":"Publisher Index Page"},{"id":362162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foxgrover, Amy C. 0000-0003-0638-5776 afoxgrover@usgs.gov","orcid":"https://orcid.org/0000-0003-0638-5776","contributorId":3261,"corporation":false,"usgs":true,"family":"Foxgrover","given":"Amy","email":"afoxgrover@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finzi Hart, Juliette A.","contributorId":214270,"corporation":false,"usgs":false,"family":"Finzi Hart","given":"Juliette A.","affiliations":[],"preferred":false,"id":759504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":196794,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759502,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"van Ormondt, Maarten","contributorId":147148,"corporation":false,"usgs":false,"family":"van Ormondt","given":"Maarten","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":759505,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vitousek, Sean","contributorId":192286,"corporation":false,"usgs":false,"family":"Vitousek","given":"Sean","affiliations":[],"preferred":false,"id":759506,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":759507,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hayden, Maya K.","contributorId":214271,"corporation":false,"usgs":false,"family":"Hayden","given":"Maya","email":"","middleInitial":"K.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":759508,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Jeanne M. 0000-0001-7549-9270 jmjones@usgs.gov","orcid":"https://orcid.org/0000-0001-7549-9270","contributorId":4676,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","email":"jmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":759509,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70202689,"text":"70202689 - 2019 - Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies","interactions":[],"lastModifiedDate":"2019-03-18T16:35:28","indexId":"70202689","displayToPublicDate":"2019-03-18T16:35:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5818,"text":"AIMS Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stability of temperate coral <i>Astrangia poculata</i> microbiome is reflected across different sequencing methodologies","title":"Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies","docAbstract":"<p><span>The microbiome of the temperate coral&nbsp;</span><i>Astrangia poculata</i><span>&nbsp;was first described in 2017 using next-generation Illumina sequencing to examine the coral’s bacterial and archaeal associates across seasons and among hosts of differing symbiotic status. To assess the impact of methodology on the detectable diversity of the coral’s microbiome, we obtained near full-length Sanger sequences from clone libraries constructed from a subset of the same&nbsp;</span><i>A. poculata</i><span>&nbsp;samples. Eight samples were analyzed: two sets of paired symbiotic (brown) and aposymbiotic (white) colonies collected in the fall (September) and two sets collected in the spring (April). Analysis of the Sanger sequences revealed that the microbiome of&nbsp;</span><i>A. poculata</i><span>exhibited a high level of richness; 806 OTUs were identified among 1390 bacterial sequences. While the Illumina study revealed that&nbsp;</span><i>A. poculata</i><span>’s microbial communities did not significantly vary according to symbiotic state, but did vary by season, Sanger sequencing did not expose seasonal or symbiotic differences in the microbiomes. Proteobacteria dominated the microbiome, forming the majority (55% to 80%) of classifiable bacteria in every sample, and the five bacterial classes with the highest mean relative portion (5% to 35%) were the same as those determined by prior Illumina sequencing. Sanger sequencing also captured the same core taxa previously identified by next-generation sequencing. Alignment of all sequences and construction of a phylogenetic tree revealed that both sequencing methods provided similar portrayals of the phylogenetic diversity within&nbsp;</span><i>A. poculata</i><span>’s bacterial associates. Consistent with previous findings, the results demonstrated that the&nbsp;</span><i>Astrangia</i><span>&nbsp;microbiome is stable notwithstanding the choice of sequencing method and the far fewer sequences generated by clone libraries (46 to 326 sequences per sample) compared to next-generation sequencing (3634 to 48481 sequences per sample). Moreover, the near-full length 16S rRNA sequences produced by this study are presented as a resource for the community studying this model system since they provide necessary information for designing primers and probes to further our understanding of this coral’s microbiome.</span></p>","language":"English","publisher":"AIMS Press","doi":"10.3934/microbiol.2019.1.62","usgsCitation":"Goldsmith, D.B., Pratte, Z.A., Kellogg, C.A., Snader, S.E., and Sharp, K.H., 2019, Stability of temperate coral Astrangia poculata microbiome is reflected across different sequencing methodologies: AIMS Microbiology, v. 5, no. 1, p. 62-76, https://doi.org/10.3934/microbiol.2019.1.62.","productDescription":"15 p.","startPage":"62","endPage":"76","ipdsId":"IP-102024","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467804,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/microbiol.2019.1.62","text":"Publisher Index Page"},{"id":437538,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C2XCQQ","text":"USGS data release","linkHelpText":"Cold-water Coral Microbiomes (Astrangia poculata) from Narragansett Bay: Sequence Data"},{"id":362160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldsmith, Dawn B. 0000-0003-0080-5346 dgoldsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0080-5346","contributorId":191764,"corporation":false,"usgs":true,"family":"Goldsmith","given":"Dawn","email":"dgoldsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":759487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":759488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snader, Sara E.","contributorId":214261,"corporation":false,"usgs":false,"family":"Snader","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":759489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Koty H.","contributorId":214262,"corporation":false,"usgs":false,"family":"Sharp","given":"Koty","email":"","middleInitial":"H.","affiliations":[{"id":39003,"text":"Roger Williams University","active":true,"usgs":false}],"preferred":false,"id":759490,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70202446,"text":"fs20193009 - 2019 - The Missouri groundwater-level observation network","interactions":[],"lastModifiedDate":"2025-05-15T13:22:59.054456","indexId":"fs20193009","displayToPublicDate":"2019-03-18T14:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3009","displayTitle":"The Missouri Groundwater-level Observation Network","title":"The Missouri groundwater-level observation network","docAbstract":"<p>The Missouri groundwater-level observation well network is a series of wells across the State of Missouri in which groundwater levels are monitored in real time and periodically. The wells monitor the water levels in multiple key aquifers, such as the Ozark aquifer in the Salem and Springfield Plateaus and the Mississippi Alluvial Plain aquifer in the South-eastern Lowlands. As of 2018, 150 real-time sites are operated as a cooperative effort between the Missouri Department of Natural Resources (MoDNR) and the U.S. Geological Survey. This fact sheet describes the network and well data from the network.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193009","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Smith, D.C., 2019, The Missouri groundwater-level observation well network (ver. 1.1, March 22, 2019): U.S. Geological Survey Fact Sheet 2019–3009, 2 p., https://doi.org/10.3133/fs20193009.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-098850","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362261,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2019/3009/versionHist.txt","size":"1 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 \"}}]}","edition":"Version 1.0: March 18, 2019; Version 1.1: March 22, 2019","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water\" data-mce-href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>1400 Independence Road, MS-100<br>Rolla, MO 65401</p>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-03-18","revisedDate":"2019-03-22","noUsgsAuthors":false,"publicationDate":"2019-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David C. 0000-0002-9645-2444 dvsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9645-2444","contributorId":206512,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dvsmith@usgs.gov","middleInitial":"C.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":758597,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70199873,"text":"ofr20181159 - 2019 - Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015","interactions":[],"lastModifiedDate":"2019-03-19T16:27:18","indexId":"ofr20181159","displayToPublicDate":"2019-03-18T11:32:43","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1159","displayTitle":"Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015","title":"Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015","docAbstract":"<p>Mercury monitoring results from about 300 <i>Morone saxatilis</i> (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.</p><p>A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at <a data-mce-href=\"https://doi.org/10.5066/F74X560J\" href=\"https://doi.org/10.5066/F74X560J\">https://doi.org/10.5066/F74X560J</a>.</p><p>In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [<i>p</i>] less than (&lt;) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.</p><p>Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being&nbsp;significantly (<i>p</i>&lt;0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)<sup>202</sup>Hg and capital delta (Δ)<sup>199</sup>Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.</p><p>Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll <i>a</i>, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll <i>a</i>, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.63551330566406,\n              36.75594019674357\n            ],\n            [\n              -111.14044189453124,\n              36.75594019674357\n            ],\n            [\n              -111.14044189453124,\n              37.020646433887805\n            ],\n            [\n              -111.63551330566406,\n              37.020646433887805\n            ],\n            [\n              -111.63551330566406,\n              36.75594019674357\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_ut@usgs.gov\" href=\"mailto:%20dc_ut@usgs.gov\">Director</a>, <a data-mce-href=\"https://ut.water.usgs.gov/\" href=\"https://ut.water.usgs.gov/\">Utah Water Science Center</a> <br>U.S. Geological Survey<br>2329 West Orton Circle West <br>Valley City, UT 84119</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>1.0 Introduction</li><li>2.0 Methodology</li><li>3.0 Biogeochemical and Physical Results</li><li>4.0 Discussion of Biogeochemical and Physical Processes</li><li>5.0 Implications of Study Results and Future Study Needs</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Abbreviations and Definitions for Appendixes 2–6</li><li>Appendix 2. Arithmetic and Modeled Least Square Means for Surface Water Parameters</li><li>Appendix 3. Arithmetic and Modeled Least Square Means for Surface Sediment Parameters, by YEAR and TYPE.1</li><li>Appendix 4. Arithmetic and Modeled Least Square Means for Surface Sediment Parameters, by YEAR and TYPE.2</li><li>Appendix 5. Arithmetic and Modeled Least Square Means for Plankton</li><li>Appendix 6. Arithmetic and Modeled Least Square Means for Striped Bass Parameters</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-03-18","noUsgsAuthors":false,"publicationDate":"2019-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":747021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marvin-DiPasquale, Mark 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":149175,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":751251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":118001,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David P.","email":"dpkrabbe@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":751252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiken, George 0000-0001-8454-0984","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":208803,"corporation":false,"usgs":true,"family":"Aiken","given":"George","affiliations":[],"preferred":true,"id":751510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyd, Eric S. 0000-0003-4436-5856","orcid":"https://orcid.org/0000-0003-4436-5856","contributorId":89739,"corporation":false,"usgs":true,"family":"Boyd","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":751511,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conaway, Christopher H. 0000-0002-0991-033X cconaway@usgs.gov","orcid":"https://orcid.org/0000-0002-0991-033X","contributorId":5074,"corporation":false,"usgs":true,"family":"Conaway","given":"Christopher","email":"cconaway@usgs.gov","middleInitial":"H.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":751512,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ogorek, Jacob M. 0000-0002-6327-0740 jmogorek@usgs.gov","orcid":"https://orcid.org/0000-0002-6327-0740","contributorId":4960,"corporation":false,"usgs":true,"family":"Ogorek","given":"Jacob","email":"jmogorek@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":751513,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Gregory M.","contributorId":211329,"corporation":false,"usgs":false,"family":"Anderson","given":"Gregory","email":"","middleInitial":"M.","affiliations":[],"preferred":true,"id":753688,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70215995,"text":"70215995 - 2019 - Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?","interactions":[],"lastModifiedDate":"2021-01-25T16:19:52.207765","indexId":"70215995","displayToPublicDate":"2019-03-18T10:17:16","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?","docAbstract":"<p id=\"ID0EF\" class=\"first\"><i><strong>Context.</strong></i><span>&nbsp;</span>Visual encounter surveying is a standard animal inventory method, modifications of which (e.g. distance sampling and repeated count surveys) are used for modelling population density. However, a variety of factors may bias visual survey counts.</p><p id=\"ID0EJ\"><i><strong>Aims.</strong></i><span>&nbsp;</span>The aim of the present study was to evaluate three observer-related biases: (1) whether fatigue compromises detection rate as a survey occasion progresses; (2) whether long-term fatigue or boredom compromise detection rates over the course of a survey period; and (3) whether observers exhibit biases in detection rates of different animal taxa.</p><p id=\"ID0EN\"><i><strong>Methods.</strong></i><span>&nbsp;</span>We analysed &gt;2.3 × 10<sup>4</sup><span>&nbsp;</span>observations of lizards and small mammals from nocturnal pedestrian visual encounter surveys, each 4 h in duration, conducted by a pool of 29 observers, each of whom surveyed for up to 31 nights.</p><p id=\"ID0ET\"><i><strong>Key results.</strong></i><span>&nbsp;</span>Detections of sleeping (diurnal) emerald tree skinks (<i>Lamprolepis smaragdina</i>) exhibited a small but statistically verified decline as the evening progressed, whereas detections of sleeping (diurnal) green anoles (<i>Anolis carolinensis</i>) increased as the evening progressed. Detections of nocturnal geckos (several species pooled) showed a weak and non-significant declining trend. Small mammal sightings (rats, shrews and mice pooled) declined strongly over the course of an evening. The participants saw greater or equal numbers of animals the more nights they surveyed. Most participants exhibited statistically significant, and often strong, taxonomic detection bias compared with the pool of peer observers. The skills of some observers appeared to be consistently above average; others consistently below average.</p><p id=\"ID0E2\"><i><strong>Conclusions.</strong></i><span>&nbsp;</span>Data on sleeping lizards suggest that neither short-term nor long-term observer fatigue is of much concern for 4-h visual searches. On the contrary, differences among observers in taxonomic bias and overall detection skills pose a problem for data interpretation.</p><p id=\"ID0E6\"><i><strong>Implications.</strong></i><span>&nbsp;</span>By comparing temporal detection patterns of immobile (e.g. sleeping) with actively moving animal taxa, sampling biases attributable to searcher fatigue versus the animals’ circadian rhythm can be disentangled and, if need be, statistically corrected for. Observer skill differences and observer-specific taxonomic biases may hamper efforts to statistically evaluate survey results, unless explicitly included as covariates in population models.</p>","language":"English","publisher":"BioOne","doi":"10.1071/WR18016","usgsCitation":"Lardner, B., Yackel Adams, A.A., Knox, A.J., Savidge, J.A., and Reed, R., 2019, Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?: Wildlife Research, v. 46, no. 2, p. 127-135, https://doi.org/10.1071/WR18016.","productDescription":"9 p.","startPage":"127","endPage":"135","ipdsId":"IP-102306","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467807,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr18016","text":"Publisher Index Page"},{"id":437539,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QTSAHY","text":"USGS data release","linkHelpText":"Visual Surveys Rapid Response Saipan 2016"},{"id":382552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knox, Adam J","contributorId":244306,"corporation":false,"usgs":false,"family":"Knox","given":"Adam","email":"","middleInitial":"J","affiliations":[{"id":40374,"text":"Maui Invasive Species Committee","active":true,"usgs":false}],"preferred":false,"id":803722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803724,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70216101,"text":"70216101 - 2019 - Field-level characteristics influence wild bee functional guilds on public lands managed for conservation","interactions":[],"lastModifiedDate":"2020-11-04T16:03:00.897758","indexId":"70216101","displayToPublicDate":"2019-03-18T09:53:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Field-level characteristics influence wild bee functional guilds on public lands managed for conservation","docAbstract":"<div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">Throughout the Midwestern US, many<span>&nbsp;</span>public lands<span>&nbsp;set aside for conservation engage in management activities (e.g., agriculture) that may act as stressors on wild bee populations. Several studies have investigated how wild bees respond to large-scale agriculture production; however, there has been limited assessment of how wild bees may be impacted by agricultural activity on public lands or how local variables may influence bee communities in these same areas. In this study, we assessed the abundance and richness of wild bee floral and nesting guilds at 30 agricultural field margins located on five Conservation Areas in Missouri. Generally, regardless of guild, bee abundance and richness was greater in field margins with more floral diversity and taller vegetation. Bee guilds responded negatively to agricultural production in Conservation Areas with fewer soil- and cavity-nesting bees collected in margins adjacent to annually cropped fields. Although fewer diet specialists were collected, specialist bee abundance and richness was greater in margins&nbsp;near fields&nbsp;that were uncropped (i.e., vegetated, but not row-cropped) during the previous year. Overall, the percentage of trees and shrubs within 800 m of study fields (i.e., “woodland”) was negatively associated with abundance and richness of bees, but specifically, reduced richness of soil-nesters and diet specialists. Our findings indicate agricultural management activities on public lands may lead to decreased abundance and richness of wild bee guilds. If public lands are to be managed for species diversity, including wild bees, maintaining diverse plant communities with taller vegetation (&gt;100 cm) near cultivated fields and/or modifying agricultural production practices on public lands may greatly improve the conservation of local bee communities.</span></p></div></div><div id=\"abs0015\" class=\"abstract graphical\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2019.e00598","usgsCitation":"Main, A., Webb, E.B., Goyne, K.W., and Mengel, D., 2019, Field-level characteristics influence wild bee functional guilds on public lands managed for conservation: Global Ecology and Conservation, v. 17, e00598, 12 p., https://doi.org/10.1016/j.gecco.2019.e00598.","productDescription":"e00598, 12 p.","ipdsId":"IP-103283","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00598","text":"Publisher Index Page"},{"id":380125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Main, A.R.","contributorId":244517,"corporation":false,"usgs":false,"family":"Main","given":"A.R.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":804091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":804092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goyne, K. W.","contributorId":244518,"corporation":false,"usgs":false,"family":"Goyne","given":"K.","email":"","middleInitial":"W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":804093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mengel, D.","contributorId":244519,"corporation":false,"usgs":false,"family":"Mengel","given":"D.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":804094,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215505,"text":"70215505 - 2019 - Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards","interactions":[],"lastModifiedDate":"2020-10-21T14:38:05.881233","indexId":"70215505","displayToPublicDate":"2019-03-18T09:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3503,"text":"Surveys in Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the&nbsp;IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long&nbsp;term future, together with the increasing availability, quality and moderate cost of off&nbsp;the&nbsp;shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS&nbsp;derived measurement can be integrated into operational monitoring services.</p></div></div><div id=\"Sec1-section\" class=\"c-article-section\"><br></div>","language":"English","publisher":"Springer","doi":"10.1007/s10712-019-09523-1","usgsCitation":"Ong, C., Carrere, V., Chabrillat, S., Clark, R., Hoefen, T.M., Kokaly, R.F., Marion, R., Souza Filho, C.R., Swayze, G.A., and Thompson, D.R., 2019, Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards: Surveys in Geophysics, v. 40, no. 3, p. 431-470, https://doi.org/10.1007/s10712-019-09523-1.","productDescription":"40 p.","startPage":"431","endPage":"470","ipdsId":"IP-093733","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467809,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10712-019-09523-1","text":"Publisher Index Page"},{"id":379585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Leadville Mining District","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.6607666015625,\n              39.06398173012625\n            ],\n            [\n              -106.02630615234374,\n              39.06398173012625\n            ],\n            [\n              -106.02630615234374,\n              39.58452390500424\n            ],\n            [\n              -106.6607666015625,\n              39.58452390500424\n            ],\n            [\n              -106.6607666015625,\n              39.06398173012625\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Ong, Cindy 0000-0002-9168-2865","orcid":"https://orcid.org/0000-0002-9168-2865","contributorId":243558,"corporation":false,"usgs":false,"family":"Ong","given":"Cindy","email":"","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":802547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carrere, Veronique","contributorId":243559,"corporation":false,"usgs":false,"family":"Carrere","given":"Veronique","email":"","affiliations":[{"id":41660,"text":"Université de Nantes","active":true,"usgs":false}],"preferred":false,"id":802548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chabrillat, Sabine 0000-0001-8600-5168","orcid":"https://orcid.org/0000-0001-8600-5168","contributorId":243560,"corporation":false,"usgs":false,"family":"Chabrillat","given":"Sabine","email":"","affiliations":[{"id":48729,"text":"Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ","active":true,"usgs":false}],"preferred":false,"id":802549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Roger N.","contributorId":225047,"corporation":false,"usgs":false,"family":"Clark","given":"Roger N.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":802550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":802551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":802552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marion, Rodolphe","contributorId":243561,"corporation":false,"usgs":false,"family":"Marion","given":"Rodolphe","email":"","affiliations":[{"id":48730,"text":"Commissariat à l'Energie Atomique","active":true,"usgs":false}],"preferred":false,"id":802553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Souza Filho, Carlos Roberto de","contributorId":193999,"corporation":false,"usgs":false,"family":"Souza Filho","given":"Carlos","email":"","middleInitial":"Roberto de","affiliations":[],"preferred":false,"id":802554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Swayze, Gregg A. 0000-0002-1814-7823 gswayze@usgs.gov","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":518,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"gswayze@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":802555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thompson, David R. 0000-0003-0635-5876","orcid":"https://orcid.org/0000-0003-0635-5876","contributorId":225042,"corporation":false,"usgs":false,"family":"Thompson","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":41027,"text":"NASA JPL/CalTech","active":true,"usgs":false}],"preferred":false,"id":802556,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70223502,"text":"70223502 - 2019 - Black-tailed prairie dog, Cynomys ludovicianus (Sciuridae), metapopulation response to novel sourced conspecific signals","interactions":[],"lastModifiedDate":"2021-08-31T14:18:08.825538","indexId":"70223502","displayToPublicDate":"2019-03-18T09:13:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":770,"text":"Animal Behaviour","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Black-tailed prairie dog, <i>Cynomys ludovicianus</i> (Sciuridae), metapopulation response to novel sourced conspecific signals","title":"Black-tailed prairie dog, Cynomys ludovicianus (Sciuridae), metapopulation response to novel sourced conspecific signals","docAbstract":"<p><span>Aggregation of territorial individuals within a species can be facilitated via conspecific signals, wherein settlement implies habitat suitability, ease of resource acquisition and/or increased predator detection. The black-tailed prairie dog is a colonial&nbsp;</span><a class=\"topic-link\" title=\"Learn more about small mammal from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/small-mammals\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/small-mammals\">small mammal</a><span>&nbsp;with alarm vocalizations that confer benefits via group vigilance against predators and increased foraging time. Although prairie dog alarm calls are relatively well understood, the information embedded in their jump-yip call, which includes both a distinct cry and a bodily gesture, remains less clear. We evaluated prairie dog behaviour in response to conspecific acoustic signals using playbacks of alarm and jump-yip calls at 26 sites in northeastern Wyoming, U.S.A. Recorded calls from an isolated colony were broadcast to a mean of five individuals per site, and behavioural responses were compared against uninfluenced behaviour and a control playback of ambient sounds. The alarm playback caused prairie dogs to increase vigilance 122% and decrease foraging time 23%, demonstrating prairie dogs will shift behaviour based on signals from individuals of an unfamiliar colony. However, the alarm call playback reduced frequency of the jump-yip behaviour only at colonies nearest the recording source. The jump-yip playback caused unfamiliar prairie dogs to display 339% more jump-yips than uninfluenced behaviour. The jump-yip playback did not alter recipients' foraging or vigilance behaviours relative to control treatments, suggesting that although prairie dogs can understand and reciprocate an unfamiliar, single modality signal, they may not shift other behaviours based on this stimulus. As such, the purpose and benefits of the jump-yip call remain unclear. Playback efficacy also had a nonlinear relationship with distance from recording source. Our work improves understanding of communication at the metapopulation level, examines the potential role of the jump-yip and provides insights for how conspecific signals might be used as a management tool.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2019.02.004","usgsCitation":"Chalfoun, A.D., Connell, L.C., Porensky, L., and Scasta, J.D., 2019, Black-tailed prairie dog, Cynomys ludovicianus (Sciuridae), metapopulation response to novel sourced conspecific signals: Animal Behaviour, v. 150, p. 189-199, https://doi.org/10.1016/j.anbehav.2019.02.004.","productDescription":"11 p.","startPage":"189","endPage":"199","ipdsId":"IP-104048","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467810,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.anbehav.2019.02.004","text":"Publisher Index Page"},{"id":388691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Thunder Basin National Grassland","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.369873046875,\n              44.56699093657141\n            ],\n            [\n              -105.084228515625,\n              44.56699093657141\n            ],\n            [\n              -105.084228515625,\n              44.83249999349062\n            ],\n            [\n              -105.369873046875,\n              44.83249999349062\n            ],\n            [\n              -105.369873046875,\n              44.56699093657141\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      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M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":822193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scasta, John D.","contributorId":264927,"corporation":false,"usgs":false,"family":"Scasta","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":822194,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70219067,"text":"70219067 - 2019 - Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy","interactions":[],"lastModifiedDate":"2021-03-23T14:44:11.665397","indexId":"70219067","displayToPublicDate":"2019-03-17T09:38:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy","docAbstract":"<p id=\"sp0090\"><span>Solid organic matter (OM) in sedimentary rocks produces petroleum and solid&nbsp;bitumen&nbsp;when it undergoes thermal maturation. The solid OM is a ‘geomacromolecule’, usually representing a mixture of various organisms with distinct biogenic origins, and can have high heterogeneity in composition. Programmed&nbsp;pyrolysis&nbsp;is a common method to reveal bulk geochemical characteristics of the dominant OM, while detailed organic&nbsp;</span>petrography<span>&nbsp;is required to reveal information about the biogenic origin of contributing&nbsp;macerals. Despite the advantages of programmed pyrolysis, it cannot provide information about the heterogeneity of chemical compositions present in the individual OM types. Therefore, other analytical techniques such as&nbsp;Raman spectroscopy&nbsp;are necessary.</span></p><p id=\"sp0095\">In this study, we compared geochemical characteristics and<span>&nbsp;</span>Raman spectra<span>&nbsp;of two sets of naturally and artificially matured Bakken source rock samples. A continuous Raman spectral map on solid bitumen particles was created from the artificially matured hydrous pyrolysis residues, in particular, to show the systematic chemical modifications in&nbsp;microscale. Spectroscopic data was plotted for both sets against&nbsp;thermal maturity&nbsp;to compare maturation rate/path for these two separate groups. The outcome showed that artificial maturation through hydrous pyrolysis does not follow the same trend as naturally-matured samples although having similar solid bitumen reflectance values (%SBRo).</span></p><p id=\"sp0100\">Furthermore, Raman spectroscopy of solid bitumen from artificially matured samples indicated the heterogeneity of OM decreases as maturity increases. This may represent an alteration in chemical structure towards more uniform compounds at higher maturity. This study may emphasize the necessity of using analytical methods such as Raman spectroscopy along with conventional<span>&nbsp;</span>geochemical methods<span>&nbsp;</span>to better reveal the underlying chemical structure of OM. Finally, observation by Raman spectroscopy of chemical alteration of OM during artificial maturation may assist in the proposal of improved pyrolysis protocols to better resemble natural geologic processes.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2019.03.009","usgsCitation":"Khatibi, S., Ostadhassan, M., Hackley, P.C., Tuschel, D., Abarghani, A., and Bubach, B., 2019, Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy: International Journal of Coal Geology, v. 206, p. 46-64, https://doi.org/10.1016/j.coal.2019.03.009.","productDescription":"19 p.","startPage":"46","endPage":"64","ipdsId":"IP-101108","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2019.03.009","text":"Publisher Index Page"},{"id":437540,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P975KILE","text":"USGS data release","linkHelpText":"Analyzing Heterogeneity in Artificially Matured Samples of Bakken Shales (2018)"},{"id":384583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.029541015625,\n              46.28622391806706\n            ],\n            [\n              -98.93188476562499,\n              46.28622391806706\n            ],\n            [\n              -98.93188476562499,\n              49.001843917978526\n            ],\n            [\n              -104.029541015625,\n              49.001843917978526\n            ],\n            [\n              -104.029541015625,\n              46.28622391806706\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Khatibi, Seyedalireza","contributorId":255596,"corporation":false,"usgs":false,"family":"Khatibi","given":"Seyedalireza","email":"","affiliations":[{"id":51594,"text":"Univ. North Dakota","active":true,"usgs":false}],"preferred":false,"id":812636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostadhassan, Mehdi","contributorId":255578,"corporation":false,"usgs":false,"family":"Ostadhassan","given":"Mehdi","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tuschel, David","contributorId":255597,"corporation":false,"usgs":false,"family":"Tuschel","given":"David","email":"","affiliations":[{"id":51595,"text":"HORIBA Scientific","active":true,"usgs":false}],"preferred":false,"id":812639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abarghani, Arash","contributorId":255576,"corporation":false,"usgs":false,"family":"Abarghani","given":"Arash","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bubach, Bailey","contributorId":255598,"corporation":false,"usgs":false,"family":"Bubach","given":"Bailey","email":"","affiliations":[{"id":51594,"text":"Univ. 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