The Laurentian Great Lakes have experienced recent ecosystem changes that could lead to reductions in adaptive capacity and ultimately a loss of biodiversity and production throughout the food web. Observed changes in Great Lakes benthic communities include declines of native species and widespread success of invasive species like dreissenid mussels in all but Lake Superior. Understanding the ecology of native benthic deepwater preyfish and the reasons for their declines is important for predicting future losses in adaptive capacity and diversity, as well as managing the Great Lakes ecosystem to avoid such losses. Native sculpin species (Cottus bairdii, C. cognatus, C. ricei, Myoxocephalus thompsonii) historically were among the most abundant of the Great Lakes native deepwater benthic preyfish community and are an important link between offshore benthic and pelagic food webs. With one exception, these species have declined in abundance throughout the Great Lakes in recent years, but relatively little is known about their biology and ecology. This review synthesizes the available knowledge for the Great Lakes sculpin species and provides suggestions for future research efforts, which include understanding reproductive ecology and spawning behavior, connectivity and dispersal of populations, early life history, and influences of interactions with native and non-native species.
Estuarine food webs are fueled by multiple different primary producers. However, identifying the relative importance of each producer to consumers is difficult, particularly for fishes that utilize multiple food sources due to both their mobility and their generally high trophic levels. Previous studies have documented broad spatial differences in the importance of primary producers to fishes within the Upper San Francisco Estuary, California, including separation between pelagic and littoral food webs. In this study, we evaluated the importance of primary producers to adult fishes in three closely spaced subregions that represented disparate habitat types (a tidal wetland channel, a turbid backwater channel, and a deep open-water channel), each a potential outcome of local restoration projects. Using stable isotope analysis coupled with a Bayesian mixing model, we identified significant differences in primary-producer contribution to fishes and invertebrates across habitats and seasons, especially in the relative contribution of submersed aquatic vegetation and phytoplankton. Most fishes utilized multiple primary producers and showed little segregation between pelagic and littoral food webs among habitats. Availability of primary producers differs seasonally and across multiple spatial scales, helping to buffer environmental variability and thus enhancing food web resilience. Ecosystem restoration may improve with emphasis on restoring a wide variety of primary producers to support consumers.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00762-9","usgsCitation":"Young, M.J., Howe, E.R., O’Rear, T., Berridge, K., and Moyle, P.B., 2021, Food web fuel differs across habitats and seasons of a tidal freshwater estuary: Estuaries and Coasts, v. 44, p. 286-301, https://doi.org/10.1007/s12237-020-00762-9.","productDescription":"16 p.","startPage":"286","endPage":"301","ipdsId":"IP-107933","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454518,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-020-00762-9","text":"Publisher Index Page"},{"id":381104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, Upper San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80782318115233,\n 38.226314067139185\n ],\n [\n -121.67770385742186,\n 38.226314067139185\n ],\n [\n -121.67770385742186,\n 38.32011084501538\n ],\n [\n -121.80782318115233,\n 38.32011084501538\n ],\n [\n -121.80782318115233,\n 38.226314067139185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howe, Emily R.","contributorId":177088,"corporation":false,"usgs":false,"family":"Howe","given":"Emily","email":"","middleInitial":"R.","affiliations":[{"id":17978,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":806324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rear, Teejay","contributorId":245510,"corporation":false,"usgs":false,"family":"O’Rear","given":"Teejay","email":"","affiliations":[{"id":49210,"text":"Univ. of California, Davis, Center for Watershed Sciences","active":true,"usgs":false}],"preferred":false,"id":806325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berridge, Kathleen","contributorId":245511,"corporation":false,"usgs":false,"family":"Berridge","given":"Kathleen","email":"","affiliations":[{"id":49211,"text":"Univ. of California, Davis, Center for Watershed Sci. AND Environmental Science Associates","active":true,"usgs":false}],"preferred":false,"id":806326,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moyle, Peter B.","contributorId":117099,"corporation":false,"usgs":false,"family":"Moyle","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":806327,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218225,"text":"70218225 - 2021 - Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices","interactions":[],"lastModifiedDate":"2021-03-19T20:54:21.790299","indexId":"70218225","displayToPublicDate":"2020-07-10T12:29:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices","docAbstract":"Small waterbodies are numerically dominant in many landscapes and provide several important ecosystem services, but automated measurement of waterbodies smaller than a standard Landsat pixel (0.09 ha) remains challenging. To further evaluate sub‐Landsat pixel techniques for estimating inundation extent of small waterbodies (basin area: 0.06–1.79 ha), we used a partial spectral unmixing method with matched filtering applied to September 1985–2018 Landsat 5 and eight imagery from southern Arizona, USA. We estimated trends in modeled surface water area each September and evaluated the ability of several common drought indices to explain variation in mean water area. Our methods accurately classified waterbodies as dry or inundated (Landsat 5: 91.3%; Landsat 8: 98.9%) and modeled and digitized surface water areas were strongly correlated (R2 = 0.70–0.92; bias = −0.024 to −0.015 ha). Estimated surface water area was best explained by the 3‐month seasonal standardized precipitation index (SPI03; July‒September). We found a wide range of estimated relationships between drought indices (e.g. SPI vs. Palmer Drought Severity Index) and estimated water area, even for different durations of the same drought index (e.g. SPI01 vs. SPI12). Mean waterbody surface area decreased by ~14% from September 1985 to September 2018, which matches declines in local annual precipitation and regional trends of reduced inundation extent of larger waterbodies. These results emphasize the importance of understanding local systems when relying on drought indices to infer variation in past or future surface water dynamics. Several challenges remain before widespread application of sub‐pixel methods is feasible, but our results provide further evidence that partial spectral unmixing with matched filtering provides reliable measures of inundation extent of small waterbodies.
","language":"English","publisher":"Zoological Society of London (Wiley)","doi":"10.1002/rse2.172","usgsCitation":"Sall, I., Jarchow, C., Sigafus, B.H., Eby, L., Forzley, M.J., and Hossack, B., 2021, Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices: Remote Sensing in Ecology and Conservation, v. 7, no. 1, p. 109-124, https://doi.org/10.1002/rse2.172.","productDescription":"16 p.","startPage":"109","endPage":"124","ipdsId":"IP-114730","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454519,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.172","text":"Publisher Index Page"},{"id":383382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona","otherGeospatial":"San Rafael Valley and neighboring areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.89599609375,\n 31.283245492650792\n ],\n [\n -110.2313232421875,\n 31.283245492650792\n ],\n [\n -110.2313232421875,\n 31.991771310172094\n ],\n [\n -110.89599609375,\n 31.991771310172094\n ],\n [\n -110.89599609375,\n 31.283245492650792\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":810490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":810491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eby, Lisa A","contributorId":251751,"corporation":false,"usgs":false,"family":"Eby","given":"Lisa A","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":810493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forzley, Michael James 0000-0001-5307-8459","orcid":"https://orcid.org/0000-0001-5307-8459","contributorId":251752,"corporation":false,"usgs":true,"family":"Forzley","given":"Michael","email":"","middleInitial":"James","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810494,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810495,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216547,"text":"70216547 - 2021 - What could explain δ13C signatures in biocrust cyanobacteria of drylands?","interactions":[],"lastModifiedDate":"2021-01-19T16:15:25.123233","indexId":"70216547","displayToPublicDate":"2020-07-03T10:31:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2729,"text":"Microbial Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"What could explain δ13C signatures in biocrust cyanobacteria of drylands?","title":"What could explain δ13C signatures in biocrust cyanobacteria of drylands?","docAbstract":"Dryland ecosystems are increasing in geographic extent and contribute greatly to interannual variability in global carbon dynamics. Disentangling interactions among dominant primary producers, including plants and autotrophic microbes, can help partition their contributions to dryland C dynamics. We measured the δ13C signatures of biological soil crust cyanobacteria and dominant plant species (C3 and C4) across a regional scale in the southwestern USA to determine if biocrust cyanobacteria were coupled to plant productivity (using plant-derived C mixotrophically), or independent of plant activity (and therefore purely autotrophic). Cyanobacterial assemblages located next to all C3 plants and one C4 species had consistently more negative δ13C (by 2‰) than the cyanobacteria collected from plant interspaces or adjacent to two C4Bouteloua grass species. The differences among cyanobacterial assemblages in δ13C could not be explained by cyanobacterial community composition, photosynthetic capacity, or any measured leaf or root characteristics (all slopes not different from zero). Thus, microsite differences in abiotic conditions near plants, rather than biotic interactions, remain a likely mechanism underlying the observed δ13C patterns to be tested experimentally.
","language":"English","publisher":"Springerlink","doi":"10.1007/s00248-020-01536-3","usgsCitation":"Stricker, E., Cain, G., Rudgers, J.A., Sinsabaugh, R.L., Fernandes, V., Nelson, C., Giraldo Silva, A., Garcia-Pichel, F., Belnap, J., and Darrouzet-Nardi, A., 2021, What could explain δ13C signatures in biocrust cyanobacteria of drylands?: Microbial Ecology, v. 81, p. 134-145, https://doi.org/10.1007/s00248-020-01536-3.","productDescription":"12 p.","startPage":"134","endPage":"145","ipdsId":"IP-119493","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":380787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jornada Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.81982421874999,\n 32.36372329228304\n ],\n [\n -106.5289306640625,\n 32.36372329228304\n ],\n [\n -106.5289306640625,\n 33.71291698851023\n ],\n [\n -107.81982421874999,\n 33.71291698851023\n ],\n [\n -107.81982421874999,\n 32.36372329228304\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","noUsgsAuthors":false,"publicationDate":"2020-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Stricker, Eva 0000-0002-9742-3434","orcid":"https://orcid.org/0000-0002-9742-3434","contributorId":245190,"corporation":false,"usgs":false,"family":"Stricker","given":"Eva","email":"","affiliations":[{"id":49109,"text":"University of New Mexico, Department of Biology, Albuquerque, NM 87131","active":true,"usgs":false}],"preferred":false,"id":805570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, Grace","contributorId":245238,"corporation":false,"usgs":false,"family":"Cain","given":"Grace","email":"","affiliations":[],"preferred":false,"id":805657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rudgers, Jennifer A.","contributorId":195173,"corporation":false,"usgs":false,"family":"Rudgers","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":805658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sinsabaugh, Robert L","contributorId":195172,"corporation":false,"usgs":false,"family":"Sinsabaugh","given":"Robert","email":"","middleInitial":"L","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":805659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernandes, Vanessa","contributorId":245239,"corporation":false,"usgs":false,"family":"Fernandes","given":"Vanessa","email":"","affiliations":[],"preferred":false,"id":805660,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Corey","contributorId":245240,"corporation":false,"usgs":false,"family":"Nelson","given":"Corey","affiliations":[],"preferred":false,"id":805661,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Giraldo Silva, Ana","contributorId":181758,"corporation":false,"usgs":false,"family":"Giraldo Silva","given":"Ana","email":"","affiliations":[],"preferred":false,"id":805662,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garcia-Pichel, Ferran","contributorId":166779,"corporation":false,"usgs":false,"family":"Garcia-Pichel","given":"Ferran","email":"","affiliations":[{"id":24511,"text":"Arizona State University, Tempe AZ USA 85287","active":true,"usgs":false}],"preferred":false,"id":805663,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805578,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Darrouzet-Nardi, Anthony adarrouzet-nardi@usgs.gov","contributorId":207292,"corporation":false,"usgs":false,"family":"Darrouzet-Nardi","given":"Anthony","email":"adarrouzet-nardi@usgs.gov","affiliations":[],"preferred":false,"id":805664,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70211292,"text":"70211292 - 2021 - Interpreting and reporting 40Ar/39Ar geochronologic data","interactions":[],"lastModifiedDate":"2021-03-05T22:02:00.254822","indexId":"70211292","displayToPublicDate":"2020-07-01T09:41:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Interpreting and reporting 40Ar/39Ar geochronologic data","title":"Interpreting and reporting 40Ar/39Ar geochronologic data","docAbstract":"The 40Ar/39Ar dating method is among the most versatile of geochronometers, having the potential to date a broad variety of K-bearing materials spanning from the time of Earth’s formation into the historical realm. Measurements using modern noble-gas mass spectrometers are now producing 40Ar/39Ar dates with analytical uncertainties of ∼0.1%, thereby providing precise time constraints for a wide range of geologic and extraterrestrial processes. Analyses of increasingly smaller subsamples have revealed age dispersion in many materials, including some minerals used as neutron fluence monitors. Accordingly, interpretive strategies are evolving to address observed dispersion in dates from a single sample. Moreover, inferring a geologically meaningful “age” from a measured “date” or set of dates is dependent on the geological problem being addressed and the salient assumptions associated with each set of data. We highlight requirements for collateral information that will better constrain the interpretation of 40Ar/39Ar data sets, including those associated with single-crystal fusion analyses, incremental heating experiments, and in situ analyses of microsampled domains. To ensure the utility and viability of published results, we emphasize previous recommendations for reporting 40Ar/39Ar data and the related essential metadata, with the amendment that data conform to evolving standards of being findable, accessible, interoperable, and reusable (FAIR) by both humans and computers. Our examples provide guidance for the presentation and interpretation of 40Ar/39Ar dates to maximize their interdisciplinary usage, reproducibility, and longevity.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35560.1","usgsCitation":"Schaen, A.J., Jicha, B.R., Hodges, K.V., Vermeesch, P., Stelten, M.E., Mercer, C.M., Phillips, D., Rivera, T., Jourdan, F., Matchan, E.L., Hemming, S.R., Morgan, L.E., Kelley, S.P., Cassata, W.S., Heizler, M.T., Vasconcelos, P.M., Benowitz, J.A., Koppers, A.A., Mark, D.F., Niespolo, E.M., Sprain, C.J., Hames, W.E., Kuiper, K.F., Turrin, B., Renne, P.R., Ross, J., Nomade, S., Guillou, H., Webb, L.E., Cohen, B.A., Calvert, A.T., Joyce, N., Ganderod, M., Wijbrans, J., Ishizuka, O., He, H., Ramirez, A., Pfander, J., Lopez-Martinez, M., Qiu, H., and Singer, B.S., 2021, Interpreting and reporting 40Ar/39Ar geochronologic data: GSA Bulletin, v. 133, no. 3-4, p. 461-487, https://doi.org/10.1130/B35560.1.","productDescription":"17 p.","startPage":"461","endPage":"487","ipdsId":"IP-113901","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science 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Constraining sediment supply and net budgets is difficult due to multiple timescales of variability in hydrodynamic forcing and suspended-sediment concentrations, as well as the fundamental limitations of measurement and modeling technologies. We used two independent observational campaigns and one hydrodynamic modeling effort to estimate the sediment supply to Jamaica Bay, New York, USA, an urbanized embayment with a history of extensive wetland loss. We found that all three estimates indicate a net import to the system, ranging from 36 x 106 – 74 x 106 kg/y, with a mean estimate of 55,000 t/y +/- 31,000 t/y, which compares well with a prior estimate derived from radionuclide tracers. Net sediment import is controlled by flood-ebb asymmetry in bed shear stress, which results in higher suspended sediment concentrations on flood tide relative to ebb. This indicates a seaward source of sediment, likely offshore marine deposits and potentially sediment from the adjacent Hudson River-Estuary that is resuspended by waves in the coastal ocean. Despite the net sediment import, a simple sediment budget suggests that the rate of supply is not sufficient to maintain the present geomorphic planform of the system relative to sea-level rise. The convergent estimates from independent methods provide reasonable guidance as context for sediment-based restoration efforts.","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00784-3","usgsCitation":"Chant, R., Ralston, D.K., Ganju, N., Pianca, C., Simonson, A., and Cartwright, R., 2021, Sediment budget estimates for a highly impacted embayment with extensive wetland loss: Estuaries and Coasts, v. 44, p. 608-626, https://doi.org/10.1007/s12237-020-00784-3.","productDescription":"19 p.","startPage":"608","endPage":"626","ipdsId":"IP-104431","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Grassey Bay, Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.14947509765625,\n 40.50126945841645\n ],\n [\n -73.7457275390625,\n 40.50126945841645\n ],\n [\n -73.7457275390625,\n 40.74101426921151\n ],\n [\n -74.14947509765625,\n 40.74101426921151\n ],\n [\n -74.14947509765625,\n 40.50126945841645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Chant, Robert","contributorId":237975,"corporation":false,"usgs":false,"family":"Chant","given":"Robert","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":795713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, David K. 0000-0002-0774-3101","orcid":"https://orcid.org/0000-0002-0774-3101","contributorId":195909,"corporation":false,"usgs":false,"family":"Ralston","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":795714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":795715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pianca, Casia","contributorId":237976,"corporation":false,"usgs":false,"family":"Pianca","given":"Casia","email":"","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":795716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simonson, Amy","contributorId":237977,"corporation":false,"usgs":false,"family":"Simonson","given":"Amy","affiliations":[{"id":47668,"text":"NYWSC","active":true,"usgs":false}],"preferred":false,"id":795717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cartwright, Richard","contributorId":237978,"corporation":false,"usgs":false,"family":"Cartwright","given":"Richard","affiliations":[{"id":47668,"text":"NYWSC","active":true,"usgs":false}],"preferred":false,"id":795718,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211520,"text":"70211520 - 2021 - Estimating age and growth of invasive sea lamprey: A review of approaches and investigation of a new method","interactions":[],"lastModifiedDate":"2022-01-05T17:47:39.475706","indexId":"70211520","displayToPublicDate":"2020-06-27T10:51:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating age and growth of invasive sea lamprey: A review of approaches and investigation of a new method","docAbstract":"We review recent advances in age and growth estimation of invasive sea lamprey (Petromyzon marinus) in the Great Lakes and present a more accurate method for growth estimation. To forecast growth and prioritize streams for control actions, sea lamprey managers currently use an average daily growth model. Here, a new linear model that included stream and lake as contributing variables was investigated and found to outperform the currently used growth model (roughly a 10 mm difference at age 1). Length-at-age of larvae between ages 1 and 4 were also best forecasted by a linear model with the predictor variables including growing degree days, stream, lake, and larval lamprey density. The model predicts that larval sea lamprey grow faster in warm streams with low densities of lamprey larvae. More accurate growth models could allow sea lamprey control managers to improve decisions concerning how sea lamprey control effort is allocated among streams, and could help inform broader modeling efforts evaluating the population demographics of a lake-wide populations exposed to varying control and environmental scenarios. Priority areas for research include investigating if temperatures have increased in sea lamprey-producing streams in response to climate change, using close-kin mark-recapture to mark family groups at age 1 to age large larvae and transformers years later, and determining if sex determination is environmentally mediated by larval growth and density.
During the 2018 eruption of Kīlauea Volcano, Hawai'i, scientists relied heavily on a conceptual model of explosive eruptions triggered when lava‐lake levels drop below the water table. Numerical modeling of multiphase groundwater flow and heat transport revealed that, contrary to expectations, liquid water inflow to the drained magma conduit would likely be delayed by months to years, owing to the inability of liquid water to transit a zone of very hot rock. The summit of Kīlauea subsequently experienced an ∼2‐month period of consistent repeated collapses, and the crater now extends below the equilibrium position of the water table. Liquid water first emerged into the deepened crater in late July 2019. The timing of first appearance of liquid water (about 14 months postcollapse) and the rate of crater lake filling (currently ∼27 kg/s) were well‐predicted by the numerical modeling done in late spring 2018, which forecast liquid inflow after 3 to 24 months at rates of 10 to 100 kg/s. A second‐generation groundwater model, reflecting the current crater geometry, forecasts lake filling over the next several years. The successful 2018 to present forecasts with both models are based on unadjusted in situ permeability estimates (1 to 6 × 10−14 m2) and water‐table elevations (600 to 800 m) from a nearby research drillhole and geophysical surveys. Important unknowns that affect the reliability of longer‐term forecasts include the equilibrium water‐table geometry, the rate of evaporation from the hot and growing crater lake (currently ∼29,000 m2 at 70‐80 °C), and heterogenous permeability changes caused by the 2018 collapse.
Dissolved organic matter (DOM) helps regulate aquatic ecosystem structure and function. In small streams, DOM concentrations are controlled by transport of terrestrial materials to waterways, and are thus highly variable. As rivers become larger, the River Continuum Concept hypothesizes that internal primary production is an increasingly important DOM source, but direct evidence is limited. Recently, the Pulse-Shunt Concept postulated that terrestrial DOM concentrations in larger rivers increase with flow and temperature, which seemingly contradicts previously reported DOM chemostasis in large rivers. This study estimates daily gross primary production (GPP) in 13 streams and rivers across the Connecticut River watershed (watershed areas 0.4–25,019 km2) from 2015 through 2017. Chemostasis of DOM concentrations is maintained by a switch from autochthonous sources of DOM at low flows to terrestrial sources of DOM at high flows in a large temperate river and to a lesser degree in smaller tributaries. At low flow, autochthonous DOM linked to aquatic GPP is the dominant fraction of the DOM pool in large rivers. This autochthonous DOM maintains chemostasis in the main stem and to a lesser extent upstream. Thus, in larger rivers, low-flow autochthonous production stabilizes DOM concentrations during the summer, a critical time for riverine ecology. Consistent with the Pulse-Shunt Concept, terrigenous DOM is the dominant fraction of DOM during higher flow periods and about 70% of annual DOM fluxes to the coast are terrestrial. This pattern of DOM switching is potentially widespread in temperate watersheds with implications to both inland waters and coastal ecosystems.
Manipulation of water velocities and turbulence using pumps, propellers, or jets is a promising alternative to physical water control structures to guide fish towards traps or fishways. Sea lamprey (Petromyzon marinus) are a species of concern in much of their native and invasive ranges, and their improved guidance could benefit management actions for both conservation and control. The flow velocity enhancement system (FVES), an emergent technology that uses a Venturi pump to generate a plume of turbulence, has shown promise guiding downstream migrating fish in slow-moving or static water conditions formed by large reservoirs, but is untested for guidance of upstream swimming fish in low current environments. The FVES had minimal impact on depth averaged velocity profiles, but produced elevated levels of turbulence. Changes in spatial distribution and number of turns suggest sea lamprey detect and are mildly attracted to turbulence induced by the FVES. These results demonstrate the potential of induced turbulence as a guidance mechanism for upstream migrating sea lamprey, but more extensive testing is needed to show the full utility of this approach.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/24705357.2020.1775504","usgsCitation":"Zielinski, D.P., Miehls, S.M., Burns, G., and Coutant, C., 2021, Adult sea lamprey respond to induced turbulence in a low current system: Journal of Ecohydraulics, v. 6, no. 1, p. 82-90, https://doi.org/10.1080/24705357.2020.1775504.","productDescription":"9 p.","startPage":"82","endPage":"90","ipdsId":"IP-115537","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":502414,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":378246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","city":"Hammond","otherGeospatial":"Hammond Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.0725326538086,\n 45.49503704949293\n ],\n [\n -84.00146484374999,\n 45.49503704949293\n ],\n [\n -84.00146484374999,\n 45.50466259908575\n ],\n [\n -84.0725326538086,\n 45.50466259908575\n ],\n [\n -84.0725326538086,\n 45.49503704949293\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Zielinski, Daniel P.","contributorId":211034,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":34820,"text":"Great Lakes Fisheries Commission, Ann Arbor, MI","active":true,"usgs":false}],"preferred":false,"id":798244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Gordon","contributorId":239968,"corporation":false,"usgs":false,"family":"Burns","given":"Gordon","email":"","affiliations":[{"id":48074,"text":"Natural Solutions ... A Dam Site Better LLC","active":true,"usgs":false}],"preferred":false,"id":798246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coutant, Charles","contributorId":239969,"corporation":false,"usgs":false,"family":"Coutant","given":"Charles","affiliations":[{"id":48075,"text":"Coutant Aquatics","active":true,"usgs":false}],"preferred":false,"id":798247,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211817,"text":"70211817 - 2021 - Eradication of sea lampreys from the Laurentian Great Lakes is possible","interactions":[],"lastModifiedDate":"2022-01-06T15:29:46.962576","indexId":"70211817","displayToPublicDate":"2020-05-14T08:12:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Eradication of sea lampreys from the Laurentian Great Lakes is possible","docAbstract":"Eradication has been achieved for many vertebrate pest control programs, primarily on small, isolated islands, but has never been considered a practical goal for invasive sea lampreys in the Laurentian Great Lakes. Our objective was to examine evidence relevant to the feasibility of setting eradication as a management goal for Great Lakes sea lampreys. Bomford and O'Brien (1995) listed six conditions for successful eradication of a vertebrate pest; here we examine evidence that these conditions are likely to be met for Great Lakes sea lampreys, with a focus on the first condition: that removal of the pest through control can exceed their rate of replenishment. We analyzed two data sets – one empirical and one synthetic – to estimate stock-recruitment relationships and calculate the exploitation rate necessary for extinction. The empirical data set included the effect of existing lampricide control and suggested an exploitation rate of 59%, in addition to lampricide control, would be sufficient for eventual eradication. The synthetic data set, derived from a simulation of stream-level recruitment dynamics in the absence of lampricide control, suggested that an overall exploitation rate of 90% would be sufficient. We suggest that both of these targets could be achieved. Meeting the other conditions will depend on the scale of the eradication effort, and on development of an exploitation strategy, such as genetic biocontrol, that can target sea lampreys in presently invulnerable habitats. Overall, we concluded that eradication of sea lampreys from the Great Lakes should not be dismissed as a prospective goal.
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University","active":true,"usgs":false}],"preferred":false,"id":795226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":795225,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216760,"text":"70216760 - 2021 - Assessment of NMR logging for estimating hydraulic conductivity in glacial aquifers","interactions":[],"lastModifiedDate":"2021-01-19T16:10:34.106194","indexId":"70216760","displayToPublicDate":"2020-05-10T09:40:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of NMR logging for estimating hydraulic conductivity in glacial aquifers","docAbstract":"Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K. Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐K (K < 10−4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K, suggesting the SDR model does not incorporate all the properties of the pore space that control K. Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.
Recently reported detrital zircon (DZ) data help to associate the Paleogene strata of the Gulf of Mexico region to various provenance areas. By far, recent work has emphasised upper Paleocene-lower Eocene and upper Oligocene strata that were deposited during the two episodes of the highest sediment supply in the Paleogene. The data reveal a dynamic drainage history, including (1) initial routing of western Cordilleran drainages towards the Gulf of Mexico in the Paleocene, (2) an eastward shift of the western continental divide, from the Jura-Cretaceous cordilleran arc to the eastern edge of the Laramide province after the Paleocene and (3) a southward shift, along the eastern Laramide province, of the headwaters of river systems draining to the Mississippi and Houston embayments at some time between the early Eocene and Oligocene. However, DZ characterisation of most (~20 Myr) of the middle Eocene-lower Oligocene section remains limited. We present 60 DZ age spectra, most of which are from the middle or upper Eocene outcrop belts, with 50–200-km spacing. We define six to eight distinct groups of DZ age spectra for middle and upper Eocene strata. Data from this and other studies resolve at least six substantial temporal changes in age spectra at various positions along the continental margin. The evolving age spectra constrain the middle and upper Eocene drainage patterns of large parts of interior North America. The most well-resolved aspects of these drainage patterns include (1) persistent rivers that flowed from erosional landscapes across the Paleozoic Appalachian orogen either into the low-lying Mississippi embayment or directly into the eastern Gulf; (2) at least during marine regressions, a trunk channel that likely flowed southward along the axial part of Mississippi Embayment and integrated tributaries from the east and west; and (3) rivers that flowed to the Houston embayment in the middle Eocene that likely originated in the Laramide province in central Colorado and southern Wyoming, as Precambrian basement highs in those source areas were being unroofed.
The migration of larval fish from spawning to rearing habitat in rivers is not well understood. This paper describes a methodology to predict larval drift using a Lagrangian particle-tracking (LPT) model with passive and active behavioural components loosely coupled to a quasi-three-dimensional hydraulic model. In the absence of measured larval drift, a heuristic approach is presented for the larval drift of two species of interest, white sturgeon (Acipenser transmontanus) and burbot (Lota lota), in the Kootenai River, Idaho. Previous studies found that many fish species prefer certain vertical zones within the water column; sturgeon tend to be found near the bottom and burbot close to the water surface. Limiting the vertical movement of larvae is incorporated into the active component of the LPT model. The results illustrate a pattern of drift where secondary flow in meander bends and other zones of flow curvature redistributes particles toward the outside of the bend for surface drifters and toward the inside of the bend for bottom drifters. This pattern periodically reinforces the intersection of drifting larvae with channel margins in meander bends. In the absence of measured larval drift data, the model provides a tool for hypothesis testing and a guide to both field and laboratory experiments to further define the role of active behaviour in drifting larvae.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2019.1709102","usgsCitation":"McDonald, R.R., and Nelson, J.M., 2021, A Lagrangian particle-tracking approach to modelling larval drift in rivers: Journal of Ecohydraulics, v. 6, no. 1, p. 17-35, https://doi.org/10.1080/24705357.2019.1709102.","productDescription":"19 p.","startPage":"17","endPage":"35","ipdsId":"IP-102070","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":502662,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":436681,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1U4O0","text":"USGS data release","linkHelpText":"fluvial-particle, U.S. Geological Survey software release"},{"id":415416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.1478357988205,\n 48.69782150172384\n ],\n [\n -116.12731836897655,\n 48.73211652626813\n ],\n [\n -116.32565352413204,\n 48.71948423713994\n ],\n [\n -116.34753878263186,\n 48.78261395455223\n ],\n [\n -116.35574575456924,\n 48.936497382156176\n ],\n [\n -116.41319455813164,\n 48.99935415693781\n ],\n [\n -116.58964445478699,\n 49.00025153683018\n ],\n [\n -116.55544873838093,\n 48.94907507626053\n ],\n [\n -116.45012593185005,\n 48.894249080754236\n ],\n [\n -116.43644764528761,\n 48.82135431506677\n ],\n [\n -116.4255050160379,\n 48.72038664873716\n ],\n [\n -116.33112483875708,\n 48.669826665231625\n ],\n [\n -116.1478357988205,\n 48.69782150172384\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":868972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":868973,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223732,"text":"70223732 - 2021 - Factors influencing Cinnamon Teal nest attendance patterns","interactions":[],"lastModifiedDate":"2021-09-07T13:22:44.296825","indexId":"70223732","displayToPublicDate":"2020-04-18T07:38:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing Cinnamon Teal nest attendance patterns","docAbstract":"Patterns of nest attendance in birds result from complex behaviours and influence the success of reproductive events. Incubation behaviours vary based on individual body condition, energy requirements and environmental factors. We assessed nest attendance patterns in Cinnamon Teal Spatula cyanoptera breeding in the San Luis Valley of Colorado in 2016–2017 using trail and video cameras to observe behaviours throughout incubation. We evaluated the effect of temporal, life-history and environmental covariates on the frequency and duration of incubation recesses as well as the incubation constancy. There was considerable model uncertainty among the models used to evaluate recess frequency. Recess duration varied according to the interaction between nest age and a quadratic effect of time of day, with hens on older nests taking longer recesses in the afternoon and hens on nests earlier in incubation taking longer recesses in the morning and evening. Incubation constancy decreased with higher ambient temperatures in the study area. This study provides evidence that Cinnamon Teal modify their behaviour during incubation according to the age of the nest and the time of day. These results improve our knowledge of Cinnamon Teal breeding ecology and shed light on the behaviours that fast-lived species may use to cope with environmental factors during nesting.
Spatial information on the distribution of ecosystem patterns and processes can be a critical component of designing and implementing effective management programs in river‐floodplain ecosystems. For example, translating how flood pulses detected within a stream gauge record are spatially manifested across a river‐valley bottom can be used to evaluate whether the current distribution of physical conditions has the potential to support priority habitats or if intervention is needed to meet desired goals. The size and complexity of large river‐floodplain systems can make mapping inundation dynamics a challenging task. We used a geospatial model to simulate 40 years (1972–2011) of daily surface‐water inundation depths for 11,331 km2 of the Upper Mississippi River System floodplain. We identified discrete inundation events at each 4‐m × 4‐m pixel in the model as sequential days of submergence. We then quantified and mapped four aspects of inundation regime – event frequency, duration, magnitude, and timing – for each pixel. The spatial distribution of inundation regime attributes varied within and among multiple levels of river organization, including navigation pools and geomorphic reaches, but only event timing exhibited a strong down‐river trend. Non‐linear relations among inundation attributes and their geospatial distributions likely reflect complex interactions among topographic, hydrologic, and anthropogenic constraints on flooding dynamics. Together, our results reveal spatial gradients in inundation dynamics not captured by hydrologic data alone. Characterizing such diversity in inundation dynamics is important for testing hypotheses about ecological processes, developing models of ecosystem functions, and informing management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3628","usgsCitation":"Van Appledorn, M., De Jager, N.R., and Rohweder, J.J., 2021, Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications: River Research and Applications, v. 37, no. 2, p. 241-255, https://doi.org/10.1002/rra.3628.","productDescription":"15 p.","startPage":"241","endPage":"255","ipdsId":"IP-113745","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 38.51378825951165\n ],\n [\n -88.154296875,\n 40.245991504199026\n ],\n [\n -86.572265625,\n 41.07935114946899\n ],\n [\n -86.7919921875,\n 41.50857729743935\n ],\n [\n -88.0224609375,\n 42.45588764197166\n ],\n [\n -89.3408203125,\n 44.213709909702054\n ],\n [\n -91.7578125,\n 45.85941212790755\n ],\n [\n -92.63671875,\n 46.195042108660154\n ],\n [\n -92.59277343749999,\n 47.724544549099676\n ],\n [\n -94.8779296875,\n 47.249406957888446\n ],\n [\n -95.9326171875,\n 47.30903424774781\n ],\n [\n -95.4052734375,\n 44.84029065139799\n ],\n [\n -93.6474609375,\n 42.13082130188811\n ],\n [\n -93.515625,\n 39.605688178320804\n ],\n [\n -90.3955078125,\n 38.51378825951165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218476,"text":"70218476 - 2021 - Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","interactions":[],"lastModifiedDate":"2021-03-01T15:28:00.787039","indexId":"70218476","displayToPublicDate":"2020-04-15T09:20:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","title":"Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","docAbstract":"Decisions about invasive species control and eradication can be difficult because of uncertainty in population demographics, movement ecology, and effectiveness of potential response actions. These decisions often include multiple stakeholders and management entities with potentially different objectives, management priorities, and jurisdictional authority. We provide a case study of using multi-party, collaborative decision analysis to aid decision makers in determining objectives and control actions for invasive grass carp (Ctenopharyngodon idella) in Lake Erie. Creating this process required binational (Canada-United States) and multi-state/provincial collaboration to craft a shared problem statement, establish objectives related to ecological, economic, and social concerns, determine potential response actions, and evaluate consequences and tradeoffs of these actions. We used participatory modeling and expert elicitation to evaluate the effectiveness of control scenarios that varied in action type (i.e., removal efforts and spawning barriers) and the temporal and spatial application of these actions. Using a matrix population model parameterized for western Lake Erie grass carp, we found that removal efforts concentrated in areas of high catchability, when paired with a spawning barrier on the Sandusky River, Ohio, USA, could effectively control grass carp in Lake Erie, if all assumptions are met. We determined a set of key uncertainties regarding gear catchability and current population size that have led to the transition to an adaptive management process. In addition, our work formed the basis for grass carp management plans for the states of Michigan and Ohio and has provided a means for collaboration among agencies for effective application of control efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.03.018","usgsCitation":"Robinson, K., DuFour, M.R., Jones, M., Herbst, S., Newcomb, T., Boase, J., Brenden, T.O., Chapman, D., Dettmers, J.M., Francis, J., Hartman, T., Kocovsky, P., Locke, B., Tyson, J., and Mayer, C., 2021, Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella): Journal of Great Lakes Research, v. 47, no. 1, p. 108-119, https://doi.org/10.1016/j.jglr.2020.03.018.","productDescription":"12 p.","startPage":"108","endPage":"119","ipdsId":"IP-111132","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":383686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Lake Erie, Maumee River, River Raisin, Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.73779296875,\n 41.290189955885644\n ],\n [\n -82.430419921875,\n 41.290189955885644\n ],\n [\n -82.430419921875,\n 42.07783959017503\n ],\n [\n -83.73779296875,\n 42.07783959017503\n ],\n [\n -83.73779296875,\n 41.290189955885644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kelly F.","contributorId":140157,"corporation":false,"usgs":false,"family":"Robinson","given":"Kelly F.","affiliations":[{"id":473,"text":"New York Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false},{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuFour, Mark R.","contributorId":203270,"corporation":false,"usgs":false,"family":"DuFour","given":"Mark","email":"","middleInitial":"R.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":811139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, M.W.","contributorId":239977,"corporation":false,"usgs":false,"family":"Jones","given":"M.W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herbst, Seth","contributorId":252926,"corporation":false,"usgs":false,"family":"Herbst","given":"Seth","affiliations":[{"id":50471,"text":"Michigan Department of Natural Resources, Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":811141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newcomb, Tammy","contributorId":252928,"corporation":false,"usgs":false,"family":"Newcomb","given":"Tammy","affiliations":[{"id":50471,"text":"Michigan Department of Natural Resources, Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":811142,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":811143,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dettmers, John M.","contributorId":191256,"corporation":false,"usgs":false,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811146,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Francis, James","contributorId":252929,"corporation":false,"usgs":false,"family":"Francis","given":"James","affiliations":[{"id":50473,"text":"Michigan Department of Natural Resources, Fisheries Division, Waterford Field Office, Waterford, MI","active":true,"usgs":false}],"preferred":false,"id":811147,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hartman, Travis","contributorId":66583,"corporation":false,"usgs":true,"family":"Hartman","given":"Travis","affiliations":[],"preferred":false,"id":811191,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":811148,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Locke, Brian","contributorId":252930,"corporation":false,"usgs":false,"family":"Locke","given":"Brian","affiliations":[{"id":50474,"text":"Ontario Ministry of Natural Resources and Forestry, Lake Erie Management Unit, Wheatley, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":811149,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tyson, Jeff","contributorId":147298,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeff","affiliations":[],"preferred":false,"id":811151,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":811150,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70210111,"text":"70210111 - 2021 - Carbon stock losses and recovery observed for a mangrove ecosystem following a major hurricane in Southwest Florida","interactions":[],"lastModifiedDate":"2021-02-17T22:21:56.760044","indexId":"70210111","displayToPublicDate":"2020-04-06T11:01:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Carbon stock losses and recovery observed for a mangrove ecosystem following a major hurricane in Southwest Florida","docAbstract":"Studies integrating mangrove in-situ observations and remote sensing analysis for specific sites often lack precise estimates of carbon stocks over time frames that include disturbance events. This study quantifies change in mangrove area from 1985 to 2018 with Landsat time series analysis, estimates above and belowground stored carbon using field data, and evaluates aboveground carbon stock changes after the 2004 Category 4, Hurricane Charley, in J.N. “Ding” Darling National Wildlife Refuge. Two allometric equation methods yielding similar results were used to estimate aboveground carbon content in three mangrove species found in the refuge. Aboveground carbon contained 67 (SE = 2) MgC ha−1 with a total refuge estimate of 74,504 MgC in 2018. Sediment contained 259 (SE = 28) MgC ha−1 for a total of 288,008 MgC in the refuge. The initial reduction in mangrove area caused by Hurricane Charley was between 0.6% and 5.3%, equivalent to between 427 MgC and 3,599 MgC under three different scenarios of carbon loss. As a result of the hurricane, approximately 61 ha of mangroves were disturbed, of which 24 ha had recovered by 2018, with 37 ha (~3% of the pre-hurricane mangrove area) still not recovered 14 years after the event. The 37 ha of mangroves that have not recovered are located in a tidally restricted area of the refuge. A longer recovery time in this area will likely result in a greater loss of carbon storage than in the rest of the refuge.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2020.106750","usgsCitation":"Peneva-Reed, E.I., Krauss, K., Bullock, E.L., Zhu, Z., Woltz, V., Drexler, J.Z., Conrad, J.R., and Stehman, S.V., 2021, Carbon stock losses and recovery observed for a mangrove ecosystem following a major hurricane in Southwest Florida: Estuarine, Coastal and Shelf Science, v. 248, 106750, 13 p., https://doi.org/10.1016/j.ecss.2020.106750.","productDescription":"106750, 13 p.","ipdsId":"IP-111057","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":374829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"J.N. “Ding” Darling National Wildlife Refuge, Sanibel Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.16949462890624,\n 26.434609799691536\n ],\n [\n -82.0513916015625,\n 26.434609799691536\n ],\n [\n -82.0513916015625,\n 26.49577131935652\n ],\n [\n -82.16949462890624,\n 26.49577131935652\n ],\n [\n -82.16949462890624,\n 26.434609799691536\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peneva-Reed, Elitsa I. 0000-0002-4570-4701","orcid":"https://orcid.org/0000-0002-4570-4701","contributorId":202809,"corporation":false,"usgs":true,"family":"Peneva-Reed","given":"Elitsa","email":"","middleInitial":"I.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":789169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":789170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bullock, Eric L. 0000-0003-3279-6771","orcid":"https://orcid.org/0000-0003-3279-6771","contributorId":224710,"corporation":false,"usgs":false,"family":"Bullock","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":40922,"text":"Department of Earth & Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":789171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":789172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woltz, Victoria 0000-0001-7843-6486","orcid":"https://orcid.org/0000-0001-7843-6486","contributorId":223011,"corporation":false,"usgs":true,"family":"Woltz","given":"Victoria","email":"","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":789173,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":789174,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conrad, Jeremy R.","contributorId":149347,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","email":"","middleInitial":"R.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":789175,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":789176,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216167,"text":"70216167 - 2021 - Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques","interactions":[],"lastModifiedDate":"2021-11-01T14:41:49.052895","indexId":"70216167","displayToPublicDate":"2020-04-01T09:21:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2375,"text":"Journal of Maps","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques","docAbstract":"Increasing urbanization and suburban growth in cities globally has highlighted the importance of land planning using detailed geomorphologic maps that depict anthropogenic landform changes. Such mapping provides information crucial for land management, hazard identification, and the management of the challenges arising from urbanization. The development and use of quantitative and repeatable methods to map anthropogenic and natural processes are required to advance the science of urban geomorphological mapping. This study investigated the application of geospatial modeling, structure-from-motion (SfM) photogrammetric methods and DEM differencing as means of quantifying anthropogenic landform changes from archival aerial imagery. Anthropogenic landforms were incorporated into a detailed geomorphologic map in an urbanizing watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia.
The Jinsha River, which comprises the upper reaches of the Yangtze River, has among the highest freshwater fish biodiversity and endemism in China, but these characteristics have rarely been quantitatively evaluated at the basin scale. We used fish presence–absence data collected from the entire Jinsha River basin (JRB) from 1964 to 2017 to determine patterns in fish biodiversity. In total, 229 freshwater fish species from 9 orders, 26 families, and 110 genera were recorded. Of these species, 161 were endemic to China, with 94 species being endemic to the Yangtze River basin, and 39 species were threatened. Fish species richness was higher in the downstream river reaches and was higher in the main stem than in the tributaries. Overfishing, water pollution, and dam construction have been threatening fish diversity in the JRB for several decades. Conservation strategies similar to those used in North America may be applicable to the JRB to help protect native fishes in this important river basin. Such strategies include (1) assessment of several tributaries as fish reserves; (2) regular adjustment of turbine operations during the fish spawning period; and (3) regulation of the many co-occurring human stressors in the JRB.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10441","usgsCitation":"Liu, H.W., Guo, C., Qu, X., Xiong, F., Paukert, C.P., Chen, Y., and Sullivan, W., 2021, Fish diversity, endemism, threats, and conservation in the Jinsha River basin (upper Yangtze River), China: North American Journal of Fisheries Management, v. 41, no. 4, p. 967-984, https://doi.org/10.1002/nafm.10441.","productDescription":"18 p.","startPage":"967","endPage":"984","ipdsId":"IP-113887","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Jinsha River Basin (Upper Yangtze River)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 90.5,\n 24.6\n ],\n [\n 105.25,\n 24.6\n ],\n [\n 105.25,\n 35.7333\n ],\n [\n 90.5,\n 35.7333\n ],\n [\n 90.5,\n 24.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, H. W.","contributorId":152164,"corporation":false,"usgs":false,"family":"Liu","given":"H.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":835337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guo, C.","contributorId":272911,"corporation":false,"usgs":false,"family":"Guo","given":"C.","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":835338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qu, X.","contributorId":279670,"corporation":false,"usgs":false,"family":"Qu","given":"X.","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":835339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xiong, F.","contributorId":279671,"corporation":false,"usgs":false,"family":"Xiong","given":"F.","affiliations":[{"id":57334,"text":"http://orcid.org/0000-0002-9369-8545","active":true,"usgs":false}],"preferred":false,"id":835340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":835341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Y.","contributorId":272912,"corporation":false,"usgs":false,"family":"Chen","given":"Y.","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":835342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sullivan, W.","contributorId":156266,"corporation":false,"usgs":false,"family":"Sullivan","given":"W.","affiliations":[],"preferred":false,"id":835343,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224613,"text":"70224613 - 2021 - Units recovery methods in compositional data analysis","interactions":[],"lastModifiedDate":"2021-09-30T11:55:22.832759","indexId":"70224613","displayToPublicDate":"2020-03-24T06:52:56","publicationYear":"2021","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":"Units recovery methods in compositional data analysis","docAbstract":"Compositional data carry relative information. Hence, their statistical analysis has to be performed on coordinates with respect to a log-ratio basis. Frequently, the modeler is required to back-transform the estimates obtained with the modeling to have them in the original units such as euros, kg or mg/liter. Approaches for recovering original units need to be formally introduced and its properties explored. Here, we formulate and analyze the properties of two procedures: a simple approach consisting of adding a residual part to the composition and an approach based on the use of an auxiliary variable. Both procedures are illustrated using a geochemical data set where the original units are recovered when spatial models are applied.
In an effort to determine contaminant presence, concentrations, and movement from a low‐level radioactive waste (LLRW) burial disposal site to ecosystems in the surrounding area, a study was developed to assess concentrations of per‐ and polyfluoroalkyl substances (PFAS), polychlorinated biphenyls (PCBs), and tritium. To complete this assessment small mammals, vegetation, soil, and insect samples were collected from areas within and adjacent to the Beatty, Nevada, LLRW site and from a reference area located approximately 3 km south of the LLRW site. Samples underwent analysis via liquid chromatography tandem mass spectrometry, gas chromatography mass spectrometry, or scintillation spectroscopy depending on the analyte of interest. Small mammal tissues showed maximum concentrations of over 1700 ng/g for PFAS, 1600 ng/g for PCBs, and 10 000 Bq/kg for tritium. The primary contaminants found in soil samples were PCBs, with maximum concentrations exceeding 25 ng/g. Trace amounts of PFAS were also detected in soils and insects. Only qualitative data were obtained from vegetation samples because of the complex matrix of the dominant plant species (creosote bush; Larrea tridentata [Sessé & Moc. ex DC.] Coville). Overall, these data indicate the presence of various anthropogenic contaminants in the ecosystem surrounding the LLRW area, but additional analyses are necessary to confirm the sources and migration pathways of PFAS and PCBs in this hyperarid environment. Environ Toxicol Chem 2020;00:1–8. © 2020 SETAC