Fog and low cloud cover (FLCC) and late summer recharge increase stream baseflow and decrease stream temperature during arid Mediterranean climate summers, which benefits salmon especially under climate warming conditions. The potential to discharge cool water to streams during the late summer (hydrologic capacity; HC) furnished by FLCC and recharge were mapped for the 299 subwatersheds ranked Core, Phase 1, or Phase 2 under the National Marine Fisheries Service Recovery Plan that prioritized restoration and threat abatement action for endangered Central California Coast Coho Salmon evolutionarily significant unit. Two spatially continuous gridded datasets were merged to compare HC: average hrs/day FLCC, a new dataset derived from a decade of hourly National Weather Satellite data, and annual average mm recharge from the USGS Basin Characterization Model. Two use‐case scenarios provide examples of incorporating FLCC‐driven HC indices into long‐term recovery planning. The first, a thermal analysis under future climate, projected 65% of the watershed area for 8–19 coho population units as thermally inhospitable under two global climate models and identified several units with high resilience (high HC under the range of projected warming conditions). The second use case investigated HC by subwatershed rank and coho population, and identified three population units with high HC in areas ranked Phase 1 and 2 and low HC in Core. Recovery planning for cold‐water fish species would benefit by including FLCC in vulnerability analyses.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12811","usgsCitation":"Torregrosa, A.A., Flint, L.E., and Flint, A.L., 2020, Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning: Journal of the American Water Resources Association, v. 56, no. 1, p. 134-160, https://doi.org/10.1111/1752-1688.12811.","productDescription":"27 p.","startPage":"134","endPage":"160","ipdsId":"IP-095384","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458480,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12811","text":"Publisher Index Page"},{"id":372761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.18945312500001,\n 41.96765920367816\n ],\n [\n -128.0126953125,\n 38.39333888832238\n ],\n [\n -122.9150390625,\n 34.08906131584994\n ],\n [\n -117.79541015625001,\n 36.82687474287728\n ],\n [\n -124.18945312500001,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783457,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70207299,"text":"70207299 - 2020 - Controls on sediment distribution in the coastal zone of the central California transform continental margin, USA","interactions":[],"lastModifiedDate":"2019-12-19T14:58:38","indexId":"70207299","displayToPublicDate":"2019-11-19T19:50:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Controls on sediment distribution in the coastal zone of the central California transform continental margin, USA","docAbstract":"We use >10,000 km of high-resolution seismic-reflection data together with multibeam bathymetry to document complex and highly variable post-Last Glacial Maximum (LGM) sediment distribution and thickness in the coastal zone (~10 m isobath to 5.6 km offshore) along a ~800 km section of central California's transform continental margin. Sediment thickness ranges from 0 (seafloor bedrock) to 64 m with a mean of 8.7 m. We delineate 25 coastal zone “sediment domains,” and group them based on common geomorphology and sediment occurrence. Thickest sediment occurs in “mountain front” and “large river” domains, which comprise 14.5% and 7.9% of the coastal zone and contain 30.1% and 18.2% of coastal zone sediment, respectively. In contrast, “small river” domains and “sediment-poor shelf” domains comprise 50.7% and 15.7% of the coastal zone and contain 18.4% and 12.7% of its sediment.
The distribution and thickness of post-LGM sediment in the coastal zone is controlled by a combination of tectonics, sediment supply, and eustasy. Sediment is derived from a tectonically controlled coastal landscape of rapidly uplifting mountain fronts, more slowly uplifting marine terraces, and fault-bounded headlands and alluvial-estuarine troughs. Sediment supply is maximized along steep, landslide-prone, mountain fronts and at the mouths of large watersheds, and minimized along lower-relief, terraced coastal landscape drained by smaller rivers and creeks. In the offshore coastal zone, tectonics generates local uplifts and basins, and influences shelf width and gradient as well as the locations of some shelf-incised submarine canyons. Sea-level rise raises base level, drowns estuaries, creates accommodation space on the shelf (amount based on gradient), and isolates the heads of many submarine canyons at or near the shelfbreak. Comparison of shelf sediment volumes with estimates of “unaltered” watershed sediment supply reveals that a relatively small proportion of post-LGM sediment supply is preserved on the shelf offshore of some of the largest rivers. Sediments deposited in shoreline and shelf environments have limited preservation potential, and the most complete long-term geologic record of the post-LGM transgression and highstand is likely represented in slope and submarine fan deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2019.106085","usgsCitation":"Johnson, S., Beeson, J.W., Watt, J., Sliter, R., and Papesh, A., 2020, Controls on sediment distribution in the coastal zone of the central California transform continental margin, USA: Marine Geology, v. 420, 106085, 29 p., https://doi.org/10.1016/j.margeo.2019.106085.","productDescription":"106085, 29 p.","ipdsId":"IP-110134","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458490,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2019.106085","text":"Publisher Index Page"},{"id":370327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.93652343749999,\n 33.43144133557529\n ],\n [\n -119.794921875,\n 33.43144133557529\n ],\n [\n -119.794921875,\n 40.212440718286466\n ],\n [\n -124.93652343749999,\n 40.212440718286466\n ],\n [\n -124.93652343749999,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"420","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beeson, Jeffrey W. 0000-0002-7396-237X","orcid":"https://orcid.org/0000-0002-7396-237X","contributorId":194964,"corporation":false,"usgs":false,"family":"Beeson","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":777609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":221271,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sliter, Ray 0000-0003-0337-3454","orcid":"https://orcid.org/0000-0003-0337-3454","contributorId":221272,"corporation":false,"usgs":true,"family":"Sliter","given":"Ray","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":777611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Papesh, Antoinette 0000-0002-1704-0557","orcid":"https://orcid.org/0000-0002-1704-0557","contributorId":221273,"corporation":false,"usgs":false,"family":"Papesh","given":"Antoinette","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":777612,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206961,"text":"70206961 - 2020 - Using integrated population models for insights into monitoring programs: An application using pink-footed geese","interactions":[],"lastModifiedDate":"2019-12-03T06:43:13","indexId":"70206961","displayToPublicDate":"2019-11-19T11:43:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Using integrated population models for insights into monitoring programs: An application using pink-footed geese","docAbstract":"Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose (Anser brachyrhynchus) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2019.108869","usgsCitation":"Johnson, F., Zimmerman, G.S., Jensen, G.H., Clausen, K.K., Frederiksen, M., and Madsen, J., 2020, Using integrated population models for insights into monitoring programs: An application using pink-footed geese: Ecological Modelling, v. 415, 108869, 13 p., https://doi.org/10.1016/j.ecolmodel.2019.108869.","productDescription":"108869, 13 p.","ipdsId":"IP-107877","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437202,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P901K3RP","text":"USGS data release","linkHelpText":"Demographic parameters for Svalbard pink-footed geese, 1991-2018"},{"id":369802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"415","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Fred 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":220964,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":776392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":776393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jensen, Gitte H.","contributorId":220965,"corporation":false,"usgs":false,"family":"Jensen","given":"Gitte","email":"","middleInitial":"H.","affiliations":[{"id":13685,"text":"Aarhus University, Department of Bioscience","active":true,"usgs":false}],"preferred":false,"id":776394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clausen, Kevin K.","contributorId":174355,"corporation":false,"usgs":false,"family":"Clausen","given":"Kevin","email":"","middleInitial":"K.","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":776395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frederiksen, Morten","contributorId":217509,"corporation":false,"usgs":false,"family":"Frederiksen","given":"Morten","email":"","affiliations":[{"id":13685,"text":"Aarhus University, Department of Bioscience","active":true,"usgs":false}],"preferred":false,"id":776396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":776397,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227078,"text":"70227078 - 2020 - RAD-seq refines previous estimates of genetic structure in Lake Erie walleye","interactions":[],"lastModifiedDate":"2021-12-29T15:43:25.641325","indexId":"70227078","displayToPublicDate":"2019-11-19T09:36:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"RAD-seq refines previous estimates of genetic structure in Lake Erie walleye","docAbstract":"Delineating population structure helps fishery managers to maintain a diverse “portfolio” of local spawning populations (stocks), as well as facilitate stock-specific management. In Lake Erie, commercial and recreational fisheries for Walleye Sander vitreus exploit numerous local spawning populations, which cannot be easily differentiated using traditional genetic data (e.g., microsatellites). Here, we used genomic information (12,264 polymorphic loci) generated using restriction site-associated DNA sequencing to investigate stock structure in Lake Erie Walleye. We found low genetic divergence (genetic differentiation index FST = 0.0006–0.0019) among the four Lake Erie western basin stocks examined, which resulted in low classification accuracies for individual samples (40–60%). However, more structure existed between western and eastern Lake Erie basin stocks (FST = 0.0042–0.0064), resulting in greater than 95% classification accuracy of samples to a lake basin. Thus, our success in using genomics to identify stock structure varied with spatial scale. Based on our results, we offer suggestions to improve the efficacy of this new genetic tool for refining stock structure and eventually determining relative stock contributions in Lake Erie Walleye and other Great Lakes populations.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10215","usgsCitation":"Chen, K., Euclide, P., Ludsin, S., Larson, W., Sovic, M.G., Gibbs, H.L., and Marschall, E., 2020, RAD-seq refines previous estimates of genetic structure in Lake Erie walleye: Transactions of the American Fisheries Society, v. 149, no. 20, p. 159-173, https://doi.org/10.1002/tafs.10215.","productDescription":"15 p.","startPage":"159","endPage":"173","ipdsId":"IP-107069","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":393592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, New York, Ohio, Ontario, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.72802734375,\n 42.84375132629021\n ],\n [\n -81.34277343749999,\n 42.69858589169842\n ],\n [\n -83.21044921875,\n 42.5530802889558\n ],\n [\n -83.583984375,\n 42.01665183556825\n ],\n [\n -83.73779296875,\n 41.541477666790286\n ],\n [\n -82.63916015625,\n 41.16211393939692\n ],\n [\n -80.96923828125,\n 41.47566020027821\n ],\n [\n -79.716796875,\n 42.06560675405716\n ],\n [\n -78.72802734375,\n 42.84375132629021\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"20","noUsgsAuthors":false,"publicationDate":"2020-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Kuan-Yu","contributorId":270528,"corporation":false,"usgs":false,"family":"Chen","given":"Kuan-Yu","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Euclide, Peter T.","contributorId":270530,"corporation":false,"usgs":false,"family":"Euclide","given":"Peter T.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":829536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ludsin, Stuart A.","contributorId":270532,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":829534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sovic, Michael G.","contributorId":270534,"corporation":false,"usgs":false,"family":"Sovic","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829538,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibbs, H. Lisle","contributorId":270536,"corporation":false,"usgs":false,"family":"Gibbs","given":"H.","email":"","middleInitial":"Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829539,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marschall, Elizabeth A.","contributorId":270538,"corporation":false,"usgs":false,"family":"Marschall","given":"Elizabeth A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":829540,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236239,"text":"70236239 - 2020 - Directivity of M 3.1 earthquake near Anza, California and the effect on peak ground motion","interactions":[],"lastModifiedDate":"2022-08-31T14:19:50.242428","indexId":"70236239","displayToPublicDate":"2019-11-19T09:14:00","publicationYear":"2020","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":"Directivity of M 3.1 earthquake near Anza, California and the effect on peak ground motion","docAbstract":"We show the effect of rupture directivity on peak ground‐motion values for a moderate magnitude event at Anza, California, and neighboring stations at the Imperial Valley. The event was located near Borrego Springs on the west side of the Salton Sea and was well recorded at broadband stations near Anza, California, and at stations on the west side of the Imperial Valley. After correcting for regional attenuation, an anomalously large residual in peak motion was observed at station ERR just to the southeast of the epicenter. Using the algorithm from Boatwright (2007), peak motions from the regional seismic networks in southern California were inverted to determine directivity, which was to the southeast along the trend of the San Jacinto fault toward station ERR. This algorithm uses peak values compiled for the ShakeMap system mostly at regional distances. It does not capture the main features of the source time function (STF) predicted by directivity. Consequently, we determined the second‐degree moments for this earthquake, which confirmed that station ERR has a shorter and higher STF compared to stations to the northwest suggesting rupture propagated to the southeast. The azimuthal distribution of local stations is sparse, but nevertheless the largest amplitudes (such as at station ERR) correlate well with the maximum in the radiation pattern and smaller values with the minima, which is the radiation pattern for SH plus the effect of directivity. Using the data from the analysis of the second‐degree moments, the characteristic length of the fault is 0.58 km, assuming an idealized unilateral extended rupture with a rupture time of 0.09 s. This yields an apparent rupture velocity of
We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N. We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X. We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P(X=0)>0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.
Microplastics are an ecological stressor with implications for ecosystem and human health when present in seafood. We quantified microplastic types, concentrations, anatomical burdens, geographic distribution, and temporal differences in Pacific oysters (Crassostrea gigas) and Pacific razor clams (Siliqua patula) from 15 Oregon coast, U.S.A. sites. Microplastics were present in organisms from all sites. On average, whole oysters and razor clams contained 10.95 ± 0.77 and 8.84 ± 0.45 microplastic pieces per individual, or 0.35 ± 0.04 pieces g−1 tissue and 0.16 ± 0.02 pieces g−1 tissue, respectively. Contamination was quantified but not subtracted. Over 99% of microplastics were fibers. Material type was determined using Fourier‐transform infrared spectroscopy. Spring samples contained more microplastics than summer samples in oysters but not razor clams. Our study is the first to document microplastics in Pacific razor clams and provides important coast‐wide data to compare microplastic burden across species, seasons, and sites.
","language":"English","publisher":"Wiley","doi":"10.1002/lol2.10124","usgsCitation":"Baechler, B., Granek, E.F., Hunter, M.G., and Conn, K., 2020, Microplastic concentrations in two Oregon bivalve species: Spatial, temporal, and species variability: Limnology and Oceanography Letters, v. 5, no. 1, p. 54-65, https://doi.org/10.1002/lol2.10124.","productDescription":"12 p.","startPage":"54","endPage":"65","ipdsId":"IP-110433","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":458522,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10124","text":"Publisher Index Page"},{"id":370881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"5","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Baechler, Britta","contributorId":221557,"corporation":false,"usgs":false,"family":"Baechler","given":"Britta","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":778656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granek, Elise F.","contributorId":176630,"corporation":false,"usgs":false,"family":"Granek","given":"Elise","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":778657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Matthew G.","contributorId":146866,"corporation":false,"usgs":false,"family":"Hunter","given":"Matthew","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":778658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conn, Kathleen E. 0000-0002-2334-6536 kconn@usgs.gov","orcid":"https://orcid.org/0000-0002-2334-6536","contributorId":3923,"corporation":false,"usgs":true,"family":"Conn","given":"Kathleen E.","email":"kconn@usgs.gov","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778655,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212613,"text":"70212613 - 2020 - Classification of oil spill by thicknesses using multiple remote sensors","interactions":[],"lastModifiedDate":"2020-08-24T14:12:42.526677","indexId":"70212613","displayToPublicDate":"2019-11-09T09:09:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Classification of oil spill by thicknesses using multiple remote sensors","docAbstract":"Satellite Synthetic Aperture Radar (SAR) is an operational tool for monitoring and assessment of oil spills. Satellite SAR has primarily been used to detect the presence/absence of oil, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents methods for the rapid classification of oil types and estimated thicknesses, from which information about thick oil and oil emulsions (i.e., “actionable” oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions, a single polarization satellite SAR image can record a signal variance between thick stable emulsions and non-emulsified oil. During a series of field campaigns in the Gulf of Mexico with in situ measurements of oil thickness, multiple satellite data were obtained including fully polarimetric C-band SAR imagery from RADARSAT-2 and multispectral imagery from ASTER and WorldView-2. One campaign included the airborne polarimetric UAVSAR L-band sensor. An oil/emulsion thickness classification product was generated based on RADARSAT-2 polarimetric imagery using entropy and the damping ratio derivations. Herein, we present the classification methods to generate oil thickness products from SAR, validated by sea-truth observations, the multispectral imagery, and the UAVSAR data. We tested the ability to deliver these products with minimum latency to responding vessels via NOAA. During field operations in the Gulf of Mexico, a satellite SAR-based product of oil delineation by relative thickness was delivered to a responding vessel 42 min after the RADARSAT-2 data acquisition. This proof-of-concept test using satellite SAR and multispectral imagery to detect emulsions and deliver a derived information product to a vessel in near-real-time points directly to methods for satellite-based assets to be used in the near future for oil spill tactical response operations.
Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long-term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density-regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska.
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A common change-point technique is the Pettitt test; however, many change-point methods are now available and testing of methods has been limited. This study investigated eight methods for detecting change points in the location (central tendency, seven methods) and scale (dispersion or spread, one method) of annual peak streamflows, using simulated data with and without change points, and peak-streamflow series from basins with known large additions of reservoir storage. Parametric methods tested, including a Bayesian one, did not perform well, even when transforming peak streamflows to approximate normality by using logarithms. Nonparametric methods other than the Pettitt test allow for more than one change point but have an unacceptable number of false positives. Based on the results of our methods comparisons, we used the Pettitt and the Mood tests to find change points in location and scale, respectively, in thousands of streamgage records in the conterminous United States. Change points in location (median) and scale are abundant, with the changes in median peak streamflow showing regional patterns, as well as a strong increased streamflow signal around 1970. The changes in scale of peak streamflows are dominated more by temporal than spatial patterns; more streamgages had decreases in scale in earlier decades than recent decades and more streamgages had increases in scale occurring in recent decades than earlier decades.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124307","collaboration":"","usgsCitation":"Ryberg, K.R., Hodgkins, G.A., and Dudley, R., 2020, Change points in annual peak streamflows: Method comparisons and historical change points in the United States: Journal of Hydrology, v. 583, https://doi.org/10.1016/j.jhydrol.2019.124307.","productDescription":"124307, 13 p.","startPage":"","ipdsId":"IP-098428","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":373948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": 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Center","active":true,"usgs":true}],"preferred":true,"id":786809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786811,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209469,"text":"70209469 - 2020 - Occupancy Patterns of Breeding American Black Ducks","interactions":[],"lastModifiedDate":"2020-04-09T18:27:45.620736","indexId":"70209469","displayToPublicDate":"2019-10-29T13:15:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy Patterns of Breeding American Black Ducks","docAbstract":"Occupancy patterns can assist with the determination of habitat limitation during breeding or wintering periods and can help guide population and habitat management efforts. American black ducks (Anas rubripes; black ducks) are thought to be limited by habitat and food availability during the winter, but breeding sites may also limit the size or growth potential of the population. The Canadian Wildlife Service conducts an annual breeding waterfowl survey that we used to explore the hypothesis that black duck carrying capacity is limited by wetlands available for breeding in Québec, Canada. We applied single‐visit, multi‐species occupancy models to the 1990–2015 population survey data to determine if there was evidence the black duck population was limited by breeding habitat. Using a dynamic (multi‐season) occupancy modeling approach, we estimated latent occupancy (occupancy accounting for imperfect detection) of black ducks and then used latent occupancy estimates to derive occupancy, colonization, and extirpation rates. We jointly modeled the occupancy dynamics of black ducks and other duck species in wetlands where both species were present. Throughout the duration of the survey, 44% of wetlands were never observed to be occupied by black ducks. Occupancy models showed wetland size was positively associated with occupancy at the first time step (initial occupancy) and colonization. All 2‐species models indicated initial black duck occupancy, persistence (continued occupancy), and colonization were positively associated with the presence of a second species. Colonization rate over the 26‐year period ranged from 7% to 27% across all models. Extirpation rates were similar and were constant through time within each model. Low occupancy rates, combined with approximately equal colonization and extirpation rates, suggest there are available wetlands for breeding black ducks in their core breeding area. If breeding habitats are not saturated, this suggests migration or wintering areas may be more limiting to black duck population abundance.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21775","usgsCitation":"Roberts, A.J., Royle, J.A., Padding, P.I., Devers, P.K., Lepage, C., and Bordage, D., 2020, Occupancy Patterns of Breeding American Black Ducks: Journal of Wildlife Management, v. 84, no. 1, p. 150-160, https://doi.org/10.1002/jwmg.21775.","productDescription":"11 p.","startPage":"150","endPage":"160","ipdsId":"IP-109082","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":373864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario, Quebec","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -56.9091796875,\n 51.34433866059924\n ],\n [\n -57.041015625,\n 52.07950600379697\n ],\n [\n -63.80859374999999,\n 52.07950600379697\n ],\n [\n -63.28125,\n 52.802761415419674\n ],\n [\n -64.3359375,\n 52.802761415419674\n ],\n [\n -65.126953125,\n 51.944264879028765\n ],\n [\n -67.32421875,\n 52.9883372533954\n ],\n [\n -67.1044921875,\n 54.95238569063361\n ],\n [\n -82.3974609375,\n 54.316523240258256\n ],\n [\n -82.177734375,\n 45.30580259943578\n ],\n [\n -74.8828125,\n 45.1510532655634\n ],\n [\n -73.1689453125,\n 45.02695045318546\n ],\n [\n -71.279296875,\n 45.1510532655634\n ],\n [\n -69.43359375,\n 47.42808726171425\n ],\n [\n -68.994140625,\n 47.42808726171425\n ],\n [\n -68.90625,\n 47.15984001304432\n ],\n [\n -61.52343749999999,\n 49.1242192485914\n ],\n [\n -56.9091796875,\n 51.34433866059924\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Anthony J.","contributorId":191131,"corporation":false,"usgs":false,"family":"Roberts","given":"Anthony","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":786634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":786635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Padding, Paul I.","contributorId":38411,"corporation":false,"usgs":true,"family":"Padding","given":"Paul","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":786636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":786637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lepage, Christine","contributorId":194564,"corporation":false,"usgs":false,"family":"Lepage","given":"Christine","email":"","affiliations":[],"preferred":false,"id":786638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bordage, Daniel","contributorId":223924,"corporation":false,"usgs":false,"family":"Bordage","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":786639,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227266,"text":"70227266 - 2020 - Population ecology and evaluation of suppression scenarios for an introduced Utah Chub population","interactions":[],"lastModifiedDate":"2022-01-06T15:10:34.376376","indexId":"70227266","displayToPublicDate":"2019-10-29T09:03:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Population ecology and evaluation of suppression scenarios for an introduced Utah Chub population","docAbstract":"Introduced Utah Chub Gila atraria were first sampled in Henrys Lake, Idaho, in 1993, and their presence in the system is a concern given possible interactions with sport fishes. Our objective was to describe the population dynamics of Utah Chub in Henrys Lake. A total of 362 Utah Chub was sampled via gill nets, with an average catch rate of 20.5 fish/net-night (SE = 6.0) during May 2016. Average TL was 210 mm (SE = 3), and average weight was 134 g (SE = 5). Pectoral fin rays were used to provide estimates of growth and age structure. Utah Chub varied in age from 2 to 12 years, and recruitment was stable (recruitment coefficient of determination = 0.96). Estimated total annual mortality was 40% (SE = 4%). Fecundity of Utah Chub in Henrys Lake increased with length and varied from 6,232 to 156,797 eggs/female. Age-structured population models were constructed using the demographics data, and estimated average population growth rate over a 10-year period was 1.17. This study provides a comprehensive description of Utah Chub population dynamics and insight on their management in systems where they are not native. This information is not only useful for guiding management actions but also serves to further our understanding of Utah Chub ecology.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10385","usgsCitation":"Roth, C.J., Beard, Z.S., Flinders, J.M., and Quist, M.C., 2020, Population ecology and evaluation of suppression scenarios for an introduced Utah Chub population: North American Journal of Fisheries Management, v. 40, no. 1, p. 133-144, https://doi.org/10.1002/nafm.10385.","productDescription":"12 p.","startPage":"133","endPage":"144","ipdsId":"IP-107852","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":393958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Henrys Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.45011901855469,\n 44.60537945347679\n ],\n [\n -111.35673522949219,\n 44.60537945347679\n ],\n [\n -111.35673522949219,\n 44.67402426917907\n ],\n [\n -111.45011901855469,\n 44.67402426917907\n ],\n [\n -111.45011901855469,\n 44.60537945347679\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Roth, Curtis J.","contributorId":204937,"corporation":false,"usgs":false,"family":"Roth","given":"Curtis","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":830200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Zachary S.","contributorId":198840,"corporation":false,"usgs":false,"family":"Beard","given":"Zachary","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":830201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flinders, Jonathan M","contributorId":270950,"corporation":false,"usgs":false,"family":"Flinders","given":"Jonathan","email":"","middleInitial":"M","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":830202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":830203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249754,"text":"70249754 - 2020 - Seasonal cycles in hematology and body mass in free-ranging gray wolves (Canis lupus) from northeastern Minnesota, USA","interactions":[],"lastModifiedDate":"2023-10-26T12:21:29.183031","indexId":"70249754","displayToPublicDate":"2019-10-26T07:19:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal cycles in hematology and body mass in free-ranging gray wolves (Canis lupus) from northeastern Minnesota, USA","docAbstract":"Studies of captive gray wolves (Canis lupus) showed seasonal cycles in hematologic values and female body mass. We used a remotely controlled recapture collar to determine whether nine female and five male free-ranging wolves handled four to 17 times in NE Minnesota, US showed similar cycles. Hematocrit, hemoglobin, red blood cell count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and body mass increased from summer toward a winter peak and then decreased again toward summer. Several hematologic values differed considerably from those of captive wolves, and the ranges in free-ranging wolves were much greater than those of captives.
Effects of ambient decreases in N deposition on forest N cycling remain unclear as soils recover from acidic deposition. To investigate, repeated soil sampling data were related to deposition, vegetation, and stream data, for 2000–2015 in North and South Buck Creek watersheds, in the Adirondack region of New York, USA. In 63 other Adirondack streams, NO3− concentrations were also compared between 2004–2005 and 2014–2015, and a link between soil calcium and stream NO3− was investigated using data from 387 Adirondack streams that were sampled in either 2003–2005 or 2010–2011. No trends in N export or NO3− concentrations were observed in either Buck watershed despite a 45% decrease in N deposition, although South Buck N export was 2 to 3 times higher than in North Buck, where 48% of deposited N was accounted for by accumulation in the upper soil. In marked contrast, the upper profile in South Buck showed a net loss of N. Increased decomposition appeared likely in South Buck as those soils are adjusted to lower levels of acidifying S deposition, whereas decomposition increases in North Buck were likely suppressed by high levels of natural organic acidity. Stream NO3− concentrations in Buck watersheds bracketed regional results and were consistent with the regional streams that showed no overall change in NO3− concentrations between 2004 and 2014. A negative correlation observed between NO3− concentration and watershed buffering capacity expressed as the ratio of Ca2+ to SO42− also suggested that stream NO3− concentrations were elevated where soil Ca depletion had occurred.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JG005036","usgsCitation":"Lawrence, G.B., Scanga, S.E., and Sabo, R.D., 2020, Recovery of soils from acidic deposition may exacerbate nitrogen export from forested watersheds: JGR: Biogeosciences, v. 125, no. 1, e2019JG005036, 18 p., https://doi.org/10.1029/2019JG005036.","productDescription":"e2019JG005036, 18 p.","ipdsId":"IP-098501","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":458577,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jg005036","text":"Publisher Index Page"},{"id":383143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scanga, Sara E. 0000-0003-4022-4167","orcid":"https://orcid.org/0000-0003-4022-4167","contributorId":178227,"corporation":false,"usgs":false,"family":"Scanga","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":28019,"text":"Deptartment of Biology, Utica College","active":true,"usgs":false}],"preferred":false,"id":810062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sabo, Robert D. 0000-0001-8713-7699","orcid":"https://orcid.org/0000-0001-8713-7699","contributorId":178226,"corporation":false,"usgs":false,"family":"Sabo","given":"Robert","email":"","middleInitial":"D.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":810063,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208058,"text":"70208058 - 2020 - Plate boundary localization, slip-rates and rupture segmentation of the Queen Charlotte Fault based on submarine tectonic geomorphology","interactions":[],"lastModifiedDate":"2023-11-08T16:57:08.69022","indexId":"70208058","displayToPublicDate":"2019-10-23T07:00:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Plate boundary localization, slip-rates and rupture segmentation of the Queen Charlotte Fault based on submarine tectonic geomorphology","docAbstract":"Linking fault behavior over many earthquake cycles to individual earthquake behavior is a primary goal in tectonic geomorphology, particularly across an entire plate boundary. Here, we examine the 1150-km-long, right-lateral Queen Charlotte-Fairweather fault system using comprehensive multibeam bathymetry data acquired along the Queen Charlotte Fault (QCF) offshore southeastern Alaska and western British Columbia. Fine-scale analysis of tectonic geomorphology allowed us to identify and reconstruct 184 strike-slip piercing points over a 630 km stretch of the QCF. Age constraints from glacial recession and offshore sedimentation patterns yield a consistent slip-rate of ∼50–57 mm/yr since ∼17–12 ka, the fastest rate for a continent-ocean strike-slip fault on Earth. These slip-rates equal or exceed estimates of Pacific-North America (PA-NA) relative motion from global plate reconstructions, indicating that PA-NA motion is highly localized. The QCF cuts the seafloor along a narrow and unusually straight trace for its entire length and multiple fault traces are observed only at local step-overs. The geometry and behavior of the QCF over many earthquake cycles is simple and typical of mature faults with relatively homogeneous stress fields. Since the QCF is the primary PA-NA plate boundary, we used the trace of the QCF to define the small circle path for relative plate motion and computed the associated Euler pole. Predicted along-strike obliquity variations based on the new pole agree with observed tectonic geomorphology and suggest that previous global plate reconstructions overestimated the degree of oblique convergence along the QCF. We also find that subtle, long-wavelength (75–150 km) bends and discrete step-overs appear to define the endpoints of M>7 earthquakes, suggesting that obliquity and resultant fault geometry may control rupture segmentation and asperity development. Lastly, the agreement between predicted obliquity and tectonic geomorphology along the entire length of QCF compelled a reevaluation of regional tectonic models. In the north, the eastern Yakatat Terrane appears to be translating northwest with the Pacific plate, and slip transferred from the QCF to the Fairweather Fault results in ∼20 mm/yr of convergence along the southern St. Elias mountains. In the south, we predict a reduced rate of convergence along the QCF west of Haida Gwaii (∼5–6 mm/yr of shortening, on average) relative to previous studies. Our results support a model for transpression and strike-slip partitioning along the edge of a hot and weak Pacific Plate, leading to crustal thickening and growth of the Queen Charlotte Terrace to the west of Haida Gwaii.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.115882","usgsCitation":"Brothers, D.S., Miller, N.C., Barrie, V., Haeussler, P., Greene, H.G., Andrews, B.D., Zielke, O., and Dartnell, P., 2020, Plate boundary localization, slip-rates and rupture segmentation of the Queen Charlotte Fault based on submarine tectonic geomorphology: Earth and Planetary Science Letters, no. 530, 115882, 16 p., https://doi.org/10.1016/j.epsl.2019.115882.","productDescription":"115882, 16 p.","ipdsId":"IP-112239","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458583,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2019.115882","text":"Publisher Index Page"},{"id":371553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, British Columbia","otherGeospatial":"Queen Charlotte fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -133.4951629472878,\n 51.22355291983479\n ],\n [\n -128.93798737425547,\n 52.061072194022785\n ],\n [\n -134.9579573372683,\n 59.85011582268859\n ],\n [\n -142.21165991313777,\n 60.39645421234209\n ],\n [\n -133.4951629472878,\n 51.22355291983479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"530","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":221807,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barrie, Vaughn 0000-0001-9742-4325","orcid":"https://orcid.org/0000-0001-9742-4325","contributorId":221808,"corporation":false,"usgs":false,"family":"Barrie","given":"Vaughn","email":"","affiliations":[{"id":40433,"text":"NRCAN","active":true,"usgs":false}],"preferred":false,"id":780297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":780298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Greene, H. Gary","contributorId":208568,"corporation":false,"usgs":false,"family":"Greene","given":"H.","email":"","middleInitial":"Gary","affiliations":[{"id":6751,"text":"Moss Landing Marine Laboratories","active":true,"usgs":false}],"preferred":false,"id":780299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrews, Brian D. 0000-0003-1024-9400 bandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-1024-9400","contributorId":201662,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian","email":"bandrews@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780300,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zielke, Olaf 0000-0002-4797-0034","orcid":"https://orcid.org/0000-0002-4797-0034","contributorId":221809,"corporation":false,"usgs":false,"family":"Zielke","given":"Olaf","email":"","affiliations":[{"id":24561,"text":"KAUST","active":true,"usgs":false}],"preferred":false,"id":780301,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dartnell, Peter 0000-0002-9554-729X","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":208208,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780302,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224286,"text":"70224286 - 2020 - Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds","interactions":[],"lastModifiedDate":"2021-09-20T12:56:45.40967","indexId":"70224286","displayToPublicDate":"2019-10-21T07:55:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds","docAbstract":"Predicting distributions is fundamental to ecology, yet hindered by spatially restricted sampling, scale-dependent relationships and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.
USA.
We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale optimization, and species-specific detectability for a suite of wide-ranging species.
Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban and hydrologic disturbances operating from local scales (100–500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at >1 scale. Five species showed evidence for continental-scale range contraction during the study.
We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broadscale drivers of occupancy for marsh birds.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12995","usgsCitation":"Stevens, B.S., and Conway, C.J., 2020, Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds: Diversity and Distributions, v. 26, no. 1, p. 34-48, https://doi.org/10.1111/ddi.12995.","productDescription":"15 p.","startPage":"34","endPage":"48","ipdsId":"IP-105638","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":458587,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12995","text":"Publisher Index Page"},{"id":389474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":823459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":823458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206418,"text":"70206418 - 2020 - Low streamflow trends at human-impacted and reference basins in the United States","interactions":[],"lastModifiedDate":"2019-11-04T14:42:50","indexId":"70206418","displayToPublicDate":"2019-10-18T14:36:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Low streamflow trends at human-impacted and reference basins in the United States","docAbstract":"We present a continent-scale exploration of trends in annual 7-day low streamflows at 2482 U.S. Geological Survey streamgages across the conterminous United States over the past 100, 75, and 50 years (1916–2015, 1941–2015 and 1966–2015). We used basin characteristics to identify subsets of study basins representative of reference basins with streamflow relatively free from human effects (n = 259), and predominantly agricultural basins (n = 78), regulated basins (n = 220), and urban basins (n = 121). Trend significance was computed using the Mann-Kendall test considering short- and long-term persistence. Lag-one autocorrelation tests of detrended 7-day low streamflows for all gage classes show that time-series independence is not an appropriate assumption for annual low streamflow data at many basins. Among all study gages, upward trends (wetter conditions) in 7-day low streamflows outnumbered downward trends (drier conditions) approximately 2–1 for the 75- and 100-year trend periods—50-year trends indicated roughly equal numbers of increases and decreases. Increases in 7-day low streamflow were consistently observed for all time periods throughout much of the northeastern quadrant of the conterminous U.S. including western New England and the Mid-Atlantic, the southeastern Great Lakes basin, northern Ohio River basin, and the Upper Mississippi River and eastern Missouri River basins. Decreases in 7-day low streamflow were consistently observed for all time periods at many gages in the southeastern U.S. and in the northwestern U.S. in much of Idaho and northwestern Washington. Overall, we observed greater percentages of statistically significant trends at gages with human-induced influences than at reference gages. Low-flow trends at agricultural gages were regionally consistent with trends at reference gages. Regulated basins had many statistically significant upward trends for all three time periods tested, which may be attributed in part to substantial increases in dam-related storage prior to 1970. Urban gages had the greatest percentage of significant decreases in 7-day low flows compared to all other gage classes even though most urban gages saw upward trends in mean annual flows. Urban gages also had the greatest percentage of significant increases in low flows second only to regulated gages, highlighting that urban development can increase or decrease low streamflows depending on the basin-specific development.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124254","usgsCitation":"Dudley, R., Hirsch, R.M., Archfield, S.A., Blum, A., and Renard, B., 2020, Low streamflow trends at human-impacted and reference basins in the United States: Journal of Hydrology, v. 580, 124254, 13 p., https://doi.org/10.1016/j.jhydrol.2019.124254.","productDescription":"124254, 13 p.","ipdsId":"IP-098641","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":458591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2019.124254","text":"Publisher Index 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0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":774480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":774481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blum, Annalise G.","contributorId":193846,"corporation":false,"usgs":false,"family":"Blum","given":"Annalise G.","affiliations":[],"preferred":false,"id":774482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":774483,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215279,"text":"70215279 - 2020 - Late Quaternary evolution and stratigraphic framework influence on coastal systems along the north-central Gulf of Mexico, USA","interactions":[],"lastModifiedDate":"2020-10-16T11:53:56.257709","indexId":"70215279","displayToPublicDate":"2019-10-14T14:14:39","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Late Quaternary evolution and stratigraphic framework influence on coastal systems along the north-central Gulf of Mexico, USA","docAbstract":"Coastal systems in the Gulf of Mexico are threatened by reduced sediment supply, storm impacts and relative sea-level rise (RSLR). The geologic record provides insight into geomorphic evolution thresholds to these forcing mechanisms to help predict future barrier evolution in response to climate change. This study synthesizes ∼2100 km of geophysical data, 700 + sediment cores, and 62 radiocarbon dates to regionally map two lowstand sequence boundaries, multiple ravinement surfaces and fourteen depositional facies demonstrating stratigraphic and antecedent topographic influences on coastal evolution. The Mississippi-Alabama (MSAL) barriers are anchored by a marine isotope stage (MIS) 5e section of Dauphin Island coupled with an MIS 2 surface gradient change. Sand for the modern MSAL barriers were largely sourced through Holocene transgressive ravinement of relict valley fill deposits, providing up to 300 × 106 m3 of sand. Mud-filled MIS 2 tributaries correspond to areas of repeated storm breaching or tidal inlets.\n\nA Holocene geomorphic evolutionary model was created for Petit Bois and Dauphin Islands, highlighting RSLR rates, changes in sediment supply and the antecedent geologic framework. As the MIS 2 surface was flooded, tidal/wave scour supplied sand to migrating marine shoals. These transgressing shoals converted drowned paleovalleys to estuaries ∼9ka BP. Islands formed in their modern positions ∼6ka BP, when sediment supply was high and RSLR rates were 2 mm/yr. Between ∼4ka-1750 CE, islands prograded from reduced RSLR rates of 1-0.4 mm/yr and sufficient sand supply from alongshore/inner shelf sources. Currently, the islands experience 3.74 mm/yr of RSLR and reduced sediment supply, resulting in barrier degradation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2019.105910","usgsCitation":"Hollis, R.S., Wallace, D.J., Miner, M.D., Gal, N.S., Dike, C.H., and Flocks, J., 2020, Late Quaternary evolution and stratigraphic framework influence on coastal systems along the north-central Gulf of Mexico, USA: Quaternary Science Reviews, v. 223, 105910, 24 p., https://doi.org/10.1016/j.quascirev.2019.105910.","productDescription":"105910, 24 p.","ipdsId":"IP-104001","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488434,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://aquila.usm.edu/masters_theses/598","text":"External Repository"},{"id":379381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"North-Central Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.40673828125,\n 29.897805610155874\n ],\n [\n -87.01171875,\n 29.897805610155874\n ],\n [\n -87.01171875,\n 30.694611546632277\n ],\n [\n -89.40673828125,\n 30.694611546632277\n ],\n [\n -89.40673828125,\n 29.897805610155874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"223","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hollis, Robert S","contributorId":243055,"corporation":false,"usgs":false,"family":"Hollis","given":"Robert","email":"","middleInitial":"S","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":801451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wallace, Davin J","contributorId":243056,"corporation":false,"usgs":false,"family":"Wallace","given":"Davin","email":"","middleInitial":"J","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":801452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miner, Michael D","contributorId":243057,"corporation":false,"usgs":false,"family":"Miner","given":"Michael","email":"","middleInitial":"D","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":801453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gal, Nina S","contributorId":243058,"corporation":false,"usgs":false,"family":"Gal","given":"Nina","email":"","middleInitial":"S","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":801454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dike, Clayton H","contributorId":243059,"corporation":false,"usgs":false,"family":"Dike","given":"Clayton","email":"","middleInitial":"H","affiliations":[{"id":38697,"text":"University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":801455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flocks, James 0000-0002-6177-7433","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":221107,"corporation":false,"usgs":true,"family":"Flocks","given":"James","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":801456,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207153,"text":"70207153 - 2020 - Assessing the hydrologic impact of historical railroad embankments on wetland vegetation response in Canaan Valley, WV (USA): The value of high-resolution data","interactions":[],"lastModifiedDate":"2020-02-06T11:05:05","indexId":"70207153","displayToPublicDate":"2019-10-09T19:57:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the hydrologic impact of historical railroad embankments on wetland vegetation response in Canaan Valley, WV (USA): The value of high-resolution data","docAbstract":"The recovery of natural ecological processes after disturbance is poorly understood. Some disturbances may be so severe as to set ecosystems onto a new trajectory. The Canaan Valley National Wildlife Refuge in West Virginia protects a unique high-altitude wetland that was heavily disturbed by logging 100 years BP and has since transitioned to a new ecological state (shrub wetland). Refuge managers interested in preserving and restoring ecosystem states expressed concerned about lingering impacts of previous disturbances (logging, railroads, beaver, deer, fire). Available data suggested hydrologic impacts from the remnant rail grade but managers had insufficient quantitative data to assess these impacts. We initiated a fine scale assessment of topography, vegetation distribution, and hydrology to assess impacts from the remnant rail grade using lidar data, vegetation surveys, and piezometers. We developed topographic models, hydrological models, and mapped vegetation distribution. We developed statistical models to assess relationships between vegetation communities, hydrology, and distance to the rail grade. Surprisingly, we found that hydrologic flow paths did not conform to expectation and were not restricted by remnant land use features. For the most part, vegetation communities are responding to topographic and environmental gradients that existed prior to disturbance. Use of highly detailed topographic data (lidar), field hydrology, and vegetation studies allowed us to more accurately assess hydrologic and vegetation regimes, eliminating the need for mitigation, saving significant resources.","language":"English","publisher":"Wiley","doi":"10.1111/rec.13061","usgsCitation":"Young, J.A., Welsch, D., and Deacon, S., 2020, Assessing the hydrologic impact of historical railroad embankments on wetland vegetation response in Canaan Valley, WV (USA): The value of high-resolution data: Restoration Ecology, v. 28, no. 1, p. 51-62, https://doi.org/10.1111/rec.13061.","productDescription":"12 p.","startPage":"51","endPage":"62","ipdsId":"IP-108544","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":437211,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KUATM7","text":"USGS data release","linkHelpText":"Environmental data collected at piezometer field plot locations used to study hydrologic impacts on vegetation due to historic rail road embankment at Canaan Valley NWR"},{"id":370120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Tucker County","otherGeospatial":"Canaan Valley","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-79.4869,39.195],[-79.4816,39.1909],[-79.4745,39.1836],[-79.4692,39.1804],[-79.4656,39.1808],[-79.4585,39.1826],[-79.4525,39.1858],[-79.4478,39.1853],[-79.4424,39.1889],[-79.4394,39.1921],[-79.4352,39.192],[-79.4269,39.1929],[-79.4239,39.1943],[-79.4216,39.1942],[-79.4186,39.192],[-79.4145,39.191],[-79.4115,39.1892],[-79.4091,39.1883],[-79.4056,39.1887],[-79.4032,39.191],[-79.3996,39.1928],[-79.3973,39.1909],[-79.3901,39.1923],[-79.3871,39.1959],[-79.3865,39.1995],[-79.3829,39.2049],[-79.374,39.2062],[-79.3704,39.2076],[-79.3675,39.203],[-79.3645,39.2003],[-79.3604,39.1994],[-79.3597,39.2066],[-79.3579,39.2089],[-79.3508,39.2102],[-79.3436,39.2124],[-79.3353,39.2142],[-79.3264,39.2146],[-79.324,39.2123],[-79.3169,39.2123],[-79.3057,39.2072],[-79.2974,39.2004],[-79.2904,39.1936],[-79.2904,39.1908],[-79.2922,39.1886],[-79.2976,39.1873],[-79.3041,39.1855],[-79.3083,39.1814],[-79.3125,39.1769],[-79.3125,39.1719],[-79.3102,39.1678],[-79.3108,39.166],[-79.3203,39.1647],[-79.3227,39.1625],[-79.3275,39.1552],[-79.3294,39.1521],[-79.3288,39.1457],[-79.3283,39.1407],[-79.3259,39.1394],[-79.3253,39.1376],[-79.326,39.1339],[-79.3319,39.1294],[-79.3343,39.1267],[-79.3349,39.124],[-79.3362,39.1186],[-79.3404,39.1145],[-79.3452,39.1059],[-79.347,39.0991],[-79.3495,39.0937],[-79.353,39.0901],[-79.3525,39.0878],[-79.3495,39.086],[-79.3371,39.0841],[-79.3294,39.0818],[-79.3199,39.0781],[-79.3164,39.0763],[-79.3105,39.0754],[-79.3087,39.0758],[-79.3081,39.0758],[-79.3057,39.0763],[-79.3004,39.0762],[-79.2993,39.0726],[-79.2981,39.0708],[-79.3011,39.0667],[-79.3017,39.0649],[-79.3036,39.0577],[-79.3036,39.0536],[-79.3048,39.0495],[-79.3066,39.0441],[-79.3126,39.0418],[-79.3174,39.0364],[-79.3156,39.0355],[-79.3115,39.0319],[-79.3109,39.0301],[-79.3122,39.0205],[-79.3122,39.0196],[-79.3122,39.0183],[-79.3158,39.0165],[-79.3206,39.0106],[-79.3218,39.0029],[-79.3248,38.9989],[-79.3261,38.9934],[-79.3267,38.9893],[-79.3315,38.9803],[-79.3381,38.9722],[-79.3417,38.9695],[-79.3482,38.9671],[-79.3573,38.9642],[-79.3646,38.967],[-79.3803,38.9658],[-79.392,38.9701],[-79.3974,38.9719],[-79.4068,38.9725],[-79.4198,38.973],[-79.4263,38.9744],[-79.4322,38.9767],[-79.4387,38.9754],[-79.4452,38.975],[-79.4535,38.9759],[-79.46,38.9773],[-79.4677,38.9787],[-79.476,38.9787],[-79.4849,38.976],[-79.4914,38.9761],[-79.5003,38.9716],[-79.6031,38.9955],[-79.683,39.0139],[-79.7629,39.0322],[-79.7842,39.0372],[-79.7776,39.0431],[-79.7741,39.0503],[-79.7758,39.0567],[-79.7752,39.0589],[-79.7764,39.0667],[-79.7728,39.0743],[-79.7716,39.0762],[-79.7775,39.0807],[-79.7793,39.0821],[-79.7817,39.0843],[-79.7822,39.0907],[-79.7858,39.0916],[-79.7888,39.093],[-79.7876,39.0984],[-79.7935,39.0989],[-79.7964,39.1007],[-79.797,39.1057],[-79.7982,39.1084],[-79.8035,39.1102],[-79.8124,39.1116],[-79.8177,39.1157],[-79.8195,39.1166],[-79.8213,39.1166],[-79.8243,39.1152],[-79.8278,39.1184],[-79.8302,39.1257],[-79.8331,39.1284],[-79.8361,39.1297],[-79.8331,39.1365],[-79.8313,39.1402],[-79.8319,39.1488],[-79.8307,39.1538],[-79.8253,39.1619],[-79.8259,39.1692],[-79.8265,39.1769],[-79.8223,39.1823],[-79.8223,39.1841],[-79.8247,39.1891],[-79.8265,39.1923],[-79.8241,39.1968],[-79.8164,39.2022],[-79.814,39.205],[-79.8128,39.2108],[-79.8133,39.2149],[-79.8157,39.2177],[-79.8145,39.2186],[-79.8104,39.2217],[-79.8086,39.2235],[-79.8074,39.2249],[-79.8091,39.2294],[-79.8056,39.2317],[-79.6836,39.2718],[-79.6225,39.253],[-79.5081,39.2168],[-79.4869,39.195]]]},\"properties\":{\"name\":\"Tucker\",\"state\":\"WV\"}}]}","volume":"28","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":777017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsch, Daniel","contributorId":221109,"corporation":false,"usgs":false,"family":"Welsch","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":777018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deacon, Sarah","contributorId":221110,"corporation":false,"usgs":false,"family":"Deacon","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":777019,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205935,"text":"70205935 - 2020 - Changes in event‐based streamflow magnitude and timing after suburban development with infiltration‐based stormwater management","interactions":[],"lastModifiedDate":"2020-01-20T12:16:24","indexId":"70205935","displayToPublicDate":"2019-10-09T13:33:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Changes in event‐based streamflow magnitude and timing after suburban development with infiltration‐based stormwater management","docAbstract":"Green stormwater infrastructure implementation in urban watersheds has outpaced our understanding of practice effectiveness on streamflow response to precipitation events. Long‐term monitoring of experimental urban watersheds in Clarksburg, Maryland, USA, provided an opportunity to examine changes in event‐based streamflow metrics in two treatment watersheds that transitioned from agriculture to suburban development with a high density of infiltration‐focused stormwater control measures (SCMs). Urban Treatment 1 has predominantly single family detached housing with 33% impervious cover and 126 SCMs. Urban Treatment 2 has a mix of single family detached and attached housing with 44% impervious cover and 219 SCMs. Differences in streamflow‐event magnitude and timing were assessed using a before‐after‐control‐reference‐impact design to compare urban treatment watersheds to a forested control and an urban control with detention‐focused SCMs. Streamflow and precipitation events were identified from 14 years of sub‐daily monitoring data with an automated approach to characterize peak streamflow, runoff yield, runoff ratio, streamflow duration, time to peak, rise rate, and precipitation depth for each event. Results indicated that streamflow magnitude and timing were altered by urbanization in the urban treatment watersheds, even with SCMs treating 100% of the impervious area. The largest hydrologic changes were observed in streamflow magnitude metrics, with greater hydrologic change in Urban Treatment 2 compared to Urban Treatment 1. While streamflow changes were observed in both urban treatment watersheds, SCMs were able to mitigate peak flows and runoff volumes compared to the urban control. The urban control had similar impervious cover to Urban Treatment 2, but Urban Treatment 2 had more than twice the precipitation depth needed to initiate a flow response and lower median peak flow and runoff yield for events less than 20 mm. Differences in impervious cover between the Urban Treatment watersheds appeared to be a large driver of differences in streamflow response, rather than SCM density. Overall, use of infiltration‐focused SCMs implemented at a watershed‐scale did provide enhanced attenuation of peak flow and runoff volumes compared to centralized‐detention SCMs.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13593","usgsCitation":"Hopkins, K.G., Bhaskar, A.S., Woznicki, S., and Fanelli, R., 2020, Changes in event‐based streamflow magnitude and timing after suburban development with infiltration‐based stormwater management: Hydrological Processes, v. 34, no. 2, p. 387-403, https://doi.org/10.1002/hyp.13593.","productDescription":"17 p.","startPage":"387","endPage":"403","ipdsId":"IP-108936","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":458618,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro‐ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro‐ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental‐scale movement data within agro‐ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non‐agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro‐ecosystems.
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USA","interactions":[],"lastModifiedDate":"2020-10-14T23:26:06.094069","indexId":"70215284","displayToPublicDate":"2019-10-08T18:15:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Influence of land use and hydrologic variability on seasonal dissolved organic carbon and nitrate export: Insights from a multi-year regional analysis for the northeastern USA","docAbstract":"Land use/land cover (LULC) change has significant impacts on nutrient loading to aquatic systems and has been linked to deteriorating water quality globally. While many relationships between LULC and nutrient loading have been identified, characterization of the interaction between LULC, climate (specifically variable hydrologic forcing) and solute export across seasonal and interannual time scales is needed to understand the processes that determine nutrient loading and responses to change. Recent advances in high-frequency water quality sensors provide opportunities to assess these interannual relationships with sufficiently high temporal resolution to capture the unpredictable, short-term storm events that likely drive important export mechanisms for dissolved organic carbon (DOC) and nitrate (NO3−–N). We deployed a network of in situ sensors in forested, agricultural, and urban watersheds across the northeastern United States. Using 2 years of high-frequency sensor data, we provide a regional assessment of how LULC and hydrologic variability affected the timing and magnitude of dissolved organic carbon and nitrate export, and the status of watershed fluxes as either supply or transport controlled. Analysis of annual export dynamics revealed systematic differences in the timing and magnitude of DOC and NO3−–N delivery among different LULC classes, with distinct regional similarities in the timing of DOC and NO3−–N fluxes from forested and urban watersheds. Conversely, export dynamics at agricultural sites appeared to be highly site-specific, likely driven by local agricultural practices and regulations. Furthermore, the magnitude of solute fluxes across watersheds responded strongly to interannual variability in rainfall, suggesting a high degree of hydrologic control over nutrient loading across the region. Thus, there is strong potential for climate-driven changes in regional hydrologic cycles to drive variation in the magnitude of downstream nutrient fluxes, particularly in watersheds where solute supply and/or transport has been modified.
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surface water ontologies: A comparison of manual and automated approaches","docAbstract":"More data are being collected about the world around us than ever before, but effectively using this information requires different data stores to be integrated in such a way that they can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two current leading automated alignment systems to integrate four ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. In addition, we show that, with minor modifications, existing alignment algorithms can be used effectively within a semi-automated alignment system for the surface water domain.","language":"English","publisher":"Springer","doi":"10.1007/s10109-019-00312-3","usgsCitation":"Cheatham, M., Varanka, D.E., Arauz, F., and Zhou, L., 2020, Alignment of surface water ontologies: A comparison of manual and automated approaches: Journal of Geographical Systems, v. 22, no. 2, p. 267-289, https://doi.org/10.1007/s10109-019-00312-3.","productDescription":"23 p.","startPage":"267","endPage":"289","ipdsId":"IP-101017","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":371902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Cheatham, Michelle","contributorId":222086,"corporation":false,"usgs":false,"family":"Cheatham","given":"Michelle","email":"","affiliations":[{"id":13348,"text":"Wright State University","active":true,"usgs":false}],"preferred":false,"id":781127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":781126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arauz, Fatima","contributorId":222087,"corporation":false,"usgs":false,"family":"Arauz","given":"Fatima","email":"","affiliations":[{"id":13348,"text":"Wright State University","active":true,"usgs":false}],"preferred":false,"id":781128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Lu","contributorId":222088,"corporation":false,"usgs":false,"family":"Zhou","given":"Lu","email":"","affiliations":[{"id":13348,"text":"Wright State University","active":true,"usgs":false}],"preferred":false,"id":781129,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}