{"pageNumber":"536","pageRowStart":"13375","pageSize":"25","recordCount":165901,"records":[{"id":70217863,"text":"70217863 - 2021 - Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","interactions":[],"lastModifiedDate":"2023-07-07T14:11:49.37828","indexId":"70217863","displayToPublicDate":"2021-01-29T07:42:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1651,"text":"Fish Physiology and Biochemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","title":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","docAbstract":"

Asian swamp eel, Monopterus albus/javanensis [Zuiew, 1973/Lacepede 1800], has been established in the southeastern USA since at least 1994, yet little is known about its ability to survive low winter temperatures. We use standard thermal methodologies to quantify low temperature responses and provide a detailed description of swamp eel reactions to cold temperatures. When exposed to chronic temperature decreases of 1.0 °C day−1, swamp eel ceased foraging at 15.0 °C, markedly diminished movements below 11.0 °C, and became incapacitated near 9.6 °C. During critical thermal minima trials, swamp eel exposed to acute temperature drops (0.25 °C min−1) tolerated temperatures as low as 6.2 °C. Swamp eel exhibited a moderate cold acclimation response, gaining 0.23 °C in cold tolerance for every 1 °C drop in acclimation temperature. Progressive time-series critical thermal minimum temperatures (CTmin) estimates for eel acclimated to 20.5 °C followed by an acute temperature decrease to 16.0 °C, revealed that cold acclimation may occur in only 8 days. Fringe populations of swamp eel in their native range periodically experience colder winter temperatures, which may explain the ability of introduced populations to survive winter cold fronts in Florida. Understanding Asian swamp eel acute and chronic thermal limits may be useful in assessing dispersal risk and range expansion in the southeastern USA.


","language":"English","publisher":"Springer","doi":"10.1007/s10695-021-00925-w","usgsCitation":"Saylor, R.K., Schofield, P., and Bennett, W., 2021, Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures: Fish Physiology and Biochemistry, v. 47, p. 465-476, https://doi.org/10.1007/s10695-021-00925-w.","productDescription":"12 p.; Data Release","startPage":"465","endPage":"476","ipdsId":"IP-120529","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":383089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418749,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NFFZTV","text":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","linkFileType":{"id":5,"text":"html"}}],"volume":"47","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Saylor, Ryan K.","contributorId":248815,"corporation":false,"usgs":false,"family":"Saylor","given":"Ryan","email":"","middleInitial":"K.","affiliations":[{"id":16703,"text":"University of West Florida","active":true,"usgs":false}],"preferred":false,"id":809965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Pam 0000-0002-8752-2797","orcid":"https://orcid.org/0000-0002-8752-2797","contributorId":213749,"corporation":false,"usgs":true,"family":"Schofield","given":"Pam","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":809966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Wayne A","contributorId":248816,"corporation":false,"usgs":false,"family":"Bennett","given":"Wayne A","affiliations":[{"id":16703,"text":"University of West Florida","active":true,"usgs":false}],"preferred":false,"id":809967,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220471,"text":"70220471 - 2021 - Reconstructing population dynamics of a threatened marine mammal using multiple data sets","interactions":[],"lastModifiedDate":"2021-05-14T12:48:58.278944","indexId":"70220471","displayToPublicDate":"2021-01-29T07:38:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing population dynamics of a threatened marine mammal using multiple data sets","docAbstract":"

Models of marine mammal population dynamics have been used extensively to predict abundance. A less common application of these models is to reconstruct historical population dynamics, filling in gaps in observation data by integrating information from multiple sources. We developed an integrated population model for the Florida manatee (Trichechus manatus latirostris) to reconstruct its population dynamics in the southwest region of the state over the past 20 years. Our model improved precision of key parameter estimates and permitted inference on poorly known parameters. Population growth was slow (averaging 1.02; 95% credible interval 1.01–1.03) but not steady, and an unusual mortality event in 2013 led to an estimated net loss of 332 (217–466) manatees. Our analyses showed that precise estimates of abundance could be derived from estimates of vital rates and a few input estimates of abundance, which may mean costly surveys to estimate abundance don’t need to be conducted as frequently. Our study also shows that retrospective analyses can be useful to: (1) model the transient dynamics of age distribution; (2) assess and communicate the conservation status of wild populations; and (3) improve our understanding of environmental effects on population dynamics and thus enhance our ability to forecast.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-021-81478-z","usgsCitation":"Hostetler, J., Martin, J., Kosempa, M., Edwards, H., Rood, K., Barton, S., and Runge, M.C., 2021, Reconstructing population dynamics of a threatened marine mammal using multiple data sets: Scientific Reports, v. 11, 2702 , 15 p., https://doi.org/10.1038/s41598-021-81478-z.","productDescription":"2702 , 15 p.","ipdsId":"IP-117972","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453658,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-81478-z","text":"Publisher Index Page"},{"id":436529,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98835OJ","text":"USGS data release","linkHelpText":"Data from: Reconstructing population dynamics of a threatened marine mammal using multiple data sets"},{"id":385637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Southwest Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.232421875,\n 25.16517336866393\n ],\n [\n -80.419921875,\n 25.16517336866393\n ],\n [\n -80.419921875,\n 28.65203063036226\n ],\n [\n -83.232421875,\n 28.65203063036226\n ],\n [\n -83.232421875,\n 25.16517336866393\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetler, J. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":258049,"corporation":false,"usgs":false,"family":"Hostetler","given":"J.","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":216734,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kosempa, M.","contributorId":258050,"corporation":false,"usgs":false,"family":"Kosempa","given":"M.","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, H.","contributorId":258052,"corporation":false,"usgs":false,"family":"Edwards","given":"H.","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rood, K.","contributorId":258054,"corporation":false,"usgs":false,"family":"Rood","given":"K.","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815616,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barton, S.","contributorId":258057,"corporation":false,"usgs":false,"family":"Barton","given":"S.","email":"","affiliations":[{"id":52219,"text":"Mote","active":true,"usgs":false}],"preferred":false,"id":815617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":815618,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223118,"text":"70223118 - 2021 - Knowledge inventory of foundational data products in planetary science","interactions":[],"lastModifiedDate":"2021-08-11T12:27:54.283197","indexId":"70223118","displayToPublicDate":"2021-01-29T07:26:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8607,"text":"The Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Knowledge inventory of foundational data products in planetary science","docAbstract":"

Some of the key components of any Planetary Spatial Data Infrastructure (PDSI) are the data products that end-users wish to discover, access, and interrogate. One precursor to the implementation of a PSDI is a knowledge inventory that catalogs what products are available, from which data producers, and at what initially understood data qualities. We present a knowledge inventory of foundational PSDI data products: geodetic coordinate reference frames, elevation or topography, and orthoimages or orthomosaics. Additionally, we catalog the available gravity models that serve as critical data for the assessment of spatial location, spatial accuracy, and ultimately spatial efficacy. We strengthen our previously published definitions of foundational data products to assist in solidifying a common vocabulary that will improve communication about these essential data products.

","language":"English","publisher":"IOP Science","doi":"10.3847/psj/abcb94","usgsCitation":"Laura, J., and Beyer, R.A., 2021, Knowledge inventory of foundational data products in planetary science: The Planetary Science Journal, v. 2, no. 1, 18, 28 p., https://doi.org/10.3847/psj/abcb94.","productDescription":"18, 28 p.","ipdsId":"IP-115047","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":453661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/abcb94","text":"Publisher Index Page"},{"id":387838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":821035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beyer, Ross A.","contributorId":264165,"corporation":false,"usgs":false,"family":"Beyer","given":"Ross","email":"","middleInitial":"A.","affiliations":[{"id":37319,"text":"SETI Institute","active":true,"usgs":false}],"preferred":false,"id":821036,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219570,"text":"70219570 - 2021 - Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","interactions":[],"lastModifiedDate":"2021-05-27T13:23:08.289537","indexId":"70219570","displayToPublicDate":"2021-01-29T07:04:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3517,"text":"Talanta","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","docAbstract":"

Spike- and blank-based procedures were applied to estimate the detection limits (DLs) for example analytes from inorganic and organic methods for water samples to compare with the U.S. Environmental Protection Agency's (EPA) Method Detection Limit (MDL) procedures (revisions 1.11 and 2.0). The multi-concentration spike-based procedures ASTM Within-laboratory Critical Level (DQCALC) and EPA's Lowest Concentration Minimum Reporting Level were compared in one application, with DQCALC further applied to many methods. The blank-based DLs, MDLb99 (99th percentile) or MDLbY (= mean blank concentration + s × t), estimated using large numbers (>100) of blank samples often provide DLs that better approach or achieve the desired ≤1% false positive risk level compared to spike-based DLs. For primarily organic methods that do not provide many uncensored blank results, spike-based DQCALC or MDL rev. 2.0 are needed to simulate the blank distribution and estimate the DL. DQCALC is especially useful for estimating DLs for multi-analyte methods having very different analyte response characteristics. Time series plots of DLs estimated using different procedures reveal that DLs are dependent on the applied procedure, should not be expected to be static over time, and seem best viewed as falling over a range versus being a single value. Use of both blank- and spike-based DL procedures help inform this DL range. Data reporting conventions that censor data at a threshold and report “less than” that threshold concentration as the reporting level have unknown and potentially high false negative risk. The U.S. Geological Survey National Water Quality Laboratory's Laboratory Reporting Level (LRL) convention (applied primarily to organic methods) attempts to simultaneously minimize both the false positive and false negative risk when <LRL is reported and data between DL and the higher LRL are allowed to be reported.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.talanta.2021.122139","usgsCitation":"Foreman, W.T., Williams, T.L., Furlong, E., Hemmerle, D., Stetson, S., Jha, V.K., Noriega, M., Decess, J.A., Reed-Parker, C., and Sandstrom, M.W., 2021, Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures: Talanta, v. 228, 122139, 15 p., https://doi.org/10.1016/j.talanta.2021.122139.","productDescription":"122139, 15 p.","ipdsId":"IP-121087","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":436530,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MUSPFI","text":"USGS data release","linkHelpText":"Data from USGS National Water Quality Laboratory methods used to calculate and compare detection limits estimated using single- and multi-concentration spike-based and blank-based procedures"},{"id":385078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foreman, William T. 0000-0002-2530-3310 wforeman@usgs.gov","orcid":"https://orcid.org/0000-0002-2530-3310","contributorId":190786,"corporation":false,"usgs":true,"family":"Foreman","given":"William","email":"wforeman@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":814196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Teresa Lynne 0000-0002-9507-9350","orcid":"https://orcid.org/0000-0002-9507-9350","contributorId":257407,"corporation":false,"usgs":true,"family":"Williams","given":"Teresa","email":"","middleInitial":"Lynne","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hemmerle, Dawn 0000-0002-9495-6681","orcid":"https://orcid.org/0000-0002-9495-6681","contributorId":257409,"corporation":false,"usgs":true,"family":"Hemmerle","given":"Dawn","email":"","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stetson, Sarah 0000-0002-4930-4748 sstetson@usgs.gov","orcid":"https://orcid.org/0000-0002-4930-4748","contributorId":216528,"corporation":false,"usgs":true,"family":"Stetson","given":"Sarah","email":"sstetson@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jha, Virendra K. 0000-0002-1076-0738 vkjha@usgs.gov","orcid":"https://orcid.org/0000-0002-1076-0738","contributorId":257416,"corporation":false,"usgs":true,"family":"Jha","given":"Virendra","email":"vkjha@usgs.gov","middleInitial":"K.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Noriega, Mary C 0000-0002-4426-3553","orcid":"https://orcid.org/0000-0002-4426-3553","contributorId":257413,"corporation":false,"usgs":false,"family":"Noriega","given":"Mary C","affiliations":[{"id":52011,"text":"USGS, National Water Quality Laboratory, retired","active":true,"usgs":false}],"preferred":false,"id":814201,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Decess, Jessica A 0000-0002-4202-3265","orcid":"https://orcid.org/0000-0002-4202-3265","contributorId":257414,"corporation":false,"usgs":false,"family":"Decess","given":"Jessica","email":"","middleInitial":"A","affiliations":[{"id":52014,"text":"Formerly: Cherokee Nation Technology Solutions, Denver, CO; Currently: The Medical Center of Aurora, Aurora, CO","active":true,"usgs":false}],"preferred":false,"id":814202,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reed-Parker, Carmen 0000-0001-9579-578X","orcid":"https://orcid.org/0000-0001-9579-578X","contributorId":257415,"corporation":false,"usgs":false,"family":"Reed-Parker","given":"Carmen","email":"","affiliations":[{"id":52011,"text":"USGS, National Water Quality Laboratory, retired","active":true,"usgs":false}],"preferred":false,"id":814203,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":814204,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229334,"text":"70229334 - 2021 - Detecting resource limitation in a large herbivore population is enhanced with measures of nutritional condition","interactions":[],"lastModifiedDate":"2022-03-03T23:56:22.478536","indexId":"70229334","displayToPublicDate":"2021-01-28T17:46:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Detecting resource limitation in a large herbivore population is enhanced with measures of nutritional condition","docAbstract":"

Resource limitation at the population level is a function of forage quality and its abundance relative to its per capita availability, which in turn, determines nutritional condition of individuals. Effects of resource limitation on population dynamics in ungulates often occur through predictable and sequential changes in vital rates, which can enable assessments of how resource limitation influences population growth. We tested theoretical predictions of bottom-up (i.e., resource limitation) forcing on moose (Alces alces) through the lens of vital rates by quantifying the relative influence of intrinsic measures of nutritional condition and extrinsic measures of remotely sensed environmental data on demographic rates. We measured rates of pregnancy, parturition, juvenile, and adult survival for 82 adult females in a population where predators largely were absent. Life stage simulation analyses (LSAs) indicated that interannual fluctuations in adult survival contributed to most of the variability in λ. We then extended the LSA to estimate vital rates as a function of bottom-up covariates to evaluate their influence on λ. We detected weak signatures of effects from environmental covariates that were remotely sensed and spatially explicit to each seasonal range. Instead, nutritional condition strongly influenced rates of pregnancy, parturition, and overwinter survival of adults, clearly implicating resource limitation on λ. Our findings depart from the classic life-history paradigm of population dynamics in ungulates in that adult survival was highly variable and generated most of the variability in population growth rates. At the surface, lack of variation explained by environmental covariates may suggest weak evidence of resource limitation in the population, when nutritional condition actually underpinned most demographics. We suggest that variability in vital rates and effects of resource limitation may depend on context more than previously appreciated, and density dependence can obfuscate the relationships between remotely sensed data and demographic rates.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.522174","usgsCitation":"Oates, B.A., Monteith, K., Goheen, J., Merkle, J., Fralick, G., and Kauffman, M., 2021, Detecting resource limitation in a large herbivore population is enhanced with measures of nutritional condition: Frontiers in Ecology and Evolution, v. 8, 522174, 15 p., https://doi.org/10.3389/fevo.2020.522174.","productDescription":"522174, 15 p.","ipdsId":"IP-085031","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453662,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.522174","text":"Publisher Index Page"},{"id":396736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Upper Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.61035156249999,\n 42.85985981506279\n ],\n [\n -110.01708984374999,\n 42.85985981506279\n ],\n [\n -110.01708984374999,\n 43.45291889355465\n ],\n [\n -110.61035156249999,\n 43.45291889355465\n ],\n [\n -110.61035156249999,\n 42.85985981506279\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Oates, Brendan A.","contributorId":275241,"corporation":false,"usgs":false,"family":"Oates","given":"Brendan","email":"","middleInitial":"A.","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":837074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monteith, Kevin L.","contributorId":287798,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goheen, Jacob R.","contributorId":287799,"corporation":false,"usgs":false,"family":"Goheen","given":"Jacob R.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkle, Jerod A.","contributorId":287800,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod A.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837073,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fralick, Gary","contributorId":287797,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837075,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217705,"text":"ds1134 - 2021 - Distribution and abundance of Least Bell's Vireos and Southwestern Willow Flycatchers on the middle San Luis Rey River, San Diego County, southern California—2020 data summary","interactions":[],"lastModifiedDate":"2021-01-29T12:45:13.001068","indexId":"ds1134","displayToPublicDate":"2021-01-28T14:17:37","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1134","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego County, Southern California—2020 Data Summary","title":"Distribution and abundance of Least Bell's Vireos and Southwestern Willow Flycatchers on the middle San Luis Rey River, San Diego County, southern California—2020 data summary","docAbstract":"

We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2020. Surveys were conducted from April 13 to July 13 (vireo) and from May 14 to July 13 (flycatcher). We found 192 vireo territories, at least 150 of which were occupied by pairs. Vireo territories increased by 40 percent from 2019 to 2020 in the portion of the middle San Luis Rey River that burned as a result of a wildfire in 2017. In contrast, vireo territories decreased by 5 percent from 2019 to 2020 in the unburned portion of the middle San Luis Rey River. 

Vireos used six different habitat types in the survey area: (1) willow-cottonwood, (2) mixed willow riparian, (3) riparian scrub, (4) upland scrub, (5) willow-sycamore, and (6) non-native. Forty-nine percent of the vireos were detected in habitat characterized as willow-cottonwood, and 93 percent of the vireos were detected in habitat with greater than 50-percent native plant cover. Of the 17 banded vireos detected in the survey area, 6 were resighted with a full color-band combination. Two other vireos with single (natal) federal bands were recaptured, identified, and color-banded in 2020. Eight vireos with a single dark blue federal band, indicating that they were banded as nestlings on the lower San Luis Rey River (LSLR), could not be recaptured for identification. One vireo with a single gold federal band, indicating that it was banded as a nestling at Marine Corps Base Camp Pendleton (MCBCP), could not be recaptured for identification. The two natal vireos that were recaptured on the middle San Luis Rey River dispersed from 2.6 to 6.2 kilometers (km) from their natal territories. Banded vireos with a known age ranged from 1 to 8 years old. 

One resident flycatcher was observed in the survey area in 2020. The resident flycatcher (male) was detected in a territory of mixed willow habitat with greater than 50-percent native plant cover. He was detected as a single male from May 27 to July 2, 2020, and no evidence of pairing or nesting was observed. The male flycatcher was resighted with a unique color-band combination and had occupied the same territory since 2018.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1134","usgsCitation":"Allen, L.D., and Kus, B.E., 2021, Distribution and abundance of Least Bell's Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the middle San Luis Rey River, San Diego County, southern California—2020 data summary: U.S. Geological Survey Data Series 1134, 11 p., https://doi.org/10.3133/ds1134.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-124769","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":382770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1134/ds1134.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":382769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1134/covrthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Middle San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.33947753906249,\n 33.02248191961359\n ],\n [\n -115.76293945312499,\n 33.08233672856376\n ],\n [\n -116.35620117187499,\n 33.84760762988741\n ],\n [\n -117.55920410156249,\n 33.394759218577995\n ],\n [\n -117.33947753906249,\n 33.02248191961359\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-01-28","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Lisa D. 0000-0002-6147-3165 ldallen@usgs.gov","orcid":"https://orcid.org/0000-0002-6147-3165","contributorId":196789,"corporation":false,"usgs":true,"family":"Allen","given":"Lisa","email":"ldallen@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":809305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":809306,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217754,"text":"70217754 - 2021 - Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist","interactions":[],"lastModifiedDate":"2021-02-01T17:12:01.757871","indexId":"70217754","displayToPublicDate":"2021-01-28T11:03:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist","docAbstract":"

Quantifying the contribution of habitat dynamics relative to intrinsic population processes in regulating species persistence remains an ongoing challenge in ecological and applied conservation. Understanding these drivers and their relationship is essential for managing habitat‐dependent species, especially those that specialize in transitional habitats. Limitations in the ability of natural disturbance to mediate transitional habitat dynamics have resulted in a decline in early‐ and mid‐successional vegetation structure and prompted the need for aggressive habitat management to replace natural perturbations and increase habitat structural complexity. We describe a collaborative effort with a group of independent land managers to design an adaptive management program for restoring an imperiled ecosystem and recovering declining populations of an endemic habitat specialist. We developed a set of integrated, hierarchical models to estimate management‐mediated transition rates among vegetation classes in two dominant scrub communities and the species response (local colonization and extinction probabilities) as a function of habitat state. Models were fit using a long‐term data set of habitat and occupancy observations from 361 Florida scrub‐jay territories across two Florida counties. Occupancy model results correspond closely to previous approaches of estimating differential survival and reproductive success associated with habitat conditions, with highest colonization and lowest extinction rates estimated for those habitat states found to have the highest rates of survival and reproduction. In addition to offering an innovative approach for jointly modeling habitat and species population dynamics, the program we describe will also be of interest from a management perspective by providing guidance for developing collaborative, adaptive management frameworks from the ground up. We engaged land managers via workshops to specify objectives and desired state‐variable conditions, identify management alternatives, and elicit consensus opinions on model parameters. Treating expert opinions as pseudo‐observations to define Dirichlet priors allowed us to make use of existing management knowledge. Formal learning was then accumulated by updating transition probability estimates as management activities were implemented over the study period. We believe this adaptive management framework provides a useful approach for increasing our understanding of complex ecological relationships and hope that it will be adopted by others who have interest in informing management and conservation efforts.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3306","usgsCitation":"Eaton, M.J., Breininger, D., Nichols, J.D., Paul, F., McGee, S., Smurl, M., DeMeyer, D., Baker, J., and Zondervan, M.B., 2021, Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist: Ecosphere, v. 12, no. 1, e03306, 26 p., https://doi.org/10.1002/ecs2.3306.","productDescription":"e03306, 26 p.","ipdsId":"IP-115268","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":488926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3306","text":"Publisher Index Page"},{"id":382851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Brevard County, Indian River County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.1285400390625,\n 27.668934069896217\n ],\n [\n -80.4034423828125,\n 27.668934069896217\n ],\n [\n -80.4034423828125,\n 28.64479960910591\n ],\n [\n -81.1285400390625,\n 28.64479960910591\n ],\n [\n -81.1285400390625,\n 27.668934069896217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":809484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breininger, David","contributorId":248597,"corporation":false,"usgs":false,"family":"Breininger","given":"David","affiliations":[{"id":49958,"text":"NASA Ecology Program","active":true,"usgs":false}],"preferred":false,"id":809485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":809486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul, F.","contributorId":248598,"corporation":false,"usgs":false,"family":"Paul","given":"F.","affiliations":[],"preferred":false,"id":809487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGee, Samantha","contributorId":248609,"corporation":false,"usgs":false,"family":"McGee","given":"Samantha","email":"","affiliations":[],"preferred":false,"id":809522,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smurl, Michelle","contributorId":248610,"corporation":false,"usgs":false,"family":"Smurl","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":809523,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeMeyer, David","contributorId":248611,"corporation":false,"usgs":false,"family":"DeMeyer","given":"David","email":"","affiliations":[],"preferred":false,"id":809524,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baker, Jonny","contributorId":248612,"corporation":false,"usgs":false,"family":"Baker","given":"Jonny","email":"","affiliations":[],"preferred":false,"id":809525,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zondervan, Maria B.","contributorId":248614,"corporation":false,"usgs":false,"family":"Zondervan","given":"Maria","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":809526,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70218170,"text":"70218170 - 2021 - Joint species distribution models of Everglades wading birds to inform restoration planning","interactions":[],"lastModifiedDate":"2023-07-07T14:08:20.276686","indexId":"70218170","displayToPublicDate":"2021-01-28T10:04:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Joint species distribution models of Everglades wading birds to inform restoration planning","docAbstract":"

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0245973","usgsCitation":"D’Acunto, L., Pearlstine, L.G., and Romanach, S., 2021, Joint species distribution models of Everglades wading birds to inform restoration planning: PLoS ONE, v. 16, no. 1, e0245973, 21 p.; Data Release, https://doi.org/10.1371/journal.pone.0245973.","productDescription":"e0245973, 21 p.; Data Release","ipdsId":"IP-119201","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453665,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245973","text":"Publisher Index Page"},{"id":383272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418748,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P934K8T0","text":"EverWaders species distribution model development and output in the Greater Everglades from 2000-2009","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.419921875,\n 25.209911213827688\n ],\n [\n -80.474853515625,\n 25.27450351782018\n ],\n [\n -80.562744140625,\n 25.348990395713393\n ],\n [\n -80.562744140625,\n 25.55730943578312\n ],\n [\n -80.299072265625,\n 26.288490072961164\n ],\n [\n -80.1617431640625,\n 26.426308999847024\n ],\n [\n -80.26611328125,\n 26.711266913515747\n ],\n [\n -80.4473876953125,\n 26.716173757934094\n ],\n [\n -80.5133056640625,\n 26.554136386183785\n ],\n [\n -80.79345703125,\n 26.504988828743404\n ],\n [\n -81.0296630859375,\n 26.455820238459893\n ],\n [\n -81.00082397460938,\n 26.292183791046618\n ],\n [\n -81.47872924804688,\n 26.223215001067558\n ],\n [\n -81.64352416992188,\n 26.172694044887898\n ],\n [\n -81.78085327148438,\n 26.078987535225927\n ],\n [\n -81.74514770507812,\n 25.90617390922084\n ],\n [\n -81.6339111328125,\n 25.808545671771615\n ],\n [\n -81.38259887695312,\n 25.720735134412106\n ],\n [\n -81.24252319335938,\n 25.492868271257127\n ],\n [\n -81.18209838867188,\n 25.370086680063082\n ],\n [\n -81.17660522460938,\n 25.21488107113259\n ],\n [\n -81.03790283203125,\n 25.069429002821355\n ],\n [\n -80.58059692382812,\n 25.14652775303499\n ],\n [\n -80.419921875,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":810304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810305,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225621,"text":"70225621 - 2021 - The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed","interactions":[],"lastModifiedDate":"2021-10-28T13:21:41.798091","indexId":"70225621","displayToPublicDate":"2021-01-28T08:15:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed","docAbstract":"

The 2018 US Geological Survey National Seismic Hazard Model (NSHM) incorporates new data and updated science to improve the underlying earthquake and ground motion forecasts for the conterminous United States. The NSHM considers many new data and component input models: (1) new earthquakes between 2013 and 2017 and updated earthquake magnitudes for some earlier earthquakes; (2) two updated smoothed seismicity models to forecast earthquake rates; (3) two suites of new central and eastern US (CEUS) ground motion models (GMMs) to translate ground shaking for various earthquake sizes and source-to-site distances considered in the model; (4) two CEUS GMMs for aleatory variability; (5) two CEUS site-effect models that modify ground shaking based on alternative shallow site conditions; (6) more advanced western US (WUS) lithologic and structural information to assess basin site effects for selected urban regions; and (7) a more comprehensive range of outputs (22 periods and 8 site classes) than in previous versions of the NSHMs. Each of these new datasets and models produces changes in the probabilistic ground shaking levels that are spatially and statistically analyzed. Recent earthquakes or changes to some older earthquake magnitudes and locations mostly result in probabilistic ground shaking levels that are similar to previous models, but local changes can reach up to +80% and −60% compared to the 2014 model. Newly developed CEUS models for GMMs, aleatory variability, and site effects cause overall changes up to ±64%. The addition of the WUS basin amplifications causes changes of up to +60% at longer periods for sites overlying deep soft soils. Across the conterminous United States, the hazard changes in the model are mainly caused by new GMMs in the CEUS, by sedimentary basin effects for long periods (≥1 s) in the WUS, and by seismicity changes for short (0.2 s) and long (1 s) periods for both areas.

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field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":825973,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":825974,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70217819,"text":"70217819 - 2021 - Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation","interactions":[],"lastModifiedDate":"2021-02-04T13:58:17.537836","indexId":"70217819","displayToPublicDate":"2021-01-28T07:53:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation","docAbstract":"

Sediment transfer, or connectivity, by aeolian processes between channel-proximal and upland deposits in river valleys is important for the maintenance of river corridor biophysical characteristics. In regulated river systems, dams control the magnitude and duration of discharge. Alterations to the flow regime driven by dams that increase the inundation duration of sediment, or which drive the encroachment of vegetation into areas formerly composed of labile sediment and result in channel narrowing, may reduce sediment transfer from near-channel deposits to uplands via aeolian processes. Employing spatial methods developed by Kasprak et al (2018 Prog. Phys. Geogr.), here we use data describing the areal extent of bare (i.e. subaerially exposed and non-vegetated) sediment along 168 km of the Colorado River downstream from Glen Canyon Dam in Grand Canyon, USA, in conjunction with inundation extent modeling to forecast how future flows of this highly regulated river will drive changes in the areal extent of sediment available for aeolian transport. We also compare modern bare sediment area to that which presumably would have existed under pre-dam hydrographs. Over the next two decades, the planned flow regime from Glen Canyon Dam will result in slight decreases in bare sediment area (−1%) on an annual scale. This is in contrast to pre-dam years, when unregulated low flows led to marked increases in bare sediment area as compared to the current discharge regime. Our findings also indicate that ~75% of bare sediment in the study reach is inundated continuously at present, owing to increased baseflows in the post-dam flow regime; consequently, any reductions in flows below modern-day low discharges have the potential to expose large areas of bare sediment. We use vegetation modeling to quantify areas susceptible to vegetation encroachment under future flows, finding that 80% of bare sediment area is suitable for colonization by invasive tamarisk under the current flow regime. Our findings imply that the Colorado River in Grand Canyon, a system marked by widespread erosion of sediment resources and encroachment of riparian vegetation in the post-dam period, is likely to continue to see decreasing bare sediment extent over the coming decades in the absence of direct intervention through flow regime modification or widespread vegetation removal.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abc9e4","usgsCitation":"Kasprak, A., Sankey, J.B., and Butterfield, B.J., 2021, Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation: Environmental Research Letters, v. 16, no. 1, 014029, 14 p., https://doi.org/10.1088/1748-9326/abc9e4.","productDescription":"014029, 14 p.","ipdsId":"IP-120844","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":486997,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abc9e4","text":"Publisher Index Page"},{"id":436532,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P918E2P3","text":"USGS data release","linkHelpText":"Discharge records and sand extents along the Colorado River between Glen Canyon Dam and Phantom Ranch, Arizona"},{"id":382945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.97241210937499,\n 35.576916524038616\n ],\n [\n -111.434326171875,\n 35.576916524038616\n ],\n [\n -111.434326171875,\n 36.57142382346277\n ],\n [\n -112.97241210937499,\n 36.57142382346277\n ],\n [\n -112.97241210937499,\n 35.576916524038616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":809824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":809825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":809826,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217716,"text":"70217716 - 2021 - Why Lyme disease is common in the northern US, but rare in the south: The roles of host choice, host-seeking behavior, and tick density","interactions":[],"lastModifiedDate":"2021-01-29T13:41:03.444159","indexId":"70217716","displayToPublicDate":"2021-01-28T07:33:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2979,"text":"PLoS Biology","active":true,"publicationSubtype":{"id":10}},"title":"Why Lyme disease is common in the northern US, but rare in the south: The roles of host choice, host-seeking behavior, and tick density","docAbstract":"

Lyme disease is common in the northeastern United States, but rare in the southeast, even though the tick vector is found in both regions. Infection prevalence of Lyme spirochetes in host-seeking ticks, an important component to the risk of Lyme disease, is also high in the northeast and northern midwest, but declines sharply in the south. As ticks must acquire Lyme spirochetes from infected vertebrate hosts, the role of wildlife species composition on Lyme disease risk has been a topic of lively academic discussion. We compared tick–vertebrate host interactions using standardized sampling methods among 8 sites scattered throughout the eastern US. Geographical trends in diversity of tick hosts are gradual and do not match the sharp decline in prevalence at southern sites, but tick–host associations show a clear shift from mammals in the north to reptiles in the south. Tick infection prevalence declines north to south largely because of high tick infestation of efficient spirochete reservoir hosts (rodents and shrews) in the north but not in the south. Minimal infestation of small mammals in the south results from strong selective attachment to lizards such as skinks (which are inefficient reservoirs for Lyme spirochetes) in the southern states. Selective host choice, along with latitudinal differences in tick host-seeking behavior and variations in tick densities, explains the geographic pattern of Lyme disease in the eastern US.

","language":"English","publisher":"PLoS","doi":"10.1371/journal.pbio.3001066","usgsCitation":"Ginsberg, H., Hickling, G.J., Burke, R.L., Ogden, N.H., Beati, L., LeBrun, R.A., Arsnoe, I.M., Gerhold, R., Han, S., Jackson, K., Maestas, L., Moody, T., Pang, G., Ross, B., Rulison, E.L., and Tsao, J.I., 2021, Why Lyme disease is common in the northern US, but rare in the south: The roles of host choice, host-seeking behavior, and tick density: PLoS Biology, v. 19, no. 1, e3001066, 20 p., https://doi.org/10.1371/journal.pbio.3001066.","productDescription":"e3001066, 20 p.","ipdsId":"IP-117549","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":453671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pbio.3001066","text":"Publisher Index Page"},{"id":382785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University","active":true,"usgs":false}],"preferred":false,"id":809362,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70220311,"text":"70220311 - 2021 - The optical river bathymetry toolkit","interactions":[],"lastModifiedDate":"2021-05-04T12:12:56.975607","indexId":"70220311","displayToPublicDate":"2021-01-28T07:10:52","publicationYear":"2021","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":"The optical river bathymetry toolkit","docAbstract":"

Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3773","usgsCitation":"Legleiter, C.J., 2021, The optical river bathymetry toolkit: River Research and Applications, v. 4, no. 37, p. 555-568, https://doi.org/10.1002/rra.3773.","productDescription":"14 p.","startPage":"555","endPage":"568","ipdsId":"IP-119553","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3773","text":"Publisher Index Page"},{"id":385444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"37","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":815120,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218778,"text":"70218778 - 2021 - Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","interactions":[],"lastModifiedDate":"2021-05-13T15:53:26.945858","indexId":"70218778","displayToPublicDate":"2021-01-28T07:10:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","docAbstract":"
  1. Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales.
  2. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data.
  3. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product.
  4. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13564","usgsCitation":"Allred, B.W., Bestelmeyer, B.T., Boyd, C.S., Brown, C., Davies, K.W., Duniway, M.C., Ellsworth, L.M., Erickson, T.A., Fuhlendorf, S.D., Griffiths, T.V., Jansen, V., Jones, M.O., Karl, J.W., Knight, A.C., Maestas, J.D., Maynard, J.J., McCord, S.E., Naugle, D., Starns, H.D., Twidwell, D., and Uden, D.R., 2021, Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty: Methods in Ecology and Evolution, v. 12, no. 5, p. 841-849, https://doi.org/10.1111/2041-210X.13564.","productDescription":"9 p.","startPage":"841","endPage":"849","ipdsId":"IP-122860","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453676,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/2041-210x.13564","text":"External Repository"},{"id":384299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Brady W.","contributorId":255105,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bestelmeyer, Brandon T.","contributorId":26180,"corporation":false,"usgs":false,"family":"Bestelmeyer","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":811806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Christopher","contributorId":255107,"corporation":false,"usgs":false,"family":"Brown","given":"Christopher","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davies, Kirk W.","contributorId":255108,"corporation":false,"usgs":false,"family":"Davies","given":"Kirk","email":"","middleInitial":"W.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":811811,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erickson, Tyler A.","contributorId":255110,"corporation":false,"usgs":false,"family":"Erickson","given":"Tyler","email":"","middleInitial":"A.","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fuhlendorf, Samuel D.","contributorId":171488,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":811813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Griffiths, Timothy V.","contributorId":255111,"corporation":false,"usgs":false,"family":"Griffiths","given":"Timothy","email":"","middleInitial":"V.","affiliations":[{"id":51437,"text":"USDA Natural Resources Conservation Service, Landscape Initiatives Team, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":811814,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jansen, Vincent","contributorId":255112,"corporation":false,"usgs":false,"family":"Jansen","given":"Vincent","email":"","affiliations":[{"id":51438,"text":"Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID 83844, USA","active":true,"usgs":false}],"preferred":false,"id":811815,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Matthew O.","contributorId":169805,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"O.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":811816,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":811817,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811818,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Maestas, Jeremy D.","contributorId":219258,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":39978,"text":"USDA Natural Resources Conservation Service, Redmond, OR","active":true,"usgs":false}],"preferred":false,"id":811819,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Maynard, Jonathan J.","contributorId":216782,"corporation":false,"usgs":false,"family":"Maynard","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":39514,"text":"USDA-Agricultural Resource Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":811820,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":811821,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811822,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Starns, Heath D.","contributorId":131091,"corporation":false,"usgs":false,"family":"Starns","given":"Heath","email":"","middleInitial":"D.","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":811823,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":811824,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":811825,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70219193,"text":"70219193 - 2021 - Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","interactions":[],"lastModifiedDate":"2021-06-01T17:29:08.413936","indexId":"70219193","displayToPublicDate":"2021-01-28T07:07:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7951,"text":"Earth Surfaces Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","docAbstract":"

In this study, we captured how a river channel responds to a sediment pulse originating from a dam removal using multiple lines of evidence derived from streamflow gages along the Patapsco River, Maryland, USA. Gages captured characteristics of the sediment pulse, including travel times of its leading edge (~7.8 km yr−1) and peak (~2.6 km yr−1) and suggest both translation and increasing dispersion. The pulse also changed local hydraulics and energy conditions, increasing flow velocities and Froude number, due to bed fining, homogenization and/or slope adjustment. Immediately downstream of the dam, recovery to pre‐pulse conditions occurred within the year, but farther downstream recovery was slower, with the tail of the sediment pulse working through the lower river by the end of the study 7 years later.

The patterns and timing of channel change associated with the sediment pulse were not driven by large flow or suspended sediment‐transporting events, with change mostly occurring during lower flows. This suggests pulse mobility was controlled by process‐factors largely independent of high flow.

In contrast, persistent changes occurred to out‐of‐channel flooding dynamics. Stage associated with flooding increased during the arrival of the sediment pulse, 1 to 2 years after dam removal, suggesting persistent sediment deposition at the channel margins and nearby floodplain. This resulted in National Weather Service‐indicated flood stages being attained by 3–43% smaller discharges compared to earlier in the study period.

This study captured a two‐signal response from the sediment pulse: (1) short‐ to medium‐term (weeks to months) translation and dispersion within the channel, resulting in aggradation and recovery of bed elevations and changing local hydraulics; and (2) dispersion and persistent longer‐term (years) effects of sediment deposition on overbank surfaces. This study further demonstrated the utility of US Geological Survey gage data to quantify geomorphic change, increase temporal resolution, and provide insights into trajectories of change over varying spatial and temporal scales.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.5083","usgsCitation":"Cashman, M.J., Gellis, A.C., Boyd, E.L., Collins, M.J., Anderson, S.W., Mcfarland, B.D., and Ryan, A.M., 2021, Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data: Earth Surfaces Processes and Landforms, v. 46, no. 6, p. 1145-1159, https://doi.org/10.1002/esp.5083.","productDescription":"15 p.","startPage":"1145","endPage":"1159","ipdsId":"IP-113441","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":436533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9REXNQ9","text":"USGS data release","linkHelpText":"Data for Specific Gage Analysis on the Patapsco River, 2010-2017"},{"id":384751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patapsco River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.9317626953125,\n 39.38738660316804\n ],\n [\n -76.98257446289062,\n 39.35394512666976\n ],\n [\n -76.88438415527344,\n 39.31198794598777\n ],\n [\n -76.8218994140625,\n 39.29976783250087\n ],\n [\n -76.7999267578125,\n 39.26043647112078\n ],\n [\n -76.75666809082031,\n 39.216295294574024\n ],\n [\n -76.68937683105469,\n 39.21097520599528\n ],\n [\n -76.60697937011719,\n 39.22480659786848\n ],\n [\n -76.9317626953125,\n 39.38738660316804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":813165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Eric L. 0000-0002-1473-967X","orcid":"https://orcid.org/0000-0002-1473-967X","contributorId":256743,"corporation":false,"usgs":true,"family":"Boyd","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Matthias J. 0000-0003-4238-2038","orcid":"https://orcid.org/0000-0003-4238-2038","contributorId":196365,"corporation":false,"usgs":false,"family":"Collins","given":"Matthias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":813168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mcfarland, Brett Dare 0000-0002-2941-4966","orcid":"https://orcid.org/0000-0002-2941-4966","contributorId":256744,"corporation":false,"usgs":true,"family":"Mcfarland","given":"Brett","email":"","middleInitial":"Dare","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ryan, Ashley Mattie 0000-0001-5647-7447","orcid":"https://orcid.org/0000-0001-5647-7447","contributorId":256746,"corporation":false,"usgs":true,"family":"Ryan","given":"Ashley","email":"","middleInitial":"Mattie","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813171,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217664,"text":"sir20205121 - 2021 - Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","interactions":[],"lastModifiedDate":"2021-01-28T01:29:43.632301","indexId":"sir20205121","displayToPublicDate":"2021-01-27T16:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5121","displayTitle":"Spring Types and Contributing Aquifers from Water-Chemistry and Multivariate Statistical Analyses for Seeps and Springs in Theodore Roosevelt National Park, North Dakota, 2018","title":"Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","docAbstract":"

Water resources in Theodore Roosevelt National Park, North Dakota, support wildlife, visitors, and staff, and play a vital role in supporting the native ecology of the park. The U.S. Geological Survey, in cooperation with the National Park Service, completed field work in 2018 for a study to address concerns about water availability and possible sources of groundwater contamination for seeps and springs in Theodore Roosevelt National Park. The objective of the study was to improve hydrologic knowledge and determine the water composition of 11 seeps and springs in the park by collecting water-chemistry data at springs, streams, wells, and rain collectors.

Water samples were collected at 26 sites at springs, streams, wells, and rain collectors in the North and South Units of Theodore Roosevelt National Park. Samples in the North Unit were collected at 5 springs, 1 stream, 2 wells, and 1 rain collector. Samples in the South Unit were collected at 6 springs, 2 streams, 8 wells, and 1 rain collector. Samples from springs, streams, and wells were collected in May, July, and September 2018. Samples from rain collectors were collected when enough daily precipitation accumulated in the collectors. Sampled precipitation events during the study period were in May, June, July, August, and September 2018. Physical properties of sampled water—temperature, pH, and specific conductance—were measured in the field. Water samples were analyzed for stable isotopes of oxygen and hydrogen and for chloride concentration. Recharge rates for aquifers supplying springs were determined using precipitation volume and chloride concentrations for a 12-day period before the sample-collection date. Multivariate statistical analysis methods used on water-chemistry data included principal component analysis, cluster analysis, and end-member mixing analysis.

Water composition was used to determine the spring type and contributing aquifers for 11 springs in the North and South Units of Theodore Roosevelt National Park from analyses of water-chemistry data between May and September 2018. In the North Unit, Achenbach Spring was classified as a filtration spring with water from an unconfined part of the upper Fort Union aquifer and infiltration of precipitation. Hagen Spring, Mandal Spring, and Stevens Spring were classified as contact springs supplied by semiconfined parts of the upper Fort Union aquifer. Overlook Spring at one time may have been a natural spring or seep but now is a developed spring that behaves like a flowing artesian well completed in a confined part of the upper Fort Union aquifer. In the South Unit, six springs were classified into two spring types: filtration and contact springs. Boicourt Spring and Sheep Butte Spring were classified as filtration springs that have water supplied by unconfined parts of the upper Fort Union aquifer and infiltrated precipitation. Big Plateau Spring, Lone Tree Spring, Sheep Pasture Spring, and Southeast Corner Spring were classified as contact springs that receive waters from a semiconfined part of the upper Fort Union aquifer.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205121","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., and Eldridge, W.G., 2021, Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5121, 48 p., https://doi.org/10.3133/sir20205121.","productDescription":"Report: viii, 48 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115769","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5121/coverthb.jpg"},{"id":382694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5121/sir20205121.pdf","text":"Report","size":"4.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"sir2020-5121"},{"id":382695,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63334655761719,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 46.87990702860922\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.48983764648438,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.52832925298343\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63677978515625,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.22726254715105\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishedDate":"2021-01-27","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eldridge, William G. 0000-0002-3562-728X","orcid":"https://orcid.org/0000-0002-3562-728X","contributorId":208529,"corporation":false,"usgs":true,"family":"Eldridge","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809197,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217663,"text":"sir20205134 - 2021 - Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data","interactions":[],"lastModifiedDate":"2021-01-28T01:40:20.23064","indexId":"sir20205134","displayToPublicDate":"2021-01-27T12:05:58","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5134","displayTitle":"Groundwater Flow Conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada: A Synthesis of Geologic, Hydrologic, Hydraulic-Property, and Tritium Data","title":"Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data","docAbstract":"

This report provides a groundwater-flow conceptualization that integrates geologic, hydrologic, hydraulic-property, and radionuclide data in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Groundwater flow in the PMOV basin is of interest because 82 underground nuclear tests were detonated, most near or below the water table. A potentiometric map and nine sets of hydrostratigraphic and hydrologic cross sections supplement the conceptualization. 

Potentiometric contours indicate that groundwater in the PMOV basin generally flows south-southwest and discharges at Oasis Valley. Groundwater encounters an alternating sequence of low- and high-transmissivity rocks, referred to as dams and pools, respectively, as it moves from east to west across eastern Pahute Mesa. Flow from all Pahute Mesa nuclear tests is to Oasis Valley and is well-constrained by water-level data. Flow converges along a corridor of high transmissivity between Pahute Mesa and Oasis Valley. 

The location of the lateral PMOV basin boundary is well defined, and this boundary, with a few minor exceptions, represents a no-flow boundary. Some boundary uncertainty exists in the northeastern part of the basin, but potential flow-rate estimates across the northeastern boundary resulting from this uncertainty are small relative to the basin groundwater budget. 

Recharge in the PMOV basin is derived from episodic pulses of modern water and the diffuse percolation of old water (greater than 1,000 years). Episodic recharge is a minor recharge component observed as a rise in groundwater levels that occurs 3 months to 1 year following a wet winter. Minor amounts of episodic recharge through an unsaturated zone in excess of 1,000 feet (ft) requires preferential flow through faults and fractures. The dominant recharge component is slow, steady, diffuse percolation of old water through the unsaturated zone. A large component of old water recharging the groundwater system is consistent with observations of isotopically light deuterium and oxygen 18 compositions in water from wells on Pahute Mesa and central Oasis Valley. About half the recharge in the PMOV basin is derived from the eastern Pahute Mesa area. The remaining recharge is derived primarily from other highland areas including Timber Mountain, Belted and Kawich Ranges, and Black Mountain. 

The PMOV groundwater system is nearly steady state, where recharge is balanced by the 5,900 acre-feet per year of natural discharge at Oasis Valley. This assumption is reasonable because the basin is dominated by steady-state conditions, where long-term changes in groundwater storage are minimal. Total groundwater withdrawals from 1963 to 2018 have amounted to less than 10 percent of annual groundwater discharge and less than 0.2 percent of the basin’s groundwater storage. Therefore, present-day (2020) conditions are considered representative of predevelopment (pre-1950) conditions in nearly all areas of the basin. 

The lower PMOV basin boundary is defined at 4,000 ft below the water table to encompass all underground nuclear tests and tritium plumes. This boundary defines the lower boundary of radionuclide migration. However, nearly all flow and tritium transport occur in the upper 1,600 ft of the saturated zone because a transmissivity-with-depth relation indicates that greater than 90 percent of the transmissivity contributing to groundwater flow occurs within 1,600 ft of the water table. Rocks at deeper depths have low transmissivity because argillic and mineralized alterations plug the fractures. 

Volcanic rocks form the primary aquifers and confining units in the PMOV basin. Volcanic hydrogeologic units (HGUs) and hydrostratigraphic units (HSUs) have transmissivity distributions that span up to eight orders of magnitude with considerable overlap between distributions. Despite the large overlap between units, mean transmissivities of aquifers are one-to-two orders of magnitude greater than the confining units. However, all volcanic-rock HGUs and HSUs are composite units, meaning that they can function spatially as either an aquifer or confining unit.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205134","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., Fenelon, J.M., and Paylor, R.L., 2021, Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data: U.S. Geological Survey Scientific Investigations Report 2020–5134, 100 p., https://doi.org/10.3133/sir20205134.","productDescription":"Report: viii, 100 p.; 2 Plates: 26.00 x 42.00 inches and 120.01 x 36.00 inches; 7 Appendixes","onlineOnly":"Y","ipdsId":"IP-095406","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":382683,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix2.xlsx","text":"Appendix 2","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 2"},{"id":382684,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix3.xlsm","text":"Appendix 3","size":"530 KB xlsm","description":"SIR 2020-5134 Appendix 3"},{"id":382685,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix4.xlsx","text":"Appendix 4","size":"6.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 4"},{"id":382681,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate02.pdf","text":"Plate 2","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 2"},{"id":382678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5134/coverthb.jpg"},{"id":382679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134"},{"id":382680,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate01.pdf","text":"Plate 1","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 1"},{"id":382682,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix1.xlsx","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 1"},{"id":382688,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix7.xlsx","text":"Appendix 7","size":"433 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 7"},{"id":382687,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix6.xlsx","text":"Appendix 6","size":"856 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 6"},{"id":382686,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix5.xlsx","text":"Appendix 5","size":"799 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 5"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 38.00\n ],\n [\n -117.00,\n 38.00\n ],\n [\n -117.00,\n 36.65079252503471\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819

","tableOfContents":"","publishedDate":"2021-01-27","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, Tracie R. 0000-0001-8553-0323 tjackson@usgs.gov","orcid":"https://orcid.org/0000-0001-8553-0323","contributorId":150591,"corporation":false,"usgs":true,"family":"Jackson","given":"Tracie","email":"tjackson@usgs.gov","middleInitial":"R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fenelon, Joseph M. 0000-0003-4449-245X jfenelon@usgs.gov","orcid":"https://orcid.org/0000-0003-4449-245X","contributorId":2355,"corporation":false,"usgs":true,"family":"Fenelon","given":"Joseph","email":"jfenelon@usgs.gov","middleInitial":"M.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paylor, Randall L. 0000-0002-1059-6384","orcid":"https://orcid.org/0000-0002-1059-6384","contributorId":248456,"corporation":false,"usgs":true,"family":"Paylor","given":"Randall","email":"","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809195,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229376,"text":"70229376 - 2021 - Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species","interactions":[],"lastModifiedDate":"2022-03-04T17:38:48.028579","indexId":"70229376","displayToPublicDate":"2021-01-27T11:23:36","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":6053,"text":"Hawaii Cooperative Studies Unit Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCFRU-001","title":"Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species","docAbstract":"

The data-limited stock assessment models used to monitor the status of coral reef fish species in the Western Pacific region are dependent upon accurate estimates of standing stock biomass generated from underwater visual surveys of reefs. However, the imperfect detection of and variable occupancy of habitat by reef fishes are not currently accounted for in these estimates. Therefore, the objective of this project was to estimate detection and occupancy coefficients for the species listed in the Western Pacific Regional Fishery Management Council’s Fishery Ecosystem Plans by analyzing the Pacific Island Fishery Science Center-Coral Reef Ecosystem Program Reef Fish Dataset. These detection and occupancy coefficients would then be applied to refine standing stock biomass estimates. In general, species with higher detection probabilities and/or lower occupancy rates tended to exhibit the greatest differences in the estimates of standing stock biomass calculated with and without accounting for detection and occupancy. The standing stock biomass of most reef fish species seem to be underestimated when detection and occupancy are not accounted for. However, the standing stock biomass of larger-bodied targeted species, such as jacks, snappers, and groupers, seem to be over-estimated relative to the estimates generated when accounting for occupancy and detection. While there are still issues to resolve regarding how well the current data collection methods meet the underlying assumptions of the detection and occupancy modeling approach, the inclusion of detection and occupancy coefficients seems likely to improve estimates of standing stock biomass of coral reef fish species.

","language":"English","publisher":"Hawaii Cooperative Research Studies Unit","collaboration":"Western Pacific Regional Fishery Management Council","usgsCitation":"Suarez, B., and Grabowski, T.B., 2021, Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species: Hawaii Cooperative Studies Unit Technical Report HCFRU-001, 22 p.","productDescription":"22 p.","ipdsId":"IP-124358","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396760,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dspace.lib.hawaii.edu/handle/10790/5553"}],"country":"Marianas Islands, United States","state":"Hawaii","otherGeospatial":"Pacific Remote Island Area,, Samoa","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Suarez, Bobbie","contributorId":287958,"corporation":false,"usgs":false,"family":"Suarez","given":"Bobbie","email":"","affiliations":[{"id":25429,"text":"UH","active":true,"usgs":false}],"preferred":false,"id":837231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837230,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218237,"text":"70218237 - 2021 - Forecasting community reassembly using climate-linked spatio-temporal ecosystem models","interactions":[],"lastModifiedDate":"2021-04-08T14:55:31.493847","indexId":"70218237","displayToPublicDate":"2021-01-27T10:55:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting community reassembly using climate-linked spatio-temporal ecosystem models","docAbstract":"

Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio‐temporal ecosystem model that approximates coupled physical, biological and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate‐linked forecasts), primary producers (spring and fall size‐fractionated chlorophyll‐a), secondary producers (copepods), juveniles (age‐0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort) and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom–up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate ‘ecological teleconnections' (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.

","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05471","usgsCitation":"Thorson, J., Arimitsu, M.L., Barnett, L., Cheng, W., Eisner, L., Haynie, A., Hermann, A., Holsman, K., Kimmel, D., Lomas, M., Richar, J., and Siddon, E., 2021, Forecasting community reassembly using climate-linked spatio-temporal ecosystem models: Ecography, v. 44, no. 4, p. 612-625, https://doi.org/10.1111/ecog.05471.","productDescription":"14 p.","startPage":"612","endPage":"625","ipdsId":"IP-119434","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":453681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05471","text":"Publisher Index Page"},{"id":383367,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorson, James","contributorId":251785,"corporation":false,"usgs":false,"family":"Thorson","given":"James","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":810580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnett, Lewis","contributorId":251786,"corporation":false,"usgs":false,"family":"Barnett","given":"Lewis","affiliations":[{"id":50398,"text":"JISAO","active":true,"usgs":false}],"preferred":false,"id":810581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Wei","contributorId":251787,"corporation":false,"usgs":false,"family":"Cheng","given":"Wei","email":"","affiliations":[{"id":50399,"text":"JISAO, NOAA","active":true,"usgs":false}],"preferred":false,"id":810582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eisner, Lisa","contributorId":251788,"corporation":false,"usgs":false,"family":"Eisner","given":"Lisa","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810583,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haynie, Alan","contributorId":251789,"corporation":false,"usgs":false,"family":"Haynie","given":"Alan","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hermann, Albert","contributorId":251790,"corporation":false,"usgs":false,"family":"Hermann","given":"Albert","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holsman, Kirsten","contributorId":251791,"corporation":false,"usgs":false,"family":"Holsman","given":"Kirsten","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810586,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kimmel, David","contributorId":251792,"corporation":false,"usgs":false,"family":"Kimmel","given":"David","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810587,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lomas, Michael","contributorId":251793,"corporation":false,"usgs":false,"family":"Lomas","given":"Michael","affiliations":[{"id":50400,"text":"Bigelow Lab for Ocean Sciences","active":true,"usgs":false}],"preferred":false,"id":810588,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Richar, Jon","contributorId":251794,"corporation":false,"usgs":false,"family":"Richar","given":"Jon","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810589,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Siddon, Elizabeth","contributorId":251795,"corporation":false,"usgs":false,"family":"Siddon","given":"Elizabeth","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810590,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70228505,"text":"70228505 - 2021 - An inventory and typology of permanent floodplain lakes in the Mississippi alluvial valley: A first step to conservation planning","interactions":[],"lastModifiedDate":"2022-02-11T17:04:24.12061","indexId":"70228505","displayToPublicDate":"2021-01-27T10:51:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"An inventory and typology of permanent floodplain lakes in the Mississippi alluvial valley: A first step to conservation planning","docAbstract":"The alluvial valley of the Mississippi River is an extensive area harboring hundreds of lakes created by fluvial dynamics. These floodplain lakes are scattered throughout the valley and carved over thousands of years by shifting river courses and other hydro-fluvial processes associated with contemporary and prehistoric rivers. These lakes have significant ecological importance as they support a large component of North American biodiversity. We used remote sensing to catalog lakes, to characterize morphology, and to construct a typology via cluster analysis. We identified over 1,300 permanent lakes totaling over 100,000 ha. The lakes were classified into 12 types according to lake size, shape, depth, connectivity, inundation frequency, and surrounding landcover. We anticipate that biotic characteristics differ among the 12 types, but large-scale systematic analyses of biotic assemblages of floodplain lakes in the region are mostly absent. Our typology can provide the framework essential for organizing research to define water dynamics, water quality, and ecological conditions such as forests, mussel, fish, and avian communities to construct conservation plans. The typology encourages a large-scale view of the properties of floodplain lakes in the alluvial valley. It is a functional tool that can be used to begin identifying conservation and research needs, adapt monitoring and management programs, customize environmental programs, and use conservation resources more effectively to achieve large-scale management objectives. ","language":"English","publisher":"Springer","doi":"10.1007/s00027-020-00775-3","usgsCitation":"Miranda, L.E., Rhodes, M., Allen, Y., and Killgore, K., 2021, An inventory and typology of permanent floodplain lakes in the Mississippi alluvial valley: A first step to conservation planning: Aquatic Sciences, v. 83, 20, 11 p., https://doi.org/10.1007/s00027-020-00775-3.","productDescription":"20, 11 p.","ipdsId":"IP-117209","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.56054687499999,\n 37.09023980307208\n ],\n [\n -90.19775390625,\n 36.63316209558658\n ],\n [\n -91.25244140624999,\n 35.02999636902566\n ],\n [\n -91.64794921875,\n 33.99802726234877\n ],\n [\n -92.3291015625,\n 32.69486597787505\n ],\n [\n -92.43896484375,\n 31.316101383495624\n ],\n [\n -92.04345703125,\n 29.82158272057499\n ],\n [\n -90.90087890624999,\n 29.11377539511439\n ],\n [\n -89.7802734375,\n 28.9600886880068\n ],\n [\n -89.296875,\n 29.916852233070173\n ],\n [\n -89.80224609374999,\n 30.44867367928756\n ],\n [\n -91.16455078125,\n 30.44867367928756\n ],\n [\n -90.9228515625,\n 31.784216884487385\n ],\n [\n -90.68115234375,\n 32.97180377635759\n ],\n [\n -90.3515625,\n 34.14363482031264\n ],\n [\n -89.18701171875,\n 36.03133177633187\n ],\n [\n -88.681640625,\n 37.020098201368114\n ],\n [\n -89.56054687499999,\n 37.09023980307208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"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":834459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rhodes, M.C.","contributorId":275997,"corporation":false,"usgs":false,"family":"Rhodes","given":"M.C.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Y.","contributorId":275998,"corporation":false,"usgs":false,"family":"Allen","given":"Y.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Killgore, K.J.","contributorId":200191,"corporation":false,"usgs":false,"family":"Killgore","given":"K.J.","email":"","affiliations":[{"id":33009,"text":"Engineer Research and Development Center, U. S. Army Corps of Engineers, Vicksburg, Mississippi","active":true,"usgs":false}],"preferred":false,"id":834462,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219521,"text":"70219521 - 2021 - Stream restoration is influenced by details of engineered habitats at a headwater mine site","interactions":[],"lastModifiedDate":"2021-04-13T12:10:00.472236","indexId":"70219521","displayToPublicDate":"2021-01-27T08:31:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"Stream restoration is influenced by details of engineered habitats at a headwater mine site","docAbstract":"

A lack of information regarding which ecological factors influence restoration success or failure has hindered scientifically based restoration decision-making. We focus on one headwater site to examine factors influencing divergent ecological outcomes of two post-mining stream restoration projects designed to improve instream conditions following 70 years of mining impacts. One project was designed to simulate natural stream conditions by creating a morphologically complex channel with high habitat heterogeneity (HH-reach). A second project was designed to reduce contaminants and sediment using a sand filter along a straight, armored channel, which resulted in different habitat characteristics and comparatively low habitat heterogeneity (LH-reach). Within 2 years of completion, stream habitat parameters and community composition within the HH-reach were similar to those of reference reaches. In contrast, habitat and community composition within the LH-reach differed substantially from reference reaches, even 7–8 years after project completion. We found that an interaction between low gradient and high light availability, created by the LH-reach design, facilitated a Chironomid-Nostoc mutualism. These symbionts dominated the epilithic surface of rocks and there was little habitat for tailed frog larvae, bioavailable macroinvertebrates, and fish. After controlling for habitat quantity, potential colonizing species’ traits, and biogeographic factors, we found that habitat characteristics combined to facilitate different ecological outcomes, whereas time since treatment implementation was less influential. We demonstrate that stream communities can respond quickly to restoration of physical characteristics and increased heterogeneity, but “details matter” because interactions between the habitats we create and between the species that occupy them can be complex, unpredictable, and can influence restoration effectiveness.

","language":"English","publisher":"MDPI","doi":"10.3390/d13020048","usgsCitation":"Arkle, R.S., and Pilliod, D., 2021, Stream restoration is influenced by details of engineered habitats at a headwater mine site: Diversity, v. 13, no. 2, 48, 23 p., https://doi.org/10.3390/d13020048.","productDescription":"48, 23 p.","ipdsId":"IP-125041","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d13020048","text":"Publisher Index Page"},{"id":385007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Meadow Creek, Stibnite Mine site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.4230499267578,\n 44.837856183947665\n ],\n [\n -115.17997741699219,\n 44.837856183947665\n ],\n [\n -115.17997741699219,\n 44.967955737828085\n ],\n [\n -115.4230499267578,\n 44.967955737828085\n ],\n [\n -115.4230499267578,\n 44.837856183947665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218711,"text":"70218711 - 2021 - Great expectations: Deconstructing the process pathways underlying beaver-related restoration","interactions":[],"lastModifiedDate":"2021-03-08T14:25:04.534627","indexId":"70218711","displayToPublicDate":"2021-01-27T07:52:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Great expectations: Deconstructing the process pathways underlying beaver-related restoration","docAbstract":"

Beaver-related restoration is a process-based strategy that seeks to address wide-ranging ecological objectives by reestablishing dam building in degraded stream systems. Although the beaver-related restoration has broad appeal, especially in water-limited systems, its effectiveness is not yet well documented. In this article, we present a process-expectation framework that links beaver-related restoration tactics to commonly expected outcomes by identifying the set of process pathways that must occur to achieve those expected outcomes. We explore the contingency implicit within this framework using social and biophysical data from project and research sites. This analysis reveals that outcomes are often predicated on complex process pathways over which humans have limited control. Consequently, expectations often shift through the course of projects, suggesting that a more useful paradigm for evaluating process-based restoration would be to identify relevant processes and to rigorously document how projects do or do not proceed along expected process pathways using both quantitative and qualitative data.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biaa165","usgsCitation":"Nash, C., Grant, G., Charnley, S., Dunham, J.B., Gosnell, H., Hausner, M.B., Pilliod, D.S., and Taylor, J.D., 2021, Great expectations: Deconstructing the process pathways underlying beaver-related restoration: BioScience, v. 71, no. 3, p. 249-267, https://doi.org/10.1093/biosci/biaa165.","productDescription":"19 p.","startPage":"249","endPage":"267","ipdsId":"IP-106141","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453685,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biaa165","text":"Publisher Index Page"},{"id":384223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Nash, Caroline","contributorId":204146,"corporation":false,"usgs":false,"family":"Nash","given":"Caroline","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":811497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Gordon E.","contributorId":30881,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon E.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":811498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Charnley, Susan","contributorId":169897,"corporation":false,"usgs":false,"family":"Charnley","given":"Susan","email":"","affiliations":[{"id":25613,"text":"Pacific Northwest Research Station, USDA Forest Service.","active":true,"usgs":false}],"preferred":false,"id":811499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":811500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gosnell, Hannah","contributorId":192214,"corporation":false,"usgs":false,"family":"Gosnell","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":811501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hausner, Mark B.","contributorId":204145,"corporation":false,"usgs":false,"family":"Hausner","given":"Mark","email":"","middleInitial":"B.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":811502,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":811503,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Taylor, Jimmy D.","contributorId":140178,"corporation":false,"usgs":false,"family":"Taylor","given":"Jimmy","email":"","middleInitial":"D.","affiliations":[{"id":13402,"text":"USDA APHIS Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":811504,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70226209,"text":"70226209 - 2021 - Assessment of flood forecast products for a coupled tributary-Coastal model","interactions":[],"lastModifiedDate":"2021-11-17T13:49:27.905474","indexId":"70226209","displayToPublicDate":"2021-01-27T07:46:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of flood forecast products for a coupled tributary-Coastal model","docAbstract":"
Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users. 
","language":"English","publisher":"MDPI","doi":"10.3390/w13030312","usgsCitation":"Cifelli, R., Johnson, L.E., Kim, J., Coleman, T., Pratt, G., Herdman, L.M., Martyr-Koller, R.C., Finzi-Hart, J., Erikson, L.H., Barnard, P.L., and Anderson, M., 2021, Assessment of flood forecast products for a coupled tributary-Coastal model: Water, v. 3, no. 13, 312, 21 p., https://doi.org/10.3390/w13030312.","productDescription":"312, 21 p.","ipdsId":"IP-125274","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13030312","text":"Publisher Index Page"},{"id":391794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Pablo 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The variability of lipid content and fatty acid (FA) composition across blubber depth and body sites are important considerations for condition and diet studies of marine mammals. We investigated lipid and FA variability among inner and outer blubber layers, three body sites, four study years, and lactation status of adult female Pacific walruses (Odobenus rosmarus divergens) using blubber samples collected from subsistence-harvested walruses in spring 2007–2010. Percent lipid content did not differ between the inner and outer blubber layers at the rump, flank, or sternum of walruses. Although FA composition differed between the inner and outer blubber layers, the difference was consistent across body sites, and differences between layers within individual FAs were small (<2%). Lipid content at the sternum of lactating females was 6% higher than non-lactating females, consistent with known variation in body condition among these reproductive classes. Across study years, lipid content varied 18% and individual FAs varied 6%, likely reflecting population-level interannual variability in energy budgets and small differences in diet among years. Consistency in blubber lipid content across blubber depth and body sites and detectable variation in blubber lipid content among reproductive classes and years suggests the potential for lipid content to be a useful indicator of walrus body condition. In addition to information on condition, FA composition of blubber samples could potentially provide insights into changes in walrus diet that may be expected to occur from changes in their access to prey resources resulting from continued sea ice loss.

","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2021.603065","usgsCitation":"Jay, C.V., Iverson, S., and Fischbach, A.S., 2021, Variability of lipids and fatty acids in Pacific walrus blubber: Frontiers in Marine Science, v. 8, 603065, 9 p., https://doi.org/10.3389/fmars.2021.603065.","productDescription":"603065, 9 p.","ipdsId":"IP-119980","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":453691,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.603065","text":"Publisher Index Page"},{"id":436535,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P9GX3E","text":"USGS data release","linkHelpText":"Pacific Walrus Blubber Lipid Content and Fatty Acid Composition, St. Lawrence Island, 2007-2010"},{"id":436534,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P9GX3E","text":"USGS data release","linkHelpText":"Pacific Walrus Blubber Lipid Content and Fatty Acid Composition, St. Lawrence Island, 2007-2010"},{"id":382754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":809262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iverson, Sara J.","contributorId":248497,"corporation":false,"usgs":false,"family":"Iverson","given":"Sara J.","affiliations":[{"id":49932,"text":"Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada","active":true,"usgs":false}],"preferred":false,"id":809263,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":809264,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224248,"text":"70224248 - 2021 - Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data","interactions":[],"lastModifiedDate":"2021-09-15T12:24:24.631311","indexId":"70224248","displayToPublicDate":"2021-01-27T07:19:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data","docAbstract":"

Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning architecture, we developed a basin-centric lumped daily mean Ts model, which was trained over 118 data-rich basins with no major dams in the conterminous United States, and showed strong results. At a national scale, we obtained a median root-mean-square error of 0.69°C, Nash–Sutcliffe model efficiency coefficient of 0.985, and correlation of 0.994, which are marked improvements over previous values reported in literature. The addition of streamflow observations as a model input strongly elevated the performance of this model. In the absence of measured streamflow, we showed that a two-stage model could be used, where simulated streamflow from a pre-trained LSTM model (Qsim) still benefited the Ts model even though no new information was brought directly into the inputs of the Ts model. The model indirectly used information learned from streamflow observations provided during the training of Qsim, potentially to improve internal representation of physically meaningful variables. Our results indicate that strong relationships exist between basin-averaged forcing variables, catchment attributes, and Ts that can be simulated by a single model trained by data on the continental scale.

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China","active":true,"usgs":false}],"preferred":false,"id":823347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":823348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":823349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":823350,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217701,"text":"70217701 - 2021 - Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.","interactions":[],"lastModifiedDate":"2023-04-10T22:11:19.232976","indexId":"70217701","displayToPublicDate":"2021-01-27T07:16:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3627,"text":"Transactions of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.","docAbstract":"

Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning.

","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/trans.13938","usgsCitation":"Moeser, C.D., and Douglas-Mankin, K.R., 2021, Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.: Transactions of the American Society of Agricultural and Biological Engineers, v. 64, no. 1, p. 137-150, https://doi.org/10.13031/trans.13938.","productDescription":"14 p.","startPage":"137","endPage":"150","ipdsId":"IP-101142","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":382749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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