The range of the southern sea otter (Enhydra lutris nereis) spans most of the central California coast from Half Moon Bay to Gaviota. Some coastal areas within this range are heavily developed and highly impacted by humans, while other areas are wild and largely pristine. Determining the relative importance of food resource abundance, environmental conditions, and anthropogenic increases in pathogens and pollutants to population change in sea otters is critical to understanding limitations to population growth. To investigate the causal links between the sluggish population growth of sea otters in central California and factors that could be driving variation in survival and reproduction, we designed a study to compare two distinct subpopulations—one in an area of low human impact (Big Sur) and one in an area of high human impact (Monterey). Between 2008 and 2011, the U.S. Geological Survey and collaborators conducted a telemetry-based study of sea otters at these two locations. The results of this study were not consistent with the hypothesis that sea otters adjacent to human population centers (Monterey) experience higher exposure to pollutants and pathogens than those in lower impacted areas (Big Sur). In fact, based on serological analysis, female sea otters from Big Sur showed higher exposure rates to Toxoplasma gondii than did female otters from Monterey, while domoic acid exposure appeared to be similar at both sites. Gene expression (specifically transcription) analysis did not indicate any consistent differences between the two populations that would have suggested a response to pathogen or toxin exposure, although there were temporal changes in gene transcription for sea otters at Big Sur following potential exposure to run-off from wildfires that occurred during the study. Together, these metrics suggest that variation in exposure to environmental stressors occurred, but patterns were not clearly attributable to differences in human population densities or land-use patterns. When compared to Monterey, sea otters in Big Sur spent more time feeding, had a higher degree of dietary specialization, were in poorer body condition, and had lower survival rates (both pups and adults). Together, these metrics suggest that otters at Big Sur had greater nutritional stress, consistent with lower per-capita resource abundance. Overall, study results indicate that density-dependent population regulation, mediated by per-capita resource abundance, is the most significant factor currently limiting population growth in the center part of the range. Additionally, spatial and temporal variation in environmental and anthropogenic stressors also can affect sea otter health, although patterns of variation are complex and are not simply a function of proximity to human populations. We also found that exposure to environmental stressors (either natural or anthropogenic in origin) often is associated with resource limitation. Finally, our results indicate that sea otter populations are structured at relatively small spatial scales, and the processes that regulate population abundance (including density-dependent resource abundance) also occur at these smaller, more local scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191022","usgsCitation":"Tinker, M.T., Tomoleoni, J.A., Weitzman, B.P., Staedler, M., Jessup, D., Murray, M.J., Miller, M., Burgess, T., Bowen, L., Miles, A.K., Thometz, N., Tarjan, L., Golson, E., Batac, F., Dodd, E., Berberich, E., Kunz, J., Bentall, G., Fujii, J., Nicholson, T., Newsome, S., Melli, A., LaRoche, N., MacCormick, H., Johnson, A., Henkel, L., Kreuder-Johnson, C., and Conrad, P., 2019, Southern sea otter (Enhydra lutris nereis) population biology at Big Sur and Monterey, California --Investigating the consequences of resource abundance and anthropogenic stressors for sea otter recovery: U.S. Geological Survey Open-File Report 2019 -1022, 225 p., https://doi.org/10.3133/ofr20191022.","productDescription":"xiv, 225 p.","numberOfPages":"244","onlineOnly":"Y","ipdsId":"IP-066698","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437531,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98B08RO","text":"USGS data release","linkHelpText":"Sea Otter Capture Data from the Big Sur-Monterey Study (2008-2011)"},{"id":362213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1022/coverthb.jpg"},{"id":362214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1022/ofr20191022.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1022"}],"country":"United States","state":"California","otherGeospatial":"Monterey, Big Sur","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.02651977539062,\n 36.488661268293136\n ],\n [\n -121.74774169921875,\n 36.488661268293136\n ],\n [\n -121.74774169921875,\n 36.677230602346214\n ],\n [\n -122.02651977539062,\n 36.677230602346214\n ],\n [\n -122.02651977539062,\n 36.488661268293136\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.95098876953125,\n 36.2165791734887\n ],\n [\n -121.74636840820312,\n 36.2165791734887\n ],\n [\n -121.74636840820312,\n 36.3693276982337\n ],\n [\n -121.95098876953125,\n 36.3693276982337\n ],\n [\n -121.95098876953125,\n 36.2165791734887\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
Fluids are well known to influence earthquakes, yet rarely are earthquakes convincingly linked to precipitation. Weak modulation or limited data often leads to ambiguous interpretations. In contrast, here we find that shallow seismicity in the Sierra Nevada range near Long Valley Caldera is strongly modulated by snowmelt. Over 33 years, shallow seismicity rates were ~37 times higher during very wet periods versus very dry periods. Relative earthquake relocations from a swarm in 2017 reveal downward migration from ~1- to 3-km depth along a steeply inclined plane. Steeply dipping strata may provide high-permeability pathways and faulting plane. Here we combine the correlated seismicity and hydrologic time series with the propagation observed in the relatively relocated earthquakes. From this combined evidence, we infer that pressure diffusion from groundwater recharge dramatically accelerated shallow seismicity rates, causing seismic swarms unrelated to volcanic processes.
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.
","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":"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−2 (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 × 106 km2 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.
","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":70202720,"text":"70202720 - 2019 - Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA","interactions":[],"lastModifiedDate":"2019-06-18T11:08:36","indexId":"70202720","displayToPublicDate":"2019-03-21T13:08:41","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":"Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA","docAbstract":"Regulations for public water systems (PWS) in the U.S. consider Cryptosporidium a microbial contaminant of surface water supplies. Ground- water is assumed free of Cryptosporidium unless surface water is entering supply wells. We determined the incidence of Cryptosporidium in PWS wells varying in surface water influence. Community and noncommunity PWS wells (n = 145) were sampled (n = 964) and analyzed for Cryptosporidium by qPCR and immunofluorescence assay (IFA). Surface water influence was assessed by stable isotopes and the expert judgment of hydrogeologists using site-specific data. Fifty-eight wells (40%) and 107 samples (11%) were Cryptosporidium- positive by qPCR, and of these samples 67 were positive by IFA. Cryptosporidium concentrations measured by qPCR and IFA were significantly\ncorrelated (p < 0.001). Cryptosporidium incidence was not associated with surface water influence as assessed by stable isotopes or expert judgment. We successfully sequenced 45 of the 107 positive samples to identify species, including C. parvum (41), C. andersoni (2), and C. hominis (2), and the predominant subtype was C. parvum IIa A17G2R1. Assuming USA regulations for surface water-supplied PWS were applicable to the study wells, wells positive for Cryptosporidium by IFA would likely be required to add treatment. Cryptosporidium is not uncommon in groundwater, even when surface water influence is absent.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.8b05446","usgsCitation":"Stokdyk, J.P., Spencer, S., Walsh, J.F., de Lambert, J.R., Fimstahl, A., Anderson, A., Rezania, L.W., and Borchardt, M.A., 2019, Cryptosporidium incidence and surface water influence of groundwater supplying public water systems in Minnesota, USA: Environmental Science & Technology, v. 23, no. 7, p. 3391-3398, https://doi.org/10.1021/acs.est.8b05446.","productDescription":"8 p.","startPage":"3391","endPage":"3398","ipdsId":"IP-102064","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":362245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"23","issue":"7","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, Susan K.","contributorId":39511,"corporation":false,"usgs":true,"family":"Spencer","given":"Susan K.","affiliations":[],"preferred":false,"id":759667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, James F.","contributorId":214333,"corporation":false,"usgs":false,"family":"Walsh","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":759668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Lambert, Jane R.","contributorId":214334,"corporation":false,"usgs":false,"family":"de Lambert","given":"Jane","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":759669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fimstahl, Aaron D. 0000-0003-2686-7596","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":214335,"corporation":false,"usgs":false,"family":"Fimstahl","given":"Aaron D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":759670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Anita C.","contributorId":214336,"corporation":false,"usgs":false,"family":"Anderson","given":"Anita C.","affiliations":[],"preferred":false,"id":759671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rezania, Lih-in W.","contributorId":214337,"corporation":false,"usgs":false,"family":"Rezania","given":"Lih-in","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":759672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":759673,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209267,"text":"70209267 - 2019 - Comparison of methods to examine diet of feral horses from non-invasively collected fecal samples","interactions":[],"lastModifiedDate":"2020-03-26T13:09:26","indexId":"70209267","displayToPublicDate":"2019-03-21T13:05:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of methods to examine diet of feral horses from non-invasively collected fecal samples","docAbstract":"Feral horses (Equus ferus caballus) have become abundant on public lands in the American West, particularly over the past 10 yr. In areas where they are overabundant, there is risk of habitat degradation. Most previous studies on diet and habitat use of feral horses were conducted more than 20 yr ago; rangelands have changed considerably in that time, so it is useful to revisit horse diets. We conducted a study to examine the diet of feral horses using noninvasive methods and subjectively compare diet analysis techniques. We collected feral horse fecal samples from a sagebrush/pinyon-juniper ecosystem in Colorado in May, August, and October 2014. We analyzed 30 fecal samples from each collection session by both microhistology and plant DNA barcoding. Both microhistology and plant DNA barcoding results indicated horse diet consisted primarily of graminoids (78.5% and 68.8%, respectively, both of which are in greater proportion than availability based on ecological site descriptions); however, the two methods differed in species composition of grasses. Similar to other studies, microhistological analyses underestimated the proportion of forbs in the diet compared with plant DNA barcoding analyses, which showed a surprisingly high contribution of forbs to the diet compared with previous studies. Our results suggest plant DNA barcoding analyses have great potential, although both methods have inherent biases.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2019.02.005","usgsCitation":"King, S., and Schoenecker, K.A., 2019, Comparison of methods to examine diet of feral horses from non-invasively collected fecal samples: Rangeland Ecology and Management, v. 72, no. 4, p. 661-666, https://doi.org/10.1016/j.rama.2019.02.005.","productDescription":"6 p.","startPage":"661","endPage":"666","ipdsId":"IP-092648","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467790,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2019.02.005","text":"Publisher Index Page"},{"id":437532,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VLB2YZ","text":"USGS data release","linkHelpText":"Fecal samples collected in May, August, and October 2014 from Little Book Cliffs Herd Management Area, Colorado, for determination of diet, persistence of DNA in the environment, individual identity, and seed germination."},{"id":373557,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Grand Junction","otherGeospatial":"Little Book Cliffs Wild Horse Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.51882934570312,\n 39.12473362566029\n ],\n [\n -108.29910278320312,\n 39.12473362566029\n ],\n [\n -108.29910278320312,\n 39.29498546816049\n ],\n [\n -108.51882934570312,\n 39.29498546816049\n ],\n [\n -108.51882934570312,\n 39.12473362566029\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"72","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Sarah R.B.","contributorId":127791,"corporation":false,"usgs":false,"family":"King","given":"Sarah R.B.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":785635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X schoeneckerk@usgs.gov","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":2001,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn","email":"schoeneckerk@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":785634,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209269,"text":"70209269 - 2019 - Potential spread of cheatgrass (Bromus tectorum) by feral horses (Equus ferus caballus) in Western Colorado","interactions":[],"lastModifiedDate":"2020-03-26T13:04:12","indexId":"70209269","displayToPublicDate":"2019-03-21T12:58:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Potential spread of cheatgrass (Bromus tectorum) by feral horses (Equus ferus caballus) in Western Colorado","title":"Potential spread of cheatgrass (Bromus tectorum) by feral horses (Equus ferus caballus) in Western Colorado","docAbstract":"The invasive grass cheatgrass (Bromus tectorum L.) presents major challenges for land management and habitat conservation in the western United States. Feral horses (Equus ferus caballus) have become overabundant in some areas of the West and can impact fragile semiarid ecosystems. Amid ongoing efforts to control cheatgrass in the Great Basin, we conducted a study to determine if feral horses contribute to the spread of cheatgrass through distribution via their feces. We collected feral horse fecal samples from Little Book Cliffs Herd Management Area in western Colorado in 2014. Fecal samples were dried, and 20 from each of 3 collection sessions were cultivated to examine germination success. Six species germinated from 18 samples (30%; mostly one plant per sample where germination occurred), including cheatgrass from 8% of samples. In a separate study we examined the diet of this same horse population using fecal plant DNA barcoding. Plant species that germinated were rare in the diet and germinated from fewer samples than expected relative to their detection in the diet. Our results suggest that feral horses could be contributing to cheatgrass propagation. Native ungulates and domestic cattle also have this potential. Although management of all large ungulates is necessary to mitigate cheatgrass spread, control of feral horse numbers is particularly necessary.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2019.02.006","usgsCitation":"King, S., Schoenecker, K.A., and Manier, D.J., 2019, Potential spread of cheatgrass (Bromus tectorum) by feral horses (Equus ferus caballus) in Western Colorado: Rangeland Ecology and Management, v. 72, no. 4, p. 706-710, https://doi.org/10.1016/j.rama.2019.02.006.","productDescription":"5 p.","startPage":"706","endPage":"710","ipdsId":"IP-095248","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467791,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2019.02.006","text":"Publisher Index Page"},{"id":373556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Grand Rapids","otherGeospatial":"Little Book Cliffs Herd Management Aarea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.51882934570312,\n 39.12473362566029\n ],\n [\n -108.29910278320312,\n 39.12473362566029\n ],\n [\n -108.29910278320312,\n 39.29498546816049\n ],\n [\n -108.51882934570312,\n 39.29498546816049\n ],\n [\n -108.51882934570312,\n 39.12473362566029\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"72","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"King, Sarah R.B.","contributorId":127791,"corporation":false,"usgs":false,"family":"King","given":"Sarah R.B.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":785641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X schoeneckerk@usgs.gov","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":2001,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn","email":"schoeneckerk@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":785640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manier, Daniel J. 0000-0002-1105-1327 manierd@usgs.gov","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":127553,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","email":"manierd@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":785642,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":"Lake Sinai Viruses (Sinaivirus) are commonly detected in honey bees (Apis mellifera) 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 Varroa destructor, 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.
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 American black bear (Ursus americanus) 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.
","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":70202482,"text":"sir20195013 - 2019 - Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-03-26T08:18:03","indexId":"sir20195013","displayToPublicDate":"2019-03-21T09:45:09","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":"2019-5013","displayTitle":"Hydraulic Conductivity Estimates from Slug Tests in the Big Sioux Aquifer Near Sioux Falls, South Dakota","title":"Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","docAbstract":"Hydraulic conductivity estimates were made for 15 observation wells using slug-out (rising-head) tests in the Big Sioux aquifer near Sioux Falls, South Dakota, as part of a cooperative study with the City of Sioux Falls to characterize the hydrogeology and the extent of the Big Sioux aquifer north of the city. Well and aquifer data were collected from field measurements and drillers’ logs. Multiple slug tests were completed at each observation well with a transducer to record the change in water level and a U.S. Geological Survey standard mechanical slug to displace the well’s water column. In total, 110 slug-out test trials were completed among the 15 observation wells. Hydraulic conductivity was estimated by curve fitting with AQTESOLV Pro version 4.50.002. Hydraulic conductivity estimates ranged from 64 to 379 feet per day (ft/d). The mean, standard deviation, and median hydraulic conductivity for the 110 slug-out test trials were 171 ft/d, 73 ft/d, and 157 ft/d, respectively. The mean hydraulic conductivity calculated for each well ranged from 88 to 270 ft/d, the standard deviation ranged from 7 to 66 ft/d, and the median hydraulic conductivity ranged from 86 to 256 ft/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195013","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Eldridge, W.G., and Medler, C.J., 2019, Hydraulic conductivity estimates from slug tests in the Big Sioux Aquifer near Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5013, 23 p., https://doi.org/10.3133/sir20195013.","productDescription":"Report: v, 24 p., Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-100666","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":362206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5013/coverthb.jpg"},{"id":362207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5013/sir20195013.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5013"},{"id":362208,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release","linkHelpText":"Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","otherGeospatial":"Big Sioux Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.846997999996,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.462111\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Pacific sand lance Ammodytes personatus are a key forage fish in the North Pacific for many species of salmon, groundfish, seabirds, and marine mammals and have historically been important to predators in relatively warm years. However, extreme declines in the nutritional value of sand lance in Prince William Sound, Alaska, USA, during 2012-2016 indicate that energy transfer from lower trophic levels to predators via sand lance may have been disrupted during the North Pacific marine heatwave in 2015 and 2016. Nutritional value (length, energy density, and whole-body energy) was measured in age-0 and age-1 sand lance collected during July in cool (2012-2013) and increasingly warm (2014-2016) years. The value of age-0 fish was relatively stable, with only minor differences among years for length and whole-body energy. By contrast, the value of age-1 fish significantly declined in 2015, and by 2016 they were 38% shorter and 13% lower in energy density compared to cooler years. This contributed to significant declines in whole-body energy of 44% in 2015 and 89% in 2016 compared to cooler years (2012-2014). The 2015 sand lance cohort experienced little growth or lipid accumulation from July 2015 at age-0 to July 2016 at age-1. This effective disruption of energy flow through pelagic food webs probably contributed to population declines and/or breeding failures observed among several predators in the Gulf of Alaska and suggests that tipping points were reached during the heatwave.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps12891","usgsCitation":"von Biela, V.R., Arimitsu, M.L., Piatt, J.F., Heflin, B., Schoen, S.K., Trowbridge, J., and Clawson, C., 2019, Extreme reduction in nutritional value of a key forage fish during the Pacific marine heatwave of 2014–2016: Marine Ecology Progress Series, v. 613, p. 171-182, https://doi.org/10.3354/meps12891.","productDescription":"12 p.","startPage":"171","endPage":"182","ipdsId":"IP-101543","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":467793,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps12891","text":"Publisher Index Page"},{"id":437534,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96N5PVE","text":"USGS data release","linkHelpText":"Pacific Sand Lance Energy Density, Length, and Age, Prince William Sound, Alaska, 2012-2016"},{"id":363578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"613","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":762341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heflin, Brielle 0000-0002-4836-9187 bheflin@usgs.gov","orcid":"https://orcid.org/0000-0002-4836-9187","contributorId":198164,"corporation":false,"usgs":true,"family":"Heflin","given":"Brielle","email":"bheflin@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762345,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trowbridge, Jannelle","contributorId":215435,"corporation":false,"usgs":false,"family":"Trowbridge","given":"Jannelle","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":762346,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clawson, Chelsea","contributorId":215436,"corporation":false,"usgs":false,"family":"Clawson","given":"Chelsea","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":762347,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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":"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.
Regional climate is an important control on the rate of coarse sediment mobilization and transport in alpine river systems. Changes in climate are then expected to cause a cascade of geomorphic responses, including adjustments in downstream channel morphology. However, the mechanics and sensitivity of channel response to short‐term climate variability remain poorly documented. In the Nooksack River, which drains a glaciated stratovolcano in Washington State, bed elevation changes were inferred from shifting stage–discharge relations at seven USGS stream gages. Decadal‐scale elevation trends at most sites can be explained as a downstream‐propagating channel response to regional climate variability, where periods of persistent warm, dry [cool, wet] conditions corresponded to periods of aggradation [incision]. The channel elevation response propagated downstream at a rate of one to four kilometers per year; propagation rate scaled closely with channel slope. Historical trends in glacier extent and flood intensity both show some potential to explain climate–sediment linkages, though assessing causation is complicated by the shared climate signal in both records. Results show the influence of the Pacific Decadal Oscillation, with relatively high coarse sediment yields prior to 1950 and since 1980, and notably lower sediment yields from 1950 to 1980. Measured sediment yields from nearby glaciated basins corroborate this history, suggesting a regional coherence to these climate–sediment linkages. These results document consistent relations between climate, sediment supply and downstream channel response at the basin‐scale, with channel responses propagating downstream over periods of decades with little apparent attenuation.
","language":"English","publisher":"AGU","doi":"10.1029/2018JF004734","usgsCitation":"Anderson, S.W., and Konrad, C.P., 2019, Downstream‐propagating channel responses to decadal‐scale climate variability in a glaciated river basin: Journal of Geophysical Research: Earth Surface, v. 124, no. 4, p. 902-919, https://doi.org/10.1029/2018JF004734.","productDescription":"18 p.","startPage":"902","endPage":"919","ipdsId":"IP-097291","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":362211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nooksack River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.64312744140624,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.499317631540286\n ],\n [\n -121.453857421875,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.9991410647952\n ],\n [\n -122.64312744140624,\n 48.499317631540286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":"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,
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).
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.
","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":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
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.
","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":70202756,"text":"70202756 - 2019 - Better approaches to managing drought in the American Southwest","interactions":[],"lastModifiedDate":"2019-04-01T15:57:28","indexId":"70202756","displayToPublicDate":"2019-03-20T12:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1578,"text":"Eos, Transactions, American Geophysical Union","onlineIssn":"2324-9250","printIssn":"0096-394","active":true,"publicationSubtype":{"id":10}},"title":"Better approaches to managing drought in the American Southwest","docAbstract":"The second in a series of USGS Southwest Region (SWR) “Science Exchange” annual workshops, focused on USGS drought science. The participants considered how extreme drought conditions are evolving in much of the American southwest, with an emphasis on integrated drought science planning at the USGS bureau and program levels. The increased need for interdisciplinary science to support resource-management decisions systems, was highlighted. \nThe workshop brought together scientists and program managers from USGS with Bureau of Land Management, National Oceanic and Atmospheric Administration, US Bureau of Reclamation and state water management departments. Key objectives of the workshop were to improve awareness of ongoing drought-science work within the region, highlight the capabilities of USGS-SWR science centers in drought science, and build new relationships to advance best approaches in drought science. Topics covered in presentations and demonstrations were broad-ranging and included monitoring for drought early warning; water use and water production associated with petroleum production; paleo perspectives on drought, and ecological consequences of drought to native fish.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019EO118533","usgsCitation":"Lambert, P., Titus, T.N., and Ostroff, A., 2019, Better approaches to managing drought in the American Southwest: Eos, Transactions, American Geophysical Union, v. 100, HTML Document, https://doi.org/10.1029/2019EO118533.","productDescription":"HTML Document","ipdsId":"IP-102913","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019eo118533","text":"Publisher Index Page"},{"id":362627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lambert, Patrick 0000-0001-6808-2303 plambert@usgs.gov","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":214412,"corporation":false,"usgs":true,"family":"Lambert","given":"Patrick","email":"plambert@usgs.gov","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":759840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":759839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostroff, Andrea","contributorId":214413,"corporation":false,"usgs":true,"family":"Ostroff","given":"Andrea","email":"","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":759841,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":"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.
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.
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).
","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":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Sex determination is the first step toward the establishment of phenotypic sex in most vertebrates. Aquatic poikilotherms such as teleost fishes exhibit a high diversity of sex-determination mechanisms and gonadal phenotypes that are remarkably plastic and responsive to a variety of environmental factors (e.g., water temperature, pH, salinity, photoperiod, population density). This chapter reviews current knowledge of genotypic and environmental sex determination systems in fishes with special reference to Atheriniformes—one of the best-characterized taxa in this field—and offers perspectives to guide and stimulate further research.
","language":"English","publisher":"Elsevier","doi":"10.1016/bs.ctdb.2019.02.003","usgsCitation":"Yamamoto, Y., Hattori, R.S., Patino, R., and Strüssmann, C., 2019, Environmental regulation of sex determination in fishes: Insights from Atheriniformes, p. 49-69, https://doi.org/10.1016/bs.ctdb.2019.02.003.","productDescription":"21 p.","startPage":"49","endPage":"69","ipdsId":"IP-102625","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yamamoto, Y.","contributorId":264653,"corporation":false,"usgs":false,"family":"Yamamoto","given":"Y.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hattori, R. S.","contributorId":264656,"corporation":false,"usgs":false,"family":"Hattori","given":"R.","email":"","middleInitial":"S.","affiliations":[{"id":54527,"text":"Sao Paulo Fisheries Institute","active":true,"usgs":false}],"preferred":false,"id":821814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strüssmann, C. A.","contributorId":264657,"corporation":false,"usgs":false,"family":"Strüssmann","given":"C. A.","affiliations":[{"id":47624,"text":"Tokyo University of Marine Science and Technology","active":true,"usgs":false}],"preferred":false,"id":821816,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215766,"text":"70215766 - 2019 - Growth disparity in sympatric kokanee breeding groups","interactions":[],"lastModifiedDate":"2020-10-30T11:55:18.644759","indexId":"70215766","displayToPublicDate":"2019-03-20T06:48:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2885,"text":"North American Journal of Aquaculture","active":true,"publicationSubtype":{"id":10}},"title":"Growth disparity in sympatric kokanee breeding groups","docAbstract":"Growth is arguably the most important dynamic rate function due to its interaction with survival and recruitment. As such, understanding the mechanisms underlying growth is a primary focus of fisheries research. Kokanee Oncorhynchus nerka in Lake Pend Oreille, Idaho, provide an interesting case study for investigating the factors that influence growth. Early‐run and late‐run kokanee occur in Lake Pend Oreille, but early‐run fish generally grow faster than late‐run fish. The observed growth disparity between early‐ and late‐run fish could be due to genetic differences between the two groups. Conversely, a common hatchery practice of slowing growth by reducing feed has been hypothesized to elicit a compensatory growth response in early‐run fish and to explain the size difference between breeding groups. Using two different experiments, we tested the hypotheses that (1) early‐run kokanee are genetically disposed to grow faster than late‐run kokanee at identical water temperatures; and (2) feed restriction elicits a compensatory growth response in early‐run kokanee that explains the observed size difference between breeding groups. Estimates of mean FL, weight, Fulton's condition factor (K), and specific growth rate (SGR) were not significantly different (P ≥ 0.05) between early‐run and late‐run fish in the first experiment. However, water temperature was positively related to mean FL, weight, K, and SGR for both breeding groups. Fish that were subjected to food deprivation exhibited an increased growth rate and obtained weights similar to those of control fish. Overall, our results suggest that early‐ and late‐run fish have similar growth potential, but certain hatchery practices likely provide early‐run fish with an initial advantage in growth, size, or both.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/naaq.10084","usgsCitation":"Klein, Z.B., Quist, M.C., Dux, A.M., and Corsi, M.P., 2019, Growth disparity in sympatric kokanee breeding groups: North American Journal of Aquaculture, v. 81, no. 2, p. 169-177, https://doi.org/10.1002/naaq.10084.","productDescription":"9 p.","startPage":"169","endPage":"177","ipdsId":"IP-103305","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2499838","text":"External Repository"},{"id":379957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.70501708984376,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 47.91450120703987\n ],\n [\n -116.13372802734375,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 48.323386716330916\n ],\n [\n -116.70501708984376,\n 47.91450120703987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":803345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Matthew P.","contributorId":212797,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":803347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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":"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 Ursus maritimus 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 (r2 = 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.
","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}]}} ]}