{"pageNumber":"192","pageRowStart":"4775","pageSize":"25","recordCount":40769,"records":[{"id":70226859,"text":"70226859 - 2022 - Climate extremes as drivers of surface-water-quality trends in the United States","interactions":[],"lastModifiedDate":"2021-12-16T12:55:51.685069","indexId":"70226859","displayToPublicDate":"2021-12-05T06:53:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Climate extremes as drivers of surface-water-quality trends in the United States","docAbstract":"

Surface-water quality can change in response to climate perturbations, such as changes in the frequency of heavy precipitation or droughts, through direct effects, such as dilution and concentration, and through physical processes, such as bank scour. Water quality might also change through indirect mechanisms, such as changing water demand or changes in runoff interaction with organic matter on the landscape. Many studies predict future changes in water-quality related to climate changes; however, fewer studies specifically document changes in water quality related to changes in climate, and they are usually limited in geographic scope. Recently, the U.S. Geological Survey's National Water-Quality Program reported nearly 12,000 trends in concentration and load for numerous water-quality constituents, including nutrients, sediment, major ions, and carbon. The results provide an unprecedented opportunity to examine sites across the conterminous United States for changes in water quality related to climate changes. We used published water-quality trends, modeled using the method of Weighted Regressions on Time, Season and Discharge, and calculated trends in climate extremes indices, using a modified Mann-Kendall trend method. The water-quality and the climate extremes trends were combined to identify areas in the conterminous United States where changes in climate extremes may have changed water quality. We investigated the water-quality trends in these areas to determine whether the trends related to changes in climate. We found that it was important to go beyond spatial correlation and examine trends on a watershed scale to investigate key drivers of trends. We found successful management practices in Iowa to reduce chloride concentrations, despite increases in icing days. For sediment, it appeared that management practices were having a larger effect than climate changes. For nutrients, complex forces affecting water quality make it difficult to unequivocally attribute water-quality change to climate change.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.152165","usgsCitation":"Ryberg, K.R., and Chanat, J.G., 2022, Climate extremes as drivers of surface-water-quality trends in the United States: Science of the Total Environment, v. 809, 152165, 12 p., https://doi.org/10.1016/j.scitotenv.2021.152165.","productDescription":"152165, 12 p.","ipdsId":"IP-130945","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488976,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2021.152165","text":"Publisher Index Page"},{"id":393005,"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":828520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chanat, Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247510,"text":"70247510 - 2022 - Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches","interactions":[],"lastModifiedDate":"2023-08-11T13:22:06.505287","indexId":"70247510","displayToPublicDate":"2021-12-04T06:48:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2622,"text":"Limnology and Oceanography: Methods","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches","title":"Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches","docAbstract":"

Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon-14 (14C)-labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high-frequency in situ data such as free-water dissolved oxygen (O2). While there is a rich literature on the comparison of approaches for estimating primary production using incubations (e.g., 14C and O2 bottle experiments), as well for approaches using high-frequency data (e.g., diel O2 and CO2 metabolism models), there are few direct comparisons of 14C incubations and free-water O2 approaches for estimating primary production. We used 20 lake-years of concurrent measurements of primary production quantified from high-frequency free-water O2 data and 14C incubations in four different lakes (4–7 years per lake) to compare these different approaches. Across all lakes, 61% of the 14C production estimates were within the 95% credible intervals of the free-water O2 production estimates. Error-in-variable regressions support the assumption that 14C methods estimate a production value between gross primary production and net primary production and the bottle effect is constant across the entire range of production values considered here. There was little evidence that daily pelagic, epilimnetic estimates of primary production differed substantially based on the selection of free-water O2 or 14C approaches in these lakes during summer stratified conditions.

","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lom3.10471","usgsCitation":"Lottig, N.R., Phillips, J., Batt, R.D., Scordo, F., Williamson, T.J., Carpenter, S.R., Chandra, S., Hanson, P.C., Solomon, C.T., Vanni, M.J., and Zwart, J.A., 2022, Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches: Limnology and Oceanography: Methods, v. 20, no. 1, p. 34-45, https://doi.org/10.1002/lom3.10471.","productDescription":"12 p.","startPage":"34","endPage":"45","ipdsId":"IP-126978","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":419693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Lottig, Noah R.","contributorId":172031,"corporation":false,"usgs":false,"family":"Lottig","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":879917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Joseph 0000-0003-2016-1306","orcid":"https://orcid.org/0000-0003-2016-1306","contributorId":318157,"corporation":false,"usgs":false,"family":"Phillips","given":"Joseph","email":"","affiliations":[{"id":69342,"text":"Holar University","active":true,"usgs":false}],"preferred":false,"id":879918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Batt, Ryan D.","contributorId":196242,"corporation":false,"usgs":false,"family":"Batt","given":"Ryan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":879919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scordo, Facundo","contributorId":298282,"corporation":false,"usgs":false,"family":"Scordo","given":"Facundo","email":"","affiliations":[{"id":64520,"text":"Instituto Argentino de Oceanografía","active":true,"usgs":false}],"preferred":false,"id":879920,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williamson, Tanner J.","contributorId":223165,"corporation":false,"usgs":false,"family":"Williamson","given":"Tanner","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":879921,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carpenter, Stephen R. 0000-0001-8097-8700","orcid":"https://orcid.org/0000-0001-8097-8700","contributorId":196945,"corporation":false,"usgs":false,"family":"Carpenter","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":879922,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandra, Sudeep 0000-0002-9297-8211","orcid":"https://orcid.org/0000-0002-9297-8211","contributorId":224786,"corporation":false,"usgs":false,"family":"Chandra","given":"Sudeep","email":"","affiliations":[{"id":32871,"text":"University of Nevada at Reno","active":true,"usgs":false}],"preferred":false,"id":879923,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":879924,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":879925,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vanni, Michael J.","contributorId":204106,"corporation":false,"usgs":false,"family":"Vanni","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":36846,"text":"Department of Zoology, Miami University (Ohio)","active":true,"usgs":false}],"preferred":false,"id":879926,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":879927,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70226885,"text":"70226885 - 2022 - Geostatistical mapping of salinity conditioned on borehole logs, Montebello Oil Field, California","interactions":[],"lastModifiedDate":"2022-03-15T16:38:07.413106","indexId":"70226885","displayToPublicDate":"2021-12-03T07:01:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Geostatistical mapping of salinity conditioned on borehole logs, Montebello Oil Field, California","docAbstract":"

We present a geostatistics-based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS-ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time-lapse salinity modeling through Bayesian updating.

","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13155","usgsCitation":"Terry, N., Day-Lewis, F., Landon, M.K., Land, M., Stanton, J.S., and Lane, J.W., 2022, Geostatistical mapping of salinity conditioned on borehole logs, Montebello Oil Field, California: Groundwater, v. 60, no. 2, p. 242-261, https://doi.org/10.1111/gwat.13155.","productDescription":"20 p.","startPage":"242","endPage":"261","ipdsId":"IP-118997","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449470,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gwat.13155","text":"External Repository"},{"id":436035,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L0XGEG","text":"USGS data release","linkHelpText":"Data used to estimate groundwater salinity above the Montebello oil field (California, USA)"},{"id":393095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Montebello Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.0703125,\n 33.742612777346885\n ],\n [\n -116.3836669921875,\n 33.742612777346885\n ],\n [\n -116.3836669921875,\n 34.048108084909835\n ],\n [\n -117.0703125,\n 34.048108084909835\n ],\n [\n -117.0703125,\n 33.742612777346885\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":828636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":828637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Land, Michael 0000-0001-5141-0307 mtland@usgs.gov","orcid":"https://orcid.org/0000-0001-5141-0307","contributorId":171938,"corporation":false,"usgs":true,"family":"Land","given":"Michael","email":"mtland@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":828641,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229712,"text":"70229712 - 2022 - Warming conditions boost reproductive output for a northern gopher tortoise population","interactions":[],"lastModifiedDate":"2022-03-16T15:47:19.149065","indexId":"70229712","displayToPublicDate":"2021-12-02T11:45:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Warming conditions boost reproductive output for a northern gopher tortoise population","docAbstract":"

The effects of climate change on at-risk species will depend on how life history processes respond to climate and whether the seasonal timing of local climate changes overlaps with species-specific windows of climate sensitivity. For long-lived, iteroparous species like gopher tortoises Gopherus polyphemus, climate likely has a greater influence on reproduction than on adult survival. Our objective was to estimate the timing, magnitude, and direction of climate-driven effects on gopher tortoise reproductive output using a 25 yr dataset collected in southeastern Georgia, USA, near the northern edge of the species’ range. We assessed the timing of climate effects on reproductive output (both probability of reproduction and clutch size) by fitting models with climate covariates (maximum temperature, precipitation, and temperature range) summarized at all possible time intervals (in 1 mo increments) within the 24 mo period prior to the summer census date. We then fit a final model of reproductive output as a function of the identified climate variables and time windows using a Bayesian mixture model. Probability of reproduction was positively correlated with the prior year’s April-May maximum temperature, and clutch size was positively correlated with the prior year’s June maximum temperature. April-May and June maximum temperatures have increased over the past 3 decades at the study site, which likely led to an increase in clutch size of approximately 1 egg (15% increase over a mean of 6.5 eggs). However, the net effect of climate change on gopher tortoise population dynamics will depend on whether there are opposing or reinforcing climate responses for other demographic rates.

","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01155","usgsCitation":"Hunter, E.A., Loope, K., Drake, K.K., Hanley, K., Jones, D.N., Shoemaker, K., and Rostal, D., 2022, Warming conditions boost reproductive output for a northern gopher tortoise population: Endangered Species Research, v. 46, p. 215-226, https://doi.org/10.3354/esr01155.","productDescription":"12 p.","startPage":"215","endPage":"226","ipdsId":"IP-132399","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449478,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01155","text":"Publisher Index Page"},{"id":397162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Fort Stewart Army Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.683349609375,\n 32.11747489684617\n ],\n [\n -81.727294921875,\n 32.10816944421472\n ],\n [\n -81.78909301757812,\n 32.12910537866883\n ],\n [\n -81.81243896484375,\n 32.10816944421472\n ],\n [\n -81.82891845703125,\n 32.10467965495091\n ],\n [\n -81.85638427734375,\n 32.051152857201714\n ],\n [\n -81.85089111328125,\n 32.0232133942454\n ],\n [\n -81.86187744140625,\n 31.991771310172094\n ],\n [\n -81.89071655273438,\n 31.949831760406877\n ],\n [\n -81.88522338867188,\n 31.91953017247695\n ],\n [\n -81.63116455078124,\n 31.84373252620705\n ],\n [\n -81.62017822265625,\n 31.85773063158148\n ],\n [\n -81.60781860351561,\n 31.85889704445453\n ],\n [\n -81.57073974609375,\n 31.87522527511162\n ],\n [\n -81.55838012695312,\n 31.865895211796346\n ],\n [\n -81.36474609375,\n 31.950997006605856\n ],\n [\n -81.3372802734375,\n 31.948666499428395\n ],\n [\n -81.30020141601562,\n 32.001088607540446\n ],\n [\n -81.41006469726562,\n 32.09769967633269\n ],\n [\n -81.46774291992186,\n 32.10002639514208\n ],\n [\n -81.683349609375,\n 32.11747489684617\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loope, Kevin J.","contributorId":288536,"corporation":false,"usgs":false,"family":"Loope","given":"Kevin J.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":838062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, K. Kristina 0000-0003-0711-7634 kdrake@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-7634","contributorId":3799,"corporation":false,"usgs":true,"family":"Drake","given":"K.","email":"kdrake@usgs.gov","middleInitial":"Kristina","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanley, Kaitlyn","contributorId":97416,"corporation":false,"usgs":true,"family":"Hanley","given":"Kaitlyn","affiliations":[],"preferred":false,"id":838064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Douglas N. Jr.","contributorId":288539,"corporation":false,"usgs":false,"family":"Jones","given":"Douglas","suffix":"Jr.","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":838065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":838066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rostal, David C.","contributorId":288543,"corporation":false,"usgs":false,"family":"Rostal","given":"David C.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":838067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70226653,"text":"70226653 - 2022 - Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services","interactions":[],"lastModifiedDate":"2021-12-02T16:19:39.846812","indexId":"70226653","displayToPublicDate":"2021-12-02T10:16:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services","docAbstract":"Social Values for Ecosystem Services (SolVES) version 4.0 is a fully open-source, GIS-based tool designed to aid in the creation of quantitative, spatially explicit models of the nonmonetary values attributed to cultural ecosystem services, such as aesthetics and recreation, specifically to facilitate their incorporation into larger ecosystem service assessments. Newly redeveloped for QGIS, SolVES can be applied in a wide variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban settings worldwide. Redeveloping SolVES for an open-source platform was intended to expand its user base by eliminating the cost of proprietary GIS software licenses and to remove the impact of proprietary software changes on SolVES development. Providing additional options would enable users to delineate relevant stakeholder groups to better assess how differing preferences impact the intensity and spatial distribution of perceived social values.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105259","usgsCitation":"Sherrouse, B.C., Semmens, D., and Ancona, Z.H., 2022, Social Values for Ecosystem Services (SolVES): Open-source spatial modeling of cultural services: Environmental Modelling & Software, v. 148, p. 1-16, https://doi.org/10.1016/j.envsoft.2021.105259.","productDescription":"105259, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-127459","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":449484,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105259","text":"Publisher Index Page"},{"id":392382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":827600,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230805,"text":"70230805 - 2022 - Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system","interactions":[],"lastModifiedDate":"2022-04-26T14:48:25.006386","indexId":"70230805","displayToPublicDate":"2021-12-02T09:25:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system","docAbstract":"

Water resource managers are faced with difficult decisions on how to satisfy human water needs while maintaining or restoring riverine ecosystems. Decision sciences have developed approaches and tools that can be used to break down difficult water management decisions into their component parts. An essential aspect of these approaches is the use of quantitative models to evaluate alternative management strategies. Here, we describe four integrated decision support models for evaluating the effect of flows on two life history stages of Chinook salmon (Oncorhynchus tshawytscha) and Steelhead (O. mykiss). We then use constrained nonlinear optimization to identify optimal flow regimes for the water year type with the least available water. These flow regimes were then used by managers to develop candidate minimum flow strategies that were evaluated using forward simulation and sensitivity analyses. We found that optimal flow regimes differed markedly from existing regulations and varied among species and life history stages. However, evaluation of tradeoffs among the four competing objectives indicated relatively minimal losses for most objectives when the optimal flows were based on equally weighting the objectives. Sensitivity analysis indicated that water temperature was the primary driver of estimated outcomes and suggested that managers consider alternative means of managing temperatures. Decision sciences have created multiple analytical tools and approaches that simplify complex problems, such as water resource management, and we believe that water resource management would benefit from their increased use.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3903","usgsCitation":"Peterson, J., Pease, J., Whitman, L., White, J., Stratton Garvin, L.E., Rounds, S.A., and Wallick, J., 2022, Integrated tools for identifying optimal flow regimes and evaluating alternative minimum flows for recovering at-risk salmonids in a highly managed system: River Research and Applications, v. 38, no. 2, p. 293-308, https://doi.org/10.1002/rra.3903.","productDescription":"16 p.","startPage":"293","endPage":"308","ipdsId":"IP-131462","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":449487,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3903","text":"Publisher Index Page"},{"id":399667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.794189453125,\n 45.84410779560204\n ],\n [\n -123.255615234375,\n 45.521743896993634\n ],\n [\n -123.72802734375,\n 44.276671273775186\n ],\n [\n -123.15673828124999,\n 43.6599240747891\n ],\n [\n -122.3876953125,\n 43.45291889355465\n ],\n [\n -121.981201171875,\n 43.41302868475145\n ],\n [\n -121.61865234375,\n 43.51668853502906\n ],\n [\n -121.26708984374999,\n 44.008620115415354\n ],\n [\n -121.35498046875,\n 44.5278427984555\n ],\n [\n -121.66259765625001,\n 45.24395342262324\n ],\n [\n -122.18994140624999,\n 45.521743896993634\n ],\n [\n -122.574462890625,\n 45.54483149242463\n ],\n [\n -122.78320312499999,\n 45.81348649679973\n ],\n [\n -122.794189453125,\n 45.84410779560204\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":841385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Jessica E.","contributorId":290612,"corporation":false,"usgs":false,"family":"Pease","given":"Jessica E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":841386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitman, Luke","contributorId":290613,"corporation":false,"usgs":false,"family":"Whitman","given":"Luke","email":"","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":841387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":841461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stratton Garvin, Laurel E. 0000-0001-8567-8619 lstratton@usgs.gov","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":270182,"corporation":false,"usgs":true,"family":"Stratton Garvin","given":"Laurel","email":"lstratton@usgs.gov","middleInitial":"E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841462,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rounds, Stewart A. 0000-0002-8540-2206","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":205029,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wallick, J. 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Lakes are warming, resulting in the loss of many native fish. In order to manage economically valuable fisheries and other ecosystem services provided by lakes, it is important for managers to have access to accurate estimates of water temperature to better understand past change and to plan for potential future further warming. These data are invaluable for making decisions such as whether to continue stocking plans as usual in certain lakes or how to set specific harvest limits. This project developed new state-of-the-art methods to model historical thermal habitat for thousands of lakes in the Midwest US.","language":"English","publisher":"North Central Climate Adaptation Science Center","usgsCitation":"Read, J., 2022, Final report: Understanding historical and predicting future lake temperatures in North and South Dakota, 15 p.","productDescription":"15 p.","ipdsId":"IP-123312","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":408143,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencebase.gov/catalog/item/61af7eb2d34eb622f69b1200"}],"country":"United States","state":"North Dakota, South 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Dakota\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854190,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237342,"text":"70237342 - 2022 - Physics-guided machine learning from simulation data: An application in modeling lake and river systems","interactions":[],"lastModifiedDate":"2022-10-11T16:20:24.97395","indexId":"70237342","displayToPublicDate":"2021-12-01T11:12:09","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Physics-guided machine learning from simulation data: An application in modeling lake and river systems","docAbstract":"This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output variables, these models often produce biased simulations due to inaccurate parameterizations or approximations used to represent the true physics. In this paper, we aim to build a new data-driven framework to monitor dynamical systems by extracting general scientific knowledge embodied in simulation data generated by the physics-based model. To handle the bias in simulation data caused by imperfect parameterization, we propose to extract general physical relations jointly from multiple sets of simulations generated by a physics-based model under different physical parameters. In particular, we develop a spatio-temporal network architecture that uses its gating variables to capture the variation of physical parameters. We initialize this model using a pre-training strategy that helps discover common physical patterns shared by different sets of simulation data. Then we fine-tune it using limited observation data via a contrastive learning process. By leveraging the complementary strength of machine learning and domain knowledge, our method has been shown to produce accurate predictions, use less training samples and generalize to out-of-sample scenarios. We further show that the method can provide insights about the variation of physical parameters over space and time in two domain applications: predicting temperature in streams and predicting temperature in lakes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IEEE International Conference on Data Mining","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"IEEE International Conference on Data Mining","conferenceDate":"December 7-10, 2021","conferenceLocation":"Auckland, New Zealand","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/ICDM51629.2021.00037","usgsCitation":"Jia, X., Xie, Y., Li, S., Chen, S., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., and Read, J., 2022, Physics-guided machine learning from simulation data: An application in modeling lake and river systems, in IEEE International Conference on Data Mining, Auckland, New Zealand, December 7-10, 2021, p. 270-279, https://doi.org/10.1109/ICDM51629.2021.00037.","productDescription":"10 p.","startPage":"270","endPage":"279","ipdsId":"IP-126776","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":854197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Sheng","contributorId":297449,"corporation":false,"usgs":false,"family":"Li","given":"Sheng","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":854199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854202,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854201,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854204,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236035,"text":"70236035 - 2022 - Data resources for NGA-subduction project","interactions":[],"lastModifiedDate":"2024-02-28T16:15:16.891191","indexId":"70236035","displayToPublicDate":"2021-12-01T10:09:11","publicationYear":"2022","noYear":false,"publicationType":{"id":26,"text":"Extramural-Authored Publication Paper"},"publicationSubtype":{"id":31,"text":"Extramural-Authored Publication"},"title":"Data resources for NGA-subduction project","docAbstract":"

A relational database was developed over a five-year period to support ground motion model (GMM) development for the Next Generation Attenuation-Subduction (NGA-Sub) project. The relational database has components that interact according to a database schema, including a source and path component used to describe attributes of seismic sources in global subduction regions and to compute source-to-site distances, a site component that describes attributes of sites where recordings have been made, and a ground motion component.

The source component of the database has information for 1880 earthquakes, mainly from the following regions: the Pacific Northwest region of North America, Alaska and the Aleutian Islands, Japan, Taiwan, New Zealand, South America, Central America, and Mexico. Of the 1880 earthquakes, 88 have finite fault models (FFMs) from the literature that were systematically reviewed, distilled to one more rectangular shapes, and trimmed according to procedures based on percentage of total slip. For earthquakes without FFMs, a simulation routine is used to represent finite fault effects required for distance calculations. This simulation routine was adjusted and made more uniform in its application than in prior NGA projects. All earthquakes are classified as interface, intraslab, shallow crustal, or outer rise, using uniform protocols developed for this project. All earthquakes are also assigned class designations adapted from a prior NGA project for active regions, that allows foreshock, mainshock, and aftershock events to be distinguished.

The site component of the database is described in a companion paper (Ahdi et al. 2020 [1]).

The ground motion component of the database consists of median – and maximum – horizontal component peak parameters (peak ground acceleration, PGA and peak ground velocity, PGV) and pseudo-spectral accelerations (PSa) at 111 oscillator periods and 11 damping ratios. Response spectra were also computed for the vertical component. Fourier amplitude spectra (FAS) and duration metrics were also computed. The ground motion recordings were obtained from collaborating organizations world-wide as uncorrected (Vol 1) digital recordings, that were corrected (componentspecific low – and high – pass filters and baseline correction, as needed) following Pacific Earthquake Engineering Research Center (PEER)/NGA protocols.

The relational database operates on each of these (and other) database components to dynamically draw relevant parameters into a single file, known as a flatfile, that is used by researchers engaged in GMM development. The flatfiles used in model development are being published with the NGA-Sub GMMs as products of the NGA-Sub project.

","conferenceTitle":"The 17th World Conference on Earthquake Engineering","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association of Earthquake Engineering","usgsCitation":"Contreras, V., Mazzoni, S., Kishida, T., Ahdi, S., Darragh, R.B., Youngs, R., Chiou, B., Kuehn, N., Wooddell, K., Bozorgnia, Y., and Stewart, J.P., 2022, Data resources for NGA-subduction project, 12 p.","productDescription":"12 p.","ipdsId":"IP-130876","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":426071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426069,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"}],"noUsgsAuthors":true,"publicationStatus":"PW","contributors":{"authors":[{"text":"Contreras, V.","contributorId":295706,"corporation":false,"usgs":false,"family":"Contreras","given":"V.","email":"","affiliations":[{"id":63911,"text":"University of California, Los Angeles, USA","active":true,"usgs":false}],"preferred":false,"id":849767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mazzoni, S.","contributorId":270337,"corporation":false,"usgs":false,"family":"Mazzoni","given":"S.","affiliations":[{"id":56148,"text":"University of California, Los Angeles, CA 90095","active":true,"usgs":false}],"preferred":false,"id":849765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kishida, T.","contributorId":203476,"corporation":false,"usgs":false,"family":"Kishida","given":"T.","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":849768,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ahdi, S.K.","contributorId":334403,"corporation":false,"usgs":false,"family":"Ahdi","given":"S.K.","affiliations":[{"id":80128,"text":"Exponent Failure Analysis, Los Angeles, CA","active":true,"usgs":false}],"preferred":false,"id":849764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Darragh, Robert B.","contributorId":25188,"corporation":false,"usgs":false,"family":"Darragh","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":895558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Youngs, R.R.","contributorId":75312,"corporation":false,"usgs":true,"family":"Youngs","given":"R.R.","email":"","affiliations":[],"preferred":false,"id":895559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chiou, B.S.J.","contributorId":74119,"corporation":false,"usgs":true,"family":"Chiou","given":"B.S.J.","email":"","affiliations":[],"preferred":false,"id":895560,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kuehn, N.","contributorId":334404,"corporation":false,"usgs":false,"family":"Kuehn","given":"N.","affiliations":[],"preferred":false,"id":895561,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wooddell, Kathryn","contributorId":47674,"corporation":false,"usgs":false,"family":"Wooddell","given":"Kathryn","email":"","affiliations":[{"id":13174,"text":"Pacific Gas & Electric","active":true,"usgs":false}],"preferred":false,"id":895562,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bozorgnia, Y.","contributorId":51427,"corporation":false,"usgs":true,"family":"Bozorgnia","given":"Y.","affiliations":[],"preferred":false,"id":895563,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":849773,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70229457,"text":"70229457 - 2022 - Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks","interactions":[],"lastModifiedDate":"2022-03-09T15:49:19.092846","indexId":"70229457","displayToPublicDate":"2021-12-01T09:44:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks","docAbstract":"

Environmental management often requires making decisions despite system uncertainty. One such example is mudflat mediation in flood control reservoirs. Reservoir mudflats limit development of diverse fish assemblages due to the lack of structural habitat provided by plants. Seeding mudflats with agricultural plants may mimic floodplain wetlands once inundated and provide fish habitat and achieve habitat management objectives. However, planting success is uncertain because of unpredictable water level fluctuations that affect plant survival and growth. Decision support tools can account for uncertainty that influences decision outcomes and reduce the risk in reservoir mudflat planting decisions. We used Bayesian decision networks and sensitivity analyses to quantify uncertainty surrounding mudflat plantings as supplemental fish habitat in four northwest Mississippi reservoirs. When averaged across all uncertainty, planting was the optimal decision only in Enid Lake. Response profiles indicated planting decisions depended on elevation contours within Enid, Sardis, and Grenada reservoirs. No planting was optimal at all elevations for Arkabutla Lake. These results provide a quantified basis for establishing best management practices and identify key system states that influence decision outcomes. The process used in this study to evaluate planting decisions can be applied to any reservoir by modifying reservoir dependent inputs to evaluate planting decisions to provide supplemental fish habitat.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.114139","usgsCitation":"Norris, D.M., Colvin, M.E., Miranda, L.E., and Lashley, M.A., 2022, Supplemental habitat is reservoir dependent: Identifying optimal planting decision using Bayesian Decision Networks: Journal of Environmental Management, v. 304, 114139, 12 p., https://doi.org/10.1016/j.jenvman.2021.114139.","productDescription":"114139, 12 p.","ipdsId":"IP-125224","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.15380859375,\n 33.43144133557529\n ],\n [\n -88.681640625,\n 33.43144133557529\n ],\n [\n -88.681640625,\n 34.96699890670367\n ],\n [\n -90.15380859375,\n 34.96699890670367\n ],\n [\n -90.15380859375,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"304","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norris, D. M.","contributorId":271192,"corporation":false,"usgs":false,"family":"Norris","given":"D.","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, M. E.","contributorId":275884,"corporation":false,"usgs":false,"family":"Colvin","given":"M.","email":"","middleInitial":"E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lashley, M. A.","contributorId":278603,"corporation":false,"usgs":false,"family":"Lashley","given":"M.","email":"","middleInitial":"A.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":837532,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226760,"text":"70226760 - 2022 - The impact of 3D finite‐fault information on ground‐motion forecasting for earthquake early warning","interactions":[],"lastModifiedDate":"2022-03-28T16:28:59.512213","indexId":"70226760","displayToPublicDate":"2021-11-30T06:33:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The impact of 3D finite‐fault information on ground‐motion forecasting for earthquake early warning","docAbstract":"

We identify aspects of finite‐source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite‐fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D‐source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line‐source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D‐source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground‐motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210162","usgsCitation":"Murray, J.R., Thompson, E.M., Baltay Sundstrom, A.S., and Minson, S.E., 2022, The impact of 3D finite‐fault information on ground‐motion forecasting for earthquake early warning: Bulletin of the Seismological Society of America, v. 112, no. 2, p. 779-802, https://doi.org/10.1785/0120210162.","productDescription":"24 p.","startPage":"779","endPage":"802","ipdsId":"IP-130425","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":392720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":828178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":828179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":828180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":828181,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241495,"text":"70241495 - 2022 - Projecting the remaining habitat for the western spadefoot (Spea hammondii) in heavily urbanized southern California","interactions":[],"lastModifiedDate":"2023-03-22T13:21:05.043486","indexId":"70241495","displayToPublicDate":"2021-11-29T08:17:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Projecting the remaining habitat for the western spadefoot (Spea hammondii) in heavily urbanized southern California","title":"Projecting the remaining habitat for the western spadefoot (Spea hammondii) in heavily urbanized southern California","docAbstract":"

Extensive urbanization in coastal southern California has reduced natural habitat in this biodiversity hotspot. To better conserve ecological communities, state and federal agencies, along with local jurisdictions and private stakeholders, developed regional conservation plans for southern California. Although many protected areas exist within this region, the patchwork nature of these protected areas might not provide good coverage for species that require multiple habitat components, such as amphibians with complex life histories. Because of declines in the past century, the status of the western spadefoot (Spea hammondii) in southern California is of concern to state and federal wildlife agencies. Species distribution models (SDMs) can aid in determining the conservation status of imperiled species by projecting where suitable habitat remains and how much is protected from further development. We built SDMs that integrated site-occupancy data from systematic pitfall trapping surveys and presence-only data from biodiversity databases and citizen science platforms to project the current distribution of western spadefoots in southern California. Western spadefoot occurrence was positively related to the cover of grassland or shrub/scrub and the % sand in the soil within a 1000 m buffer, and was negatively related to slope, elevation, and distance to ephemeral streams or vernal pools. Most of the remaining unprotected habitat for western spadefoots is in the southern half of its historical range in western San Diego and Riverside counties. A few large tracts of spadefoot habitat exist on U.S. Department of Defense lands and smaller tracts remain on ecological reserves owned by state and local government agencies. Only small patches of habitat remain in the northern half of this clade’s historical range in Ventura, Orange, Los Angeles, and San Bernardino counties. Existing regional conservation plans provide ostensible spatial coverage of the majority of extant habitat for western spadefoots in southern California, but most of the habitat within the jurisdiction of these plans lacks formal protection, exposing this species to further declines as urbanization continues in the 21st century.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01944","usgsCitation":"Rose, J.P., Halstead, B., Packard, R.H., and Fisher, R., 2022, Projecting the remaining habitat for the western spadefoot (Spea hammondii) in heavily urbanized southern California: Global Ecology and Conservation, v. 33, e01944, 16 p., https://doi.org/10.1016/j.gecco.2021.e01944.","productDescription":"e01944, 16 p.","ipdsId":"IP-123687","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01944","text":"Publisher Index Page"},{"id":486326,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YYKW1H","text":"USGS data release","linkHelpText":"Code to fit integrated Species Distribution Models to occurrence data for the Western Spadefoot (Spea hammondii) in Southern California."},{"id":414543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"southern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.03709900369654,\n 32.618965807566894\n ],\n [\n -116.03709900369654,\n 34.57584559465637\n ],\n [\n -118.93425527477612,\n 34.57584559465637\n ],\n [\n -118.93425527477612,\n 32.618965807566894\n ],\n [\n -116.03709900369654,\n 32.618965807566894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Packard, Robert H.","contributorId":303286,"corporation":false,"usgs":false,"family":"Packard","given":"Robert","email":"","middleInitial":"H.","affiliations":[{"id":65748,"text":"Western Riverside County Multiple Species Habitat Conservation Plan","active":true,"usgs":false}],"preferred":false,"id":867028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867029,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229725,"text":"70229725 - 2022 - Influence of seasonal extreme flows on Brook Trout recruitment","interactions":[],"lastModifiedDate":"2022-03-16T16:35:05.386435","indexId":"70229725","displayToPublicDate":"2021-11-27T11:31:14","publicationYear":"2022","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":"Influence of seasonal extreme flows on Brook Trout recruitment","docAbstract":"

Populations of Brook Trout Salvelinus fontinalis exhibit large variation in annual recruitment (abundance of young of the year [age 0]), which is likely a product of density-dependent and density-independent factors. Quantifying the importance of each of these mechanisms in regulating Brook Trout recruitment would be valuable to managers that are responsible for the conservation of this iconic species throughout its native range. We analyzed a time series of age-0 and adult Brook Trout density data collected from 10 streams in the Sinnemahoning Creek watershed, north-central Pennsylvania (2010–2019), using Bayesian hierarchical modeling to partition the density-dependent effects of adult density and the density-independent effects of elevated streamflow on Brook Trout recruitment. Multiple models were examined, and the top-ranked model showed that Brook Trout recruitment followed a Ricker stock–recruitment relationship, with annual recruitment negatively influenced by maximum streamflow during the spring season (March–April). This model will be useful in predicting future variation in Brook Trout recruitment under climate change scenarios in which the frequency and intensity of high-flow events are expected to increase.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10347","usgsCitation":"Sweka, J., and Wagner, T., 2022, Influence of seasonal extreme flows on Brook Trout recruitment: North American Journal of Fisheries Management, v. 151, no. 2, p. 231-244, https://doi.org/10.1002/tafs.10347.","productDescription":"14 p.","startPage":"231","endPage":"244","ipdsId":"IP-130483","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Sinnemahoning Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.64013671875,\n 41.22824901518529\n ],\n [\n -77.67333984375,\n 41.22824901518529\n ],\n [\n -77.67333984375,\n 41.96765920367816\n ],\n [\n -78.64013671875,\n 41.96765920367816\n ],\n [\n -78.64013671875,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweka, John A.","contributorId":288581,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838107,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226719,"text":"70226719 - 2022 - Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology","interactions":[],"lastModifiedDate":"2022-01-25T17:28:32.758596","indexId":"70226719","displayToPublicDate":"2021-11-27T06:56:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9941,"text":"Bulletin Ecological Society of America","active":true,"publicationSubtype":{"id":10}},"title":"Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology","docAbstract":"Landscape connectivity is vital not only spatially, but also temporally; as ecosystems change, it is important to be aware of past, present, and future variables that may impact ecosystem function and biodiversity. As climate and environments continue to change, choosing appropriate restoration targets is becoming more challenging. By considering the paleoecological and paleoenvironmental record for a given region, restoration practitioners are not only able to bear witness to that region’s dynamic history, but also potentially identify multiple, alternative natural ecosystem states. Indeed, one of the deliverables of conservation paleobiology, a field that applies paleontological data and methods to present-day conservation, is to inform restoration targets. Consideration of future change is equally important, and paleoecological and paleoclimatological data are essential for informing models that can help us understand how climate change is affecting species and ecosystems at different temporal scales. The symposium “A dynamic perspective on ecosystem restoration: Establishing temporal\nconnectivity at the intersection between paleoecology and restoration ecology” gathered representatives from macroecology, paleoecology, and restoration ecology to share their perspectives on temporal connectivity and how consideration of an ecosystem’s past, present, and future can positively impact restoration and conservation. Some speakers approached the topic theoretically, while others considered it from a more practical and applied standpoint. The goals of the symposium were to build a stronger relationship among the subdisciplines, stimulate new ideas, and identify data and/or products that would be useful to share across subdisciplines.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/bes2.1954","usgsCitation":"Reid, R., McGuire, J., Svenning, J., Wingard, G.L., and Moreno-Mateos, D., 2022, Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology: Bulletin Ecological Society of America, v. 103, no. 1, e01954, 6 p., https://doi.org/10.1002/bes2.1954.","productDescription":"e01954, 6 p.","ipdsId":"IP-134688","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467212,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/bes2.1954","text":"External Repository"},{"id":392566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Reid, Rachel","contributorId":269802,"corporation":false,"usgs":false,"family":"Reid","given":"Rachel","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":827949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jenny","contributorId":269803,"corporation":false,"usgs":false,"family":"McGuire","given":"Jenny","email":"","affiliations":[{"id":56035,"text":"GA Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":827950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Svenning, Jens-Christiane","contributorId":269804,"corporation":false,"usgs":false,"family":"Svenning","given":"Jens-Christiane","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":827951,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wingard, G. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":827952,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moreno-Mateos, David","contributorId":269806,"corporation":false,"usgs":false,"family":"Moreno-Mateos","given":"David","email":"","affiliations":[{"id":16810,"text":"Harvard Univ.","active":true,"usgs":false}],"preferred":false,"id":827953,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229223,"text":"70229223 - 2022 - Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community","interactions":[],"lastModifiedDate":"2022-03-03T16:58:26.697189","indexId":"70229223","displayToPublicDate":"2021-11-25T10:46:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community","docAbstract":"

Disturbances such as fire provide an opportunity for invasive plant species to exploit newly created niche space. Whether initial invaders facilitate, compete with, or do not affect later invaders is important to determine in communities affected by multiple invaders. This analysis focuses on the newer invaders Taeniatherum caput-medusae (medusahead) and Ventenata dubia (ventenata) in sagebrush-steppe communities previously invaded by Bromus tectorum (cheatgrass), during the first 5 years of recovery after wildfire. We combined probabilistic co-occurrence analysis and Getis-Ord spatial clustering analysis to assess relationships between different exotic annual grass species and native and introduced perennial bunchgrasses, then used Bayesian generalized linear models to determine if and how medusahead and ventenata differed in their environmental relationships and thus invasion niches. Medusahead presence was positively associated with both other exotic annual grasses, but ventenata presence was negatively associated with cheatgrass presence. Medusahead hotspots were more spatially similar to cheatgrass hotspots while ventenata hotspots were unique. Both invaders were negatively related to total perennial bunchgrass cover but disassociations between invaders and different perennial bunchgrasses were species-specific. Medusahead and ventenata occupied different niches; medusahead in low elevation, low precipitation areas and ventenata in higher elevation, higher precipitation areas. Despite seemingly similar ecology and growth requirements among these annual grasses and a tendency to be considered uniformly in both research and management, the species appeared to have different invasion niches.

","language":"English","publisher":"Springer Link","doi":"10.1007/s10530-021-02669-3","usgsCitation":"Applestein, C., and Germino, M., 2022, Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community: Biological Invasions, v. 24, p. 741-759, https://doi.org/10.1007/s10530-021-02669-3.","productDescription":"19 p.","startPage":"741","endPage":"759","ipdsId":"IP-131007","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":436038,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TG16C5","text":"USGS data release","linkHelpText":"Presence and cover of exotic annual and perennial grass species during five years post-fire on the Soda Wildfire"},{"id":396709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Owyhee Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.564453125,\n 42.00848901572399\n ],\n [\n -116.20239257812499,\n 42.00848901572399\n ],\n [\n -116.20239257812499,\n 44.12702800650004\n ],\n [\n -118.564453125,\n 44.12702800650004\n ],\n [\n -118.564453125,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":836972,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230210,"text":"70230210 - 2022 - Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","interactions":[],"lastModifiedDate":"2023-06-09T13:55:46.784692","indexId":"70230210","displayToPublicDate":"2021-11-25T10:23:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","title":"Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation","docAbstract":"

Estimates of parameters that affect population dynamics, including the size at which individuals reproduce, are crucial for efforts aimed at understanding how imperiled species may recover from the numerous threats they face. In this study, we observed loggerhead marine turtles (Caretta caretta) nesting at three sites in the Gulf of Mexico at sizes assumed nonreproductive in this region (≤87 cm curved carapace length-notch [CCL-n]). These smaller individuals ranged from 74.0 to 86.9 cm CCL-n, and the proportion of smaller nesting loggerheads was 0.13 across three study sites: Gulf Shores, AL; Dry Tortugas National Park, Florida (FL); and Everglades National Park (ENP), FL. The greatest proportion of smaller nesters was observed at ENP at 0.24. Tracking data indicated that the smaller nesters migrated shorter distances and swam in shallower waters compared to the larger nesting loggerheads (>87 cm CCL-n) in our dataset. These results provide valuable information on two of the smallest subpopulations of NW Atlantic loggerheads and understudied ENP turtles. Our results have potential applications in the classification and interpretation of stranding limits and bycatch estimates, population modeling (e.g., stage durations and fecundity), and understanding threats and subpopulation recovery efforts for multiple subpopulations of this imperiled species.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.581","usgsCitation":"Benscoter, A., Smith, B., and Hart, K., 2022, Loggerhead marine turtles (Caretta caretta) nesting at smaller sizes than expected in the Gulf of Mexico: Implications for turtle behavior, population dynamics, and conservation: Conservation Science and Practice, v. 4, no. 1, e581, 14 p.; Data Release, https://doi.org/10.1111/csp2.581.","productDescription":"e581, 14 p.; Data Release","ipdsId":"IP-128726","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":449512,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.581","text":"Publisher Index Page"},{"id":398119,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417872,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96S4B8P"}],"country":"United States","state":"Alabama, Florida","city":"Gulf Shores","otherGeospatial":"Dry Tortugas National Park, Gulf of Mexico, Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.02108764648438,\n 30.181934730780572\n ],\n [\n -87.506103515625,\n 30.181934730780572\n ],\n [\n -87.506103515625,\n 30.349176094149833\n ],\n [\n -88.02108764648438,\n 30.349176094149833\n ],\n [\n -88.02108764648438,\n 30.181934730780572\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.4034423828125,\n 25.348990395713393\n ],\n [\n -80.947265625,\n 25.353954558526684\n ],\n [\n -81.3812255859375,\n 25.997549919572112\n ],\n [\n -81.8646240234375,\n 26.017297563851745\n ],\n [\n -81.419677734375,\n 25.502784548755354\n ],\n [\n -81.1505126953125,\n 25.075648445630527\n ],\n [\n -80.738525390625,\n 25.015928763367857\n ],\n [\n -80.3924560546875,\n 25.19002975536265\n ],\n [\n -80.3594970703125,\n 25.304303764403617\n ],\n [\n -80.3814697265625,\n 25.329131707091477\n ],\n [\n -80.4034423828125,\n 25.348990395713393\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.01544189453125,\n 24.540877160098404\n ],\n [\n -82.75314331054688,\n 24.540877160098404\n ],\n [\n -82.75314331054688,\n 24.729369599118222\n ],\n [\n -83.01544189453125,\n 24.729369599118222\n ],\n [\n -83.01544189453125,\n 24.540877160098404\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Benscoter, Allison 0000-0003-4205-3808","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":216194,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":839564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218324,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839565,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227187,"text":"70227187 - 2022 - Calcareous plankton biostratigraphic fidelity and species richness during the last 10 m.y. of the Cretaceous at Blake Plateau, subtropical North Atlantic","interactions":[],"lastModifiedDate":"2022-01-04T14:53:03.822031","indexId":"70227187","displayToPublicDate":"2021-11-25T08:48:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1344,"text":"Cretaceous Research","active":true,"publicationSubtype":{"id":10}},"title":"Calcareous plankton biostratigraphic fidelity and species richness during the last 10 m.y. of the Cretaceous at Blake Plateau, subtropical North Atlantic","docAbstract":"

Species distributions of well-preserved and diverse assemblages of planktonic foraminifera and calcareous nannofossils spanning the last 10 m.y. of the Cretaceous (middle Campanian through Maastrichtian) are analyzed from samples taken across a 1400 m depth transect at Blake Nose in the western subtropical North Atlantic (Ocean Drilling Program Sites 1049, 1050 and 1052). Age models constructed by integrating foraminiferal, calcareous nannofossil, and magnetic polarity datum events provide a reliable framework for temporal correlation of the sites. This framework enables comparisons of species richness and abundance among sites and evaluation of the reliability of first and last appearance datums for regional and global correlation. Among the standard primary zonal marker datums, six of nine planktonic foraminifer and six of seven calcareous nannofossil events are considered reliable for constraining the age-depth models. Secondary datum ages calculated for 17 planktonic foraminiferal events suggest correlation offsets among the three sites of <0.1 m.y. for four species (including Pseudoguembelina praehariaensis Tur and Huber n. sp.), 0.1–0.5 m.y. for six events, and 0.6–1.0 m.y. for eight species. Secondary calcareous nannofossil datum ages calculated for six species show less reliability, with offsets of <0.1 m.y. for one species, 0.7 m.y. for one species, and 1.0–1.5 m.y. for the remaining four species. The distinctly identifiable new species Trinitella suturis Tur and Huber has lowest occurrences that are diachronous by as much as 1.73 m.y. among the Blake Nose sites. Occurrence rarity is the most likely explanation for the age offsets of this and other diachronous species.

Planktonic foraminiferal assemblages show no significant differences in species composition and relative abundance among the three sites, suggesting the sites were all located below a single oligotrophic surface water mass. Species richness counts pooled in 200 kyr bins for the Blake Nose sites reveals high species origination rates from the late Campanian through early Maastrichtian and highest species richness (51–63 species) during the Maastrichtian. The only significant extinction pulse during the mid-Campanian through Maastrichtian occurs between 66.2 and 66.4 Ma with loss of five species representing ∼9% of the assemblage. These extinctions occur at the same time as a globally recognized warming event correlated with a pulse of eruptions at the Deccan Traps in India.

Calcareous nannofossil assemblages show no significant change in relative abundance among the three sites. Two significant extinction events are documented: one from 75.20 to 75.40 Ma with a loss of six species representing ∼6% of the assemblage and one from 66.2 to 66.4 Ma with a loss of nine species representing ∼9% of the assemblage. The former event is associated with a hiatus at the base of Zone CC24, and the latter corresponds to Deccan Trap warming.

Hiatuses are identified at all three Blake Nose sites near the base of the Maastrichtian (∼71.5 Ma; lowermost Pseudoguembelina palpebra/CC24 Zone) and only at the deeper Sites 1049 and 1050 in the mid-Maastrichtian (∼67.2 Ma; Pseudoguembelina hariaensis/CC26b Zone) and the mid-Campanian (∼75.9 Ma; base of Radotruncana calcarata/CC22 Zone). Slumping from across the shelf and slope could have caused the early Maastrichtian hiatus while changes in the pattern and strength of deep-water circulation may have been responsible for the mid-Maastrichtian and mid-Campanian hiatuses.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.cretres.2021.105095","usgsCitation":"Huber, B.T., Tur, N.A., Self-Trail, J., and MacLeod, K.G., 2022, Calcareous plankton biostratigraphic fidelity and species richness during the last 10 m.y. of the Cretaceous at Blake Plateau, subtropical North Atlantic: Cretaceous Research, v. 131, 105095, 42 p., https://doi.org/10.1016/j.cretres.2021.105095.","productDescription":"105095, 42 p.","ipdsId":"IP-132563","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":449513,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cretres.2021.105095","text":"Publisher Index Page"},{"id":393850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Blake Plateau, subtropical North Atlantic","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77,\n 29.5\n ],\n [\n -76,\n 29.5\n ],\n [\n -76,\n 30.5\n ],\n [\n -77,\n 30.5\n ],\n [\n -77,\n 29.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, Brian T.","contributorId":270771,"corporation":false,"usgs":false,"family":"Huber","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":36858,"text":"Smithsonian","active":true,"usgs":false}],"preferred":false,"id":830008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tur, Nataliya A.","contributorId":270772,"corporation":false,"usgs":false,"family":"Tur","given":"Nataliya","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":830010,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacLeod, Kenneth G.","contributorId":270773,"corporation":false,"usgs":false,"family":"MacLeod","given":"Kenneth","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":830011,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227326,"text":"70227326 - 2022 - Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","interactions":[],"lastModifiedDate":"2022-01-10T12:59:36.251299","indexId":"70227326","displayToPublicDate":"2021-11-24T06:56:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA","docAbstract":"

Factors affecting groundwater quality used for domestic supply within the Marcellus Shale footprint in north-central and north-east Pennsylvania are identified using a combination of spatial, statistical, and geochemical modeling. Untreated groundwater, sampled during 2011–2017 from 472 domestic wells within the study area, exhibited wide ranges in pH (4.5–9.3), total dissolved solids (TDS, 22–1960 mg/L), sodium (0.3–760 mg/L), chloride (0.3–1020 mg/L), bromide (<0.01–8.6 mg/L), and methane (<0.001–77 mg/L). The wells had depths ranging from 10 to 394 m; 69.5 percent were completed in sandstone bedrock, 19.3 percent in shale, 4.2 percent in siltstone, 4 percent in carbonate, and 3 percent in unconsolidated alluvial or glacial deposits. Groundwater quality in the Delaware River watershed, in the eastern part of the study area where Marcellus gas has not been developed, was similar to that in the Susquehanna, Allegheny, and Genesee River watersheds in the western part of the study area where natural gas production from Marcellus Shale has been ongoing since 2008. Most groundwaters were calcium/bicarbonate type with near-neutral pH; approximately 10 percent were sodium/bicarbonate and 1 percent were sodium/chloride types. Sodium-enriched waters, which were mostly from shale and siltstone aquifers, had the greatest frequency of elevated pH (>8.5) and elevated concentrations of TDS (>250 mg/L), bromide (>0.15 mg/L), methane (>7.0 mg/L), and lithium (>60 μg/L). Geochemical models indicate these characteristics could result from progressive mineral dissolution combined with cation exchange, plus mixing with locally important salinity sources, including as much as 0.7 percent Appalachian Basin brine and/or road-deicing salt. Multivariate correlation models suggest the observed variability in methane concentrations may be attributed to several environmental factors, such as geochemical evolution along groundwater flow paths, redox conditions, and/or mixing with saline groundwater or brine. Most samples having elevated methane were from shale aquifers, which were mainly in the Susquehanna River basin and had the greatest density of gas wells compared to other lithologies. Samples having elevated methane were also observed in the Delaware River watershed and other areas outside gas development. Isotopic compositions of methane for a subset of 39 samples (selected because of elevated methane) and relatively high ratios of methane to ethane in those samples indicated methane could be derived from microbial gas mixed with thermogenic gas that may have undergone degradation and/or fractionation during migration. The methods used in this study could be broadly applicable to understanding major factors affecting groundwater quality, particularly for explaining variations in ionic composition with pH and identifying sources of salinity and associated constituents (e.g. sodium, chloride, bromide, lithium, methane) that may have geogenic or anthropogenic origins.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2021.105149","usgsCitation":"Cravotta, C., Senior, L.A., and Conlon, M.D., 2022, Factors Affecting Groundwater Quality Used for Domestic Supply in Marcellus Shale Region of North-Central and North-East Pennsylvania, USA: Applied Geochemistry, v. 137, 105149, 19 p., https://doi.org/10.1016/j.apgeochem.2021.105149.","productDescription":"105149, 19 p.","ipdsId":"IP-129093","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":449515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2021.105149","text":"Publisher Index Page"},{"id":394090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Marcellus Shale region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.640625,\n 42.06560675405716\n ],\n [\n -76.728515625,\n 40.91351257612758\n ],\n [\n -75.0146484375,\n 40.94671366508002\n ],\n [\n -74.8828125,\n 41.21172151054787\n ],\n [\n -74.8388671875,\n 41.44272637767212\n ],\n [\n -75.1904296875,\n 42.032974332441405\n ],\n [\n -76.640625,\n 42.06560675405716\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"137","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senior, Lisa A. 0000-0003-2629-1996 lasenior@usgs.gov","orcid":"https://orcid.org/0000-0003-2629-1996","contributorId":2150,"corporation":false,"usgs":true,"family":"Senior","given":"Lisa","email":"lasenior@usgs.gov","middleInitial":"A.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conlon, Matthew D. 0000-0001-8266-9610 mconlon@usgs.gov","orcid":"https://orcid.org/0000-0001-8266-9610","contributorId":201291,"corporation":false,"usgs":true,"family":"Conlon","given":"Matthew","email":"mconlon@usgs.gov","middleInitial":"D.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830477,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236746,"text":"70236746 - 2022 - A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: Reply","interactions":[],"lastModifiedDate":"2022-09-19T11:54:02.485184","indexId":"70236746","displayToPublicDate":"2021-11-20T06:52:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: Reply","docAbstract":"

No abstract available.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3593","usgsCitation":"Schoolmaster, D., Zirbel, C.R., and Cronin, J.P., 2022, A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: Reply: Ecology, v. 103, no. 2, e03593, 17 p., https://doi.org/10.1002/ecy.3593.","productDescription":"e03593, 17 p.","ipdsId":"IP-129779","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":406942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolmaster, Donald 0000-0003-0910-4458","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":202356,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":852077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zirbel, Chad R 0000-0002-9289-1722","orcid":"https://orcid.org/0000-0002-9289-1722","contributorId":224302,"corporation":false,"usgs":false,"family":"Zirbel","given":"Chad","email":"","middleInitial":"R","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":852078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cronin, James P. 0000-0001-6791-5828 jcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-6791-5828","contributorId":5834,"corporation":false,"usgs":true,"family":"Cronin","given":"James","email":"jcronin@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":852079,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226953,"text":"70226953 - 2022 - Correspondence analysis for mineral commodity research: An example workflow for mineralized calderas, southwest United States","interactions":[],"lastModifiedDate":"2022-03-15T16:40:05.579972","indexId":"70226953","displayToPublicDate":"2021-11-19T07:08:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Correspondence analysis for mineral commodity research: An example workflow for mineralized calderas, southwest United States","docAbstract":"

Historical mine and mineral deposit datasets are routinely used to inform quantitative mineral assessment models, but they also can contain a wealth of supplementary qualitative information that is generally underutilized. We present a workflow that uses correspondence analysis, an exploratory tool commonly applied to multivariate abundance data, to better utilize qualitative data in these historical datasets. The workflow involves extraction of qualitative information on ore mineralogy from a mineral deposit database, attaches those data to a target geological feature, and analyzes the underlying data structure with correspondence analysis and hierarchical clustering. The output of correspondence analysis is inversely weighted to the relative frequency of ore minerals, and therefore rare mineral species (i.e., those with unusually low frequencies) can disproportionately contribute to the total variance of the dataset. We present a novel technique for aggregating frequencies of rare mineral species that minimizes this effect. We apply this workflow to evaluate how ore mineral assemblages in former and active mines vary in spatial relation to silicic calderas in the southwestern United States. The most common ore mineral associations observed spatially and genetically associated to calderas include those related to polymetallic, base metal-rich systems and epithermal Au–Ag systems. Three other groups of mineralized calderas were identified, including: (1) Hg–Sb mineralized calderas in the northern Great Basin and western Nevada volcanic field; (2) calderas associated with elevated abundances of Mn oxides/hydroxides, fluorite, and Be-minerals, mostly in eastern Utah and New Mexico; and (3) calderas with numerous U ± F deposits, which are located in central Colorado, the eastern Great Basin and in northern Nevada. The latter three groups are associated with economically significant critical mineral resources, including the Li resources of the McDermitt complex and Be associated with the Spor Mountain on the margin of the Thomas caldera complex. We conclude that correspondence analysis is a promising technique that can enhance data exploration of the qualitative information held within mineral deposit datasets. Consequently, it could have numerous applications for mineral potential mapping, resource assessment projects, and characterization of mineral systems.


","language":"English","publisher":"Springer","doi":"10.1007/s11053-021-09963-w","usgsCitation":"Rosera, J.M., and Coleman, D.S., 2022, Correspondence analysis for mineral commodity research: An example workflow for mineralized calderas, southwest United States: Natural Resources Research, v. 31, p. 9-36, https://doi.org/10.1007/s11053-021-09963-w.","productDescription":"28 p.","startPage":"9","endPage":"36","ipdsId":"IP-130118","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":393297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.541015625,\n 31.12819929911196\n ],\n [\n -102.48046875,\n 31.12819929911196\n ],\n [\n -102.48046875,\n 42.16340342422401\n ],\n [\n -124.541015625,\n 42.16340342422401\n ],\n [\n -124.541015625,\n 31.12819929911196\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosera, Joshua Mark 0000-0003-3807-5000","orcid":"https://orcid.org/0000-0003-3807-5000","contributorId":270284,"corporation":false,"usgs":true,"family":"Rosera","given":"Joshua","email":"","middleInitial":"Mark","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":828923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coleman, Drew S","contributorId":192880,"corporation":false,"usgs":false,"family":"Coleman","given":"Drew","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":828924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226722,"text":"70226722 - 2022 - Identifying factors that affect mountain lake sensitivity to atmospheric nitrogen deposition across multiple scales","interactions":[],"lastModifiedDate":"2021-12-07T12:54:56.960495","indexId":"70226722","displayToPublicDate":"2021-11-19T06:46:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Identifying factors that affect mountain lake sensitivity to atmospheric nitrogen deposition across multiple scales","docAbstract":"

Increased nitrogen (N) deposition rates over the past century have affected both North American and European mountain lake ecosystems. Ecological sensitivity of mountain lakes to N deposition varies, however, because chemical and biological responses are modulated by local watershed and lake properties. We evaluated predictors of mountain lake sensitivity to atmospheric N deposition across North American and European mountain ranges and included as response variables dissolved inorganic N (DIN = N\"singleNH4+ + N\"singleNO3–) concentrations and phytoplankton biomass. Predictors of these responses were evaluated at three different spatial scales (hemispheric, regional, subregional) using regression tree, random forest, and generalized additive model (GAM) analysis. Analyses agreed that Northern Hemisphere mountain lake DIN was related to N deposition rates and smaller scale spatial variability (e.g., regional variability between North American and European lakes, and subregional variability between mountain ranges). Analyses suggested that DIN, N deposition, and subregional variability were important for Northern Hemisphere mountain lake phytoplankton biomass. Together, these findings highlight the need for finer-scale, subregional analyses (by mountain range) of lake sensitivity to N deposition. Subregional analyses revealed differences in predictor variables of lake sensitivity. In addition to N deposition rates, lake and watershed features such as land cover, bedrock geology, maximum lake depth (Zmax), and elevation were common modulators of lake DIN. Subregional phytoplankton biomass was consistently positively related with total phosphorus (TP) in Europe, while North American locations showed variable relationships with N or P. This study reveals scale-dependent watershed and lake characteristics modulate mountain lake ecological responses to atmospheric N deposition and provides important context to inform empirically based management strategies.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117883","usgsCitation":"Burpee, B., Saros, J., Nanus, L., Baron, J., Brahney, J., Christianson, K., Gantz, T., Heard, A., Hundey, B., Koinig, K., Kopacek, J., Moser, K., Nydick, K., Oleksy, I., Sadro, S., Sommaruga, R., Vinebrooke, R., and Williams, J., 2022, Identifying factors that affect mountain lake sensitivity to atmospheric nitrogen deposition across multiple scales: Water Research, v. 209, 117883, 13 p., https://doi.org/10.1016/j.watres.2021.117883.","productDescription":"117883, 13 p.","ipdsId":"IP-129777","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":392565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"209","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burpee, Benjamin","contributorId":269807,"corporation":false,"usgs":false,"family":"Burpee","given":"Benjamin","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":827955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saros, Jasmine","contributorId":269808,"corporation":false,"usgs":false,"family":"Saros","given":"Jasmine","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":827956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nanus, Leora","contributorId":269809,"corporation":false,"usgs":false,"family":"Nanus","given":"Leora","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":827957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":827959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Christianson, Kyle","contributorId":269811,"corporation":false,"usgs":false,"family":"Christianson","given":"Kyle","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":827960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gantz, Taylor","contributorId":269812,"corporation":false,"usgs":false,"family":"Gantz","given":"Taylor","email":"","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":827961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Heard, Andi","contributorId":269813,"corporation":false,"usgs":false,"family":"Heard","given":"Andi","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":827962,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hundey, Beth","contributorId":269814,"corporation":false,"usgs":false,"family":"Hundey","given":"Beth","email":"","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":827963,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koinig, Karin","contributorId":269815,"corporation":false,"usgs":false,"family":"Koinig","given":"Karin","email":"","affiliations":[{"id":17993,"text":"University of Innsbruck","active":true,"usgs":false}],"preferred":false,"id":827964,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kopacek, Jiri","contributorId":269817,"corporation":false,"usgs":false,"family":"Kopacek","given":"Jiri","email":"","affiliations":[{"id":56037,"text":"České Budějovice, Czech Republic","active":true,"usgs":false}],"preferred":false,"id":827965,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moser, Katrina","contributorId":269819,"corporation":false,"usgs":false,"family":"Moser","given":"Katrina","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":827966,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Nydick, Koren","contributorId":269821,"corporation":false,"usgs":false,"family":"Nydick","given":"Koren","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":827967,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Oleksy, Isabella A.","contributorId":269822,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella A.","affiliations":[{"id":33412,"text":"Cary Institute for Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":827968,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Sadro, Steven","contributorId":269824,"corporation":false,"usgs":false,"family":"Sadro","given":"Steven","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":827969,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sommaruga, Ruben","contributorId":269827,"corporation":false,"usgs":false,"family":"Sommaruga","given":"Ruben","email":"","affiliations":[{"id":17993,"text":"University of Innsbruck","active":true,"usgs":false}],"preferred":false,"id":827970,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Vinebrooke, Rolf","contributorId":269829,"corporation":false,"usgs":false,"family":"Vinebrooke","given":"Rolf","email":"","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":827971,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Williams, Jason","contributorId":269831,"corporation":false,"usgs":false,"family":"Williams","given":"Jason","affiliations":[{"id":6912,"text":"Idaho Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":827972,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70226560,"text":"70226560 - 2022 - Predicting coastal impacts by wave farms: A comparison of wave-averaged and wave-resolving models","interactions":[],"lastModifiedDate":"2021-11-29T11:59:05.179476","indexId":"70226560","displayToPublicDate":"2021-11-19T05:56:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9933,"text":"Renewable Energy","active":true,"publicationSubtype":{"id":10}},"title":"Predicting coastal impacts by wave farms: A comparison of wave-averaged and wave-resolving models","docAbstract":"

Wave energy converters (WECs) will have to be arranged into arrays of many devices to extract commercially viable amounts of energy. To understand the potential coastal impacts of WEC arrays, most research to date has relied on wave-averaged models given their computational efficiency. However, it is unknown how accurate wave-averaged model predictions are given a lack of validation data and their inherent simplifications of various hydrodynamic processes (e.g., diffraction). This paper compares the predictions of coastal wave farm impacts from a coupled wave-averaged and flow model (Delft3D-SNL-SWAN), to a wave-resolving wave-flow model (SWASH) that intrinsically accounts for more of the relevant physics. Model predictions were compared using an idealized coastal bathymetry over a range of wave conditions and wave farm geometries. Both models predicted the largest impacts (changes to the nearshore hydrodynamics) for large and dense wave farms located close to the shore (1 km) and the smallest impacts for the small and widely spaced farm at a greater offshore distance (3 km). However, the wave-resolving model generally predicted somewhat larger impacts (i.e., changes to the nearshore wave heights, mean velocities and mean water levels). We also found that coupling the wave-averaged model to a flow model resulted in more realistic downstream predictions than the stand-alone wave-averaged model.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.renene.2021.11.048","usgsCitation":"David, D.R., Rijnsdorp, D.P., Hansen, J., Lowe, R.J., and Buckley, M.L., 2022, Predicting coastal impacts by wave farms: A comparison of wave-averaged and wave-resolving models: Renewable Energy, v. 183, p. 764-780, https://doi.org/10.1016/j.renene.2021.11.048.","productDescription":"17 p.","startPage":"764","endPage":"780","ipdsId":"IP-127957","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":392172,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"David, Daniel R.","contributorId":269522,"corporation":false,"usgs":false,"family":"David","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":24588,"text":"The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":827356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rijnsdorp, Dirk P.","contributorId":261463,"corporation":false,"usgs":false,"family":"Rijnsdorp","given":"Dirk","email":"","middleInitial":"P.","affiliations":[{"id":17614,"text":"Delft University of Technology","active":true,"usgs":false}],"preferred":false,"id":827357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Jeff E.","contributorId":146437,"corporation":false,"usgs":false,"family":"Hansen","given":"Jeff E.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":827358,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowe, Ryan J.","contributorId":152265,"corporation":false,"usgs":false,"family":"Lowe","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":827359,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buckley, Mark L. 0000-0002-1909-4831","orcid":"https://orcid.org/0000-0002-1909-4831","contributorId":203481,"corporation":false,"usgs":true,"family":"Buckley","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":827360,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229753,"text":"70229753 - 2022 - Combining fixed-location count data and movement data to estimate abundance of a lake sturgeon spawning run","interactions":[],"lastModifiedDate":"2022-06-01T15:13:31.917051","indexId":"70229753","displayToPublicDate":"2021-11-18T10:02:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Combining fixed-location count data and movement data to estimate abundance of a lake sturgeon spawning run","docAbstract":"

Estimating abundance of migrating fishes is challenging. While sonars can be deployed continuously, improper assumptions about unidirectional migration and complete spatial coverage can lead to inaccurate estimates. To address these challenges, we present a framework for combining fixed-location count data from a dual-frequency identification sonar (DIDSON) with movement data from acoustic telemetry to estimate spawning run abundance of lake sturgeon (Acipenser fulvescens). Acoustic telemetry data were used to estimate the probability of observing a lake sturgeon on the DIDSON and to determine the probability that a lake sturgeon passing the DIDSON site had passed the site previously during the season. Combining probabilities with DIDSON counts, using a Bayesian integrated model, we estimated the following abundances: 99 (42–215 credible interval, CI) in 2017, 131 (82–248 CI) in 2018, and 92 (47–184 CI) in 2019. Adding movement data generated better inferences on count data by incorporating fish behavior (e.g., multiple migrations in a single season) and its uncertainty into abundance estimates. This framework can be applied to count and movement data to estimate abundance of spawning runs of other migratory fishes in riverine systems.

","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2021-0140","usgsCitation":"Izzo, L., Zydlewski, G.B., and Parrish, D.L., 2022, Combining fixed-location count data and movement data to estimate abundance of a lake sturgeon spawning run: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 6, p. 925-935, https://doi.org/10.1139/cjfas-2021-0140.","productDescription":"11 p.","startPage":"925","endPage":"935","ipdsId":"IP-127748","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449533,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/51632","text":"External Repository"},{"id":397240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Winooski River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.28910827636717,\n 44.3768766587829\n ],\n [\n -72.93342590332031,\n 44.3768766587829\n ],\n [\n -72.93342590332031,\n 44.54448397425684\n ],\n [\n -73.28910827636717,\n 44.54448397425684\n ],\n [\n -73.28910827636717,\n 44.3768766587829\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":288673,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":838211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zydlewski, Gayle Barbin","contributorId":288674,"corporation":false,"usgs":false,"family":"Zydlewski","given":"Gayle","email":"","middleInitial":"Barbin","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":838212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parrish, Donna L. 0000-0001-9693-6329 dparrish@usgs.gov","orcid":"https://orcid.org/0000-0001-9693-6329","contributorId":138661,"corporation":false,"usgs":true,"family":"Parrish","given":"Donna","email":"dparrish@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838210,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226893,"text":"70226893 - 2022 - Directional selection shifts trait distributions of planted species in dryland restoration","interactions":[],"lastModifiedDate":"2022-03-28T16:31:35.115055","indexId":"70226893","displayToPublicDate":"2021-11-18T06:28:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Directional selection shifts trait distributions of planted species in dryland restoration","docAbstract":"
  1. The match between species trait values and local abiotic filters can restrict community membership. An often-implicit assumption of this relationship is that abiotic filters select for a single locally optimal strategy, though difficulty in isolating effects of the abiotic environment from those of dispersal limitation and biotic interactions has resulted in few empirical tests of this assumption. Similar constraints have made it difficult to assess whether the type and intensity of abiotic filters shift along gradients of environmental harshness, as predicted by the stress-dominance hypothesis.
  2. We planted 9,216 plants of 29 perennial grass and forb species that had a range of functional trait values and were assigned to a warm, intermediate or cool temperature tolerance pool across eight sites on the Colorado Plateau. We compared the distributions of traits of surviving individuals to null distributions to evaluate whether there were shifts in trait means and variation. Borrowing from phenotypic selection concepts in evolutionary biology, we assessed support for stabilizing, directional and disruptive abiotic filtering of trait distributions and whether these types of filtering varied with initial species pool.
  3. Functional composition was significantly different from null distributions for nearly all traits at all sites, with trait variation more restricted in harsher abiotic conditions, supporting the stress-dominance hypothesis. Contrary to expectations, we primarily found evidence for directional selection, which increased in frequency in warm species pools while disruptive selection was found more often in cool and intermediate species pools.
  4. Synthesis. This study provides a controlled experimental approach to test the effect of the abiotic environment on plant trait filtering. We found that opportunistic strategies allowing for rapid water acquisition during favourable periods improved survival at warmer sites. Species with these strategies may be expected to benefit from increasing aridity and may be selected for active management efforts. More generally, the prevalence of directional selection may have important implications for dynamic vegetation models that rely on trait distributions for translating environmental variation into ecosystem processes.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2745.13816","usgsCitation":"Balazs, K.R., Munson, S.M., Havrilla, C.A., and Butterfield, B.J., 2022, Directional selection shifts trait distributions of planted species in dryland restoration: Journal of Ecology, v. 110, no. 3, p. 540-552, https://doi.org/10.1111/1365-2745.13816.","productDescription":"13 p.","startPage":"540","endPage":"552","ipdsId":"IP-126175","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449538,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zenodo.org/record/5736447","text":"External Repository"},{"id":393088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"110","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Balazs, Kathleen R.","contributorId":223214,"corporation":false,"usgs":false,"family":"Balazs","given":"Kathleen","email":"","middleInitial":"R.","affiliations":[{"id":24810,"text":"Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":828669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":828670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Havrilla, Caroline Ann 0000-0003-3913-0980","orcid":"https://orcid.org/0000-0003-3913-0980","contributorId":228882,"corporation":false,"usgs":true,"family":"Havrilla","given":"Caroline","email":"","middleInitial":"Ann","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":828671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":828672,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265770,"text":"70265770 - 2022 - New insights on faulting and intrusion processes during the June 2007, East Rift Zone eruption of Kilauea volcano, Hawai'i","interactions":[],"lastModifiedDate":"2025-04-16T13:17:34.472644","indexId":"70265770","displayToPublicDate":"2021-11-12T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"New insights on faulting and intrusion processes during the June 2007, East Rift Zone eruption of Kilauea volcano, Hawai'i","docAbstract":"

The East Rift Zone (ERZ) of Kīlauea Volcano, Hawai'i, represents one of the most volcanically active regions in the world. The 2007 Father's Day (FD) dike intrusion, eruption, and accompanying slow-slip event (SSE) has been previously modeled using geodetic data to constrain the geometry of the intrusion and the timing and magnitude of the SSE. Here, we perform inversions of three interferometric synthetic aperture radar (InSAR) datasets and a new intensity offset tracking dataset to assess the effect of integrating intensity cross-correlation offsets into inversion problems and explore additional potential models for the intrusion geometry of the FD event based on this additional data. The overall lowest misfit single Okada model for all datasets opens 2.3 m, strikes 73 degrees while dipping sub-vertically at 83 degrees, and extends approximately 2.9 km to the ENE and 2.4 km downdip. The differences are minor between complex en-echelon distributed Okada and decollement model of (Montgomery-Brown et al., 2010) or 3D-MBEM breaching models including multiple surface breaches and free-slipping decollement movement. Finally, we examine the static Coulomb stress changes for the proposed decollement fault created by our preferred model and a representative model of deep rift opening and find that deep rift zones dilation, not shallow ERZ intrusions, are likely modulating slip on the decollement.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107425","usgsCitation":"Leeburn, J., Wauthier, C., Montgomery-Brown, E.K., and Gonzalez-Santana, J., 2022, New insights on faulting and intrusion processes during the June 2007, East Rift Zone eruption of Kilauea volcano, Hawai'i: Journal of Volcanology and Geothermal Research, v. 421, 107425, 14 p., https://doi.org/10.1016/j.jvolgeores.2021.107425.","productDescription":"107425, 14 p.","ipdsId":"IP-125424","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488262,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2021.107425","text":"Publisher Index Page"},{"id":484584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2908512348302,\n 19.41341967491155\n ],\n [\n -155.2908512348302,\n 19.401793724963014\n ],\n [\n -155.27525765813795,\n 19.401793724963014\n ],\n [\n -155.27525765813795,\n 19.41341967491155\n ],\n [\n -155.2908512348302,\n 19.41341967491155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"421","noUsgsAuthors":false,"publicationDate":"2021-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Leeburn, J.","contributorId":353406,"corporation":false,"usgs":false,"family":"Leeburn","given":"J.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wauthier, C.","contributorId":353409,"corporation":false,"usgs":false,"family":"Wauthier","given":"C.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":933491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzalez-Santana, J.","contributorId":353412,"corporation":false,"usgs":false,"family":"Gonzalez-Santana","given":"J.","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":933492,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}