The shoaling transformation from generally linear deep-water waves to asymmetric shallow-water waves modifies wave shapes and causes near-bed orbital velocities to become asymmetrical, contributing to net sediment transport. In this work, we used two methods to estimate the asymmetric wave shape from data at three sites. The first method converted wave measurements made at the surface to idealized near-bottom wave-orbital velocities using a set of empirical equations: the “parameterized” waveforms. The second method involved direct measurements of velocities and pressure made near the seabed: the “direct” waveforms. Estimates from the two methods were well correlated at all three sites (Pearson’s correlation coefficient greater than 0.85). Both methods were used to drive bedload-transport calculations that accounted for asymmetric waves, and the results were compared with a traditional excess-stress formulation and field estimates of bedload transport derived from ripple migration rates based on sonar imagery. The cumulative bedload transport from the parameterized waveform was 25% greater than the direct waveform, mainly because the parameterized waveform did not account for negative skewness. Calculated transport rates were comparable to rates estimated from ripple migration except during the largest event, when calculated rates were as much as 100 times greater, which occurred during high period waves.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse10020223","usgsCitation":"Kalra, T., Suttles, S.E., Sherwood, C.R., Warner, J.C., Aretxabaleta, A., and Leavitt, G.R., 2022, Shoaling wave shape estimates from field observations and derived bedload sediment rates: Journal of Marine Science and Engineering, v. 10, no. 2, 223, 27 p., https://doi.org/10.3390/jmse10020223.","productDescription":"223, 27 p.","ipdsId":"IP-130494","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020223","text":"Publisher Index Page"},{"id":395674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Massachusetts, New York","otherGeospatial":"Fire Island, Martha Vineyard, Matanzas Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.30260467529297,\n 29.898252057056208\n ],\n [\n -81.2739372253418,\n 29.898252057056208\n ],\n [\n -81.2739372253418,\n 29.916405869526507\n ],\n [\n -81.30260467529297,\n 29.916405869526507\n ],\n [\n -81.30260467529297,\n 29.898252057056208\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.57891845703125,\n 41.31907562295139\n ],\n [\n -70.52604675292969,\n 41.31907562295139\n ],\n [\n -70.52604675292969,\n 41.38041517477678\n ],\n [\n -70.57891845703125,\n 41.38041517477678\n ],\n [\n -70.57891845703125,\n 41.31907562295139\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.19744110107422,\n 40.61890405098613\n ],\n [\n -73.1744384765625,\n 40.61890405098613\n ],\n [\n -73.1744384765625,\n 40.63206312461566\n ],\n [\n -73.19744110107422,\n 40.63206312461566\n ],\n [\n -73.19744110107422,\n 40.61890405098613\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":834048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suttles, Steven E. 0000-0002-4119-8370 ssuttles@usgs.gov","orcid":"https://orcid.org/0000-0002-4119-8370","contributorId":192272,"corporation":false,"usgs":true,"family":"Suttles","given":"Steven","email":"ssuttles@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leavitt, Gibson Robert Scott 0000-0001-5362-9150","orcid":"https://orcid.org/0000-0001-5362-9150","contributorId":275364,"corporation":false,"usgs":true,"family":"Leavitt","given":"Gibson","email":"","middleInitial":"Robert Scott","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232989,"text":"70232989 - 2022 - Empirical map-based nonergodic models of site response in the greater Los Angeles area","interactions":[],"lastModifiedDate":"2022-07-15T13:43:13.704946","indexId":"70232989","displayToPublicDate":"2022-02-08T08:31:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Empirical map-based nonergodic models of site response in the greater Los Angeles area","docAbstract":"We develop empirical estimates of site response at seismic stations in the Los Angeles area using recorded ground motions from 414 M 3–7.3 earthquakes in southern California. The data are from a combination of the Next Generation Attenuation‐West2 project, the 2019 Ridgecrest earthquakes, and about 10,000 newly processed records. We estimate site response using an iterative mixed‐effects residuals partitioning approach, accounting for azimuthal variations in anelastic attenuation and potential bias due to spatial clusters of colocated earthquakes. This process yields site response for peak ground acceleration, peak ground velocity, and pseudospectral acceleration relative to a 760 m/s shear‐wave velocity (
The 2018 Kīlauea caldera collapse produced extraordinary sequences of seismicity and deformation, with 62 episodic collapse events which significantly altered the landscape of the summit region. Despite decades of focused scientific studies at Kīlauea, detailed information about the internal structure of the volcano is limited. Recently developed techniques in seismic interferometry can be used to monitor the internal structure of an active volcano more directly by detecting subtle spatiotemporal changes in seismic wave velocity, but their utility relies on accurate interpretations of the underlying phenomena causing those velocity changes. Here, we retrospectively apply repeating-earthquake-based seismic interferometry to the 2018 Kīlauea eruption sequence. We find that seismic velocities changed over two distinct time scales: a sudden increase followed by a slower decrease in velocity in the hours following each collapse event, and a gradual, long-term decrease in velocity over several weeks that ceased approximately 1 month prior to the end of the eruption. Modeling suggests that short-term changes can be explained by magma reservoir pressurization which specifically closed vertical ring fractures. Long-term changes are related to subsidence of the caldera and likely include the influence of inelastic strain from the formation of new fractures. These observations provide new insights into the evolution of Kīlauea during its progressive collapse and will inform future interpretations for near-real-time monitoring at hazardous volcanoes around the world using similar techniques, especially where a dominant fracture orientation is present.
As coastal communities grow more vulnerable to sea-level rise and increased storminess, communities have turned to nature-based solutions to bolster coastal resilience and protection. Marshes have significant wave attenuation properties and can play an important role in coastal protection for many communities. Many restoration projects seek to maximize this ecosystem service but how much marsh restoration is enough to deliver measurable coastal protection benefits is still unknown. This question is critical to guiding assessments of cost effectiveness and for funding, implementation, and optimizing of marsh restoration for risk reduction projects. This study uses SWAN model simulations to determine empirical relationships between wave attenuation and marsh vegetation. The model runs consider several different common marsh morphologies (including systems with channels, ponds, and fringing mudflats), vegetation placement, and simulated storm intensity. Up to a 95% reduction in wave energy is seen at as low as 50% vegetation cover. Although these empirical relationships between vegetative cover and wave attenuation provide essential insight for marsh restoration, it is also important to factor in lifespan estimates of restored marshes when making overall restoration decisions. The results of this study are important for coastal practitioners and managers seeking performance goals and metrics for marsh restoration, enhancement, and creation.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.756670","usgsCitation":"Castagno, K.A., Ganju, N., Beck, M.W., Bowden, A., and Scyphers, S.B., 2022, How much marsh restoration is enough to deliver wave attenuation coastal protection benefits?: Frontiers in Marine Science, v. 8, 756670, 10 p., https://doi.org/10.3389/fmars.2021.756670.","productDescription":"756670, 10 p.","ipdsId":"IP-132439","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448878,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.756670","text":"Publisher Index Page"},{"id":395623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2022-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Castagno, Katherine A. 0000-0003-4060-926X","orcid":"https://orcid.org/0000-0003-4060-926X","contributorId":267188,"corporation":false,"usgs":false,"family":"Castagno","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":55434,"text":"Center for Coastal Studies, Provincetown, MA, USA","active":true,"usgs":false}],"preferred":false,"id":833497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":833499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowden, Alison","contributorId":274903,"corporation":false,"usgs":false,"family":"Bowden","given":"Alison","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":833500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scyphers, Steven B.","contributorId":274810,"corporation":false,"usgs":false,"family":"Scyphers","given":"Steven","middleInitial":"B.","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833501,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228204,"text":"70228204 - 2022 - Behavioral state-dependent habitat selection and implications for animal translocations","interactions":[],"lastModifiedDate":"2022-02-07T15:22:24.279529","indexId":"70228204","displayToPublicDate":"2022-02-07T09:10:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral state-dependent habitat selection and implications for animal translocations","docAbstract":"Riparian ecosystems in the western USA have been invaded by non-native woody species deliberately introduced for stream bank stabilization, agricultural windbreaks, and urban shade. Recent work suggests that the non-native tree Ulmus pumila (Siberian elm) is capable of significant spread in western riparian ecosystems, that range infilling is still incomplete, and that the invasion is dispersal-limited. Our objective was to understand the interacting roles of propagule pressure from upland U. pumila, human influences, and river geomorphology in promoting riparian U. pumila invasion along the South Platte River, Colorado, USA. We used linear regression and information-theoretic model selection to evaluate the relative importance of these factors to riparian U. pumila stem density. U. pumila stem density increased with increasing channel and floodplain restriction and increasing human influence from both urban and rural development. Model selection indicated that local upland U. pumila seed sources were relatively unimportant to riparian U. pumila stem density, suggesting that upland propagule pressure is currently contributing less than other human influences to U. pumila spread along the South Platte River. In particular, higher road density was the most important predictor for the proportional abundance of smaller U. pumila individuals (DBH<5-cm and 5-15-cm), suggesting that human influence in densely populated areas has been the primary driver of recent U. pumila population expansion. U. pumila stem density was only weakly associated with abundance of other common riparian tree species. Land managers and other entities concerned with non-native tree invasion into important riparian habitat may be able to reduce U. pumila spread most effectively by focusing U. pumila control efforts where human influences are greatest.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01516-4","usgsCitation":"Reynolds, L., Perry, L., Shafroth, P., Katz, G.L., and Norton, A.S., 2022, Invasion of Siberian elm (Ulmus pumila) along the South Platte River: The roles of seed source, human influence, and river geomorphology: Wetlands, v. 42, p. 1-23, https://doi.org/10.1007/s13157-021-01516-4.","productDescription":"10, 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-129736","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435979,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S5M1B4","text":"USGS data release","linkHelpText":"Riparian woody stem densities and landscape variables along the South Platte River, Colorado, United States, 2011-2016"},{"id":395527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.083984375,\n 38.736946065676\n ],\n [\n -102.0355224609375,\n 38.736946065676\n ],\n [\n -102.0355224609375,\n 41.000629848685385\n ],\n [\n -106.083984375,\n 41.000629848685385\n ],\n [\n -106.083984375,\n 38.736946065676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Reynolds, Lindsay 0000-0001-9973-9312 reynoldsl@usgs.gov","orcid":"https://orcid.org/0000-0001-9973-9312","contributorId":150076,"corporation":false,"usgs":true,"family":"Reynolds","given":"Lindsay","email":"reynoldsl@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":833426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Laura perryl@usgs.gov","contributorId":4345,"corporation":false,"usgs":true,"family":"Perry","given":"Laura","email":"perryl@usgs.gov","affiliations":[],"preferred":true,"id":833427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":833428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katz, Gabrielle L.","contributorId":194352,"corporation":false,"usgs":false,"family":"Katz","given":"Gabrielle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":833429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norton, Andrew S.","contributorId":171631,"corporation":false,"usgs":false,"family":"Norton","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":833430,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268895,"text":"70268895 - 2022 - Daily foraging activity of an imperiled ground squirrel: Effects of hibernation, thermal environment, body condition, and conspecific density","interactions":[],"lastModifiedDate":"2025-07-10T13:48:54.434432","indexId":"70268895","displayToPublicDate":"2022-02-07T08:45:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":982,"text":"Behavioral Ecology and Sociobiology","active":true,"publicationSubtype":{"id":10}},"title":"Daily foraging activity of an imperiled ground squirrel: Effects of hibernation, thermal environment, body condition, and conspecific density","docAbstract":"Food acquisition is among the most important tasks faced by free-ranging animals. Predation and thermal risks, however, can make foraging a costly endeavor and foraging can preclude other important activities. Moreover, seasonal life cycle events such as hibernation impose energetic thresholds and time constraints on foraging. These factors interact with an animal’s endogenous state to influence foraging behavior. We tested a suite of predictions based on foraging theory to explore the effects of thermal environment, body condition, and conspecific density on aboveground activity (which is primarily foraging activity) of the northern Idaho ground squirrel (Urocitellus brunneus), an imperiled rodent that hibernates for 9 months each year. We took advantage of the squirrels’ semi-fossorial lifestyle to document daily aboveground activity by attaching geolocators to squirrels. We modeled squirrel activity with generalized linear mixed-effects models to document the relative importance of thermal environment, body condition, and conspecific density for daily aboveground activity. Aboveground activity by northern Idaho ground squirrels increased throughout their active season and leaner squirrels increased their activity more than heavier squirrels as residual foraging opportunities diminished. Thermal conditions also influenced squirrel activity: squirrels spent less time above ground during extreme temperatures and on days with significant precipitation. Aboveground activity of northern Idaho ground squirrels largely adhered to predictions of risk-sensitive and state-dependent foraging theory. Management actions that enhance forage will likely improve the probability of recovery for this federally threatened species by minimizing trade-offs squirrels need to make to acquire sufficient food to survive hibernation and reproduce in subsequent years.
","language":"English","publisher":"Springer","doi":"10.1007/s00265-022-03142-4","usgsCitation":"Allison, A., and Conway, C.J., 2022, Daily foraging activity of an imperiled ground squirrel: Effects of hibernation, thermal environment, body condition, and conspecific density: Behavioral Ecology and Sociobiology, v. 76, 28, https://doi.org/10.1007/s00265-022-03142-4.","productDescription":"28","ipdsId":"IP-126309","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","noUsgsAuthors":false,"publicationDate":"2022-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Allison, Austin A Z.","contributorId":337876,"corporation":false,"usgs":false,"family":"Allison","given":"Austin A Z.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":942536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":942535,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255188,"text":"70255188 - 2022 - Estimating wolf abundance from cameras","interactions":[],"lastModifiedDate":"2024-06-14T16:17:05.49915","indexId":"70255188","displayToPublicDate":"2022-02-06T11:11:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating wolf abundance from cameras","docAbstract":"Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of low-density carnivores with cameras. We used motion-triggered trail cameras and a space-to-event model to estimate gray wolf (Canis lupus) abundance across three study areas in Idaho, USA, 2016–2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km2).
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3933","usgsCitation":"Ausband, D.E., Lukacs, P.M., Hurley, M., Roberts, S., Strickfaden, K.M., and Moeller, A.K., 2022, Estimating wolf abundance from cameras: Ecosphere, v. 13, no. 3, e3933, 8 p., https://doi.org/10.1002/ecs2.3933.","productDescription":"e3933, 8 p.","ipdsId":"IP-127324","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490031,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3933","text":"Publisher Index Page"},{"id":430214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.27446366280928,\n 44.31760887473328\n ],\n [\n -116.04474769799485,\n 43.63573860529246\n ],\n [\n -114.91434636204788,\n 44.35330616742618\n ],\n [\n -113.82239386322908,\n 44.47620619443575\n ],\n [\n -113.55750390521284,\n 45.06145713298196\n ],\n [\n -113.94277353962592,\n 45.707133973439966\n ],\n [\n -114.39076245396207,\n 45.48040816198744\n ],\n [\n -114.60043079769942,\n 45.629047190084634\n ],\n [\n -114.33198947843407,\n 46.693362013709105\n ],\n [\n -114.57075471714238,\n 46.69432428995722\n ],\n [\n -115.72924921268883,\n 47.494572748462474\n ],\n [\n -115.72433918041612,\n 47.63759616318168\n ],\n [\n -116.0416806012203,\n 47.99276499625725\n ],\n [\n -116.03354364171281,\n 48.99569218537363\n ],\n [\n -117.02625270166422,\n 48.98501346607878\n ],\n [\n -117.04252662067968,\n 46.428652837122826\n ],\n [\n -116.87164826052927,\n 45.87625130087565\n ],\n [\n -116.46480028513943,\n 45.61504858360104\n ],\n [\n -117.27446366280928,\n 44.31760887473328\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukacs, Paul M.","contributorId":207269,"corporation":false,"usgs":false,"family":"Lukacs","given":"Paul","email":"","middleInitial":"M.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":903694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurley, Mark A.","contributorId":287804,"corporation":false,"usgs":false,"family":"Hurley","given":"Mark A.","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":903695,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, Shane","contributorId":279606,"corporation":false,"usgs":false,"family":"Roberts","given":"Shane","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":903696,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strickfaden, Kaitlyn M.","contributorId":339386,"corporation":false,"usgs":false,"family":"Strickfaden","given":"Kaitlyn","email":"","middleInitial":"M.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":903697,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moeller, Anna K.","contributorId":338940,"corporation":false,"usgs":false,"family":"Moeller","given":"Anna","email":"","middleInitial":"K.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":903698,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228202,"text":"70228202 - 2022 - Assessment of cereal grain waste densities to aid waterfowl conservation planning in the Klamath Basin","interactions":[],"lastModifiedDate":"2022-07-07T16:36:55.148622","indexId":"70228202","displayToPublicDate":"2022-02-06T09:49:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of cereal grain waste densities to aid waterfowl conservation planning in the Klamath Basin","docAbstract":"Postharvest waste seed from cereal grains is a major dietary component of waterfowl in the Klamath Basin in northeastern California and southeastern Oregon, a region that plays host to over a million waterfowl annually. Understanding food abundance is critical to local waterfowl management; therefore, we conducted a study in 2008 to investigate waste grain densities in barley, oat, and wheat fields. We used hierarchal mixed effect models to assess several factors that may affect waste grain densities postharvest. We also compared the effects of residue management practices to measure the effect of these treatments. To understand the scope of postharvest practices, we conducted a weekly road survey to document treatments applied to fields in our study area. We found that region best explained the variance of postharvest waste grain in barley fields, where the Tule Lake region had 89% greater densities than Lower Klamath. Neither harvester age nor baling affected waste grain in oats fields. In wheat fields, the model containing region and lodging ranked highest, where the Tule Lake region had 66% greater waste densities than Lower Klamath, and lodging increased waste grain by 70%. Burning did not reduce waste grain in barley or oat fields. Chisel-disking reduced waste grain by 94% in wheat fields compared with postharvest. Our field treatment survey found that 70% of barley fields were untreated while 18% were disked and 13% were burned and flooded. We estimated that 82% of oat fields were burned postharvest, while 18% were burned and flooded. In wheat, 61% of fields were left untreated, while 16% were disked, 8% were chisel-plowed, and 7% were flooded postharvest. Flooding and burning occurred primarily on National Wildlife Refuges, while disking, chisel-plowing, and postharvest irrigation occurred solely on private properties. Our results indicate that reducing tillage treatments would boost accessibility of cereal grain food resources to waterfowl in the Klamath Basin, and incentives to flood grain fields on private properties should be considered for the same purpose when and where possible.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-091","usgsCitation":"Skalos, D., Fleskes, J., Kohl, J.D., Herzog, M.P., and Casazza, M.L., 2022, Assessment of cereal grain waste densities to aid waterfowl conservation planning in the Klamath Basin: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 3-16, https://doi.org/10.3996/JFWM-20-091.","productDescription":"14 p.","startPage":"3","endPage":"16","ipdsId":"IP-125071","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448886,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-091","text":"Publisher Index Page"},{"id":395533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.222412109375,\n 42.00032514831621\n ],\n [\n -123.167724609375,\n 39.825413103424786\n ],\n [\n -119.981689453125,\n 42.00848901572399\n ],\n [\n -120.95947265624999,\n 43.874138181474734\n ],\n [\n -124.222412109375,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Skalos, Daniel A.","contributorId":250668,"corporation":false,"usgs":false,"family":"Skalos","given":"Daniel A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":833396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":210345,"corporation":false,"usgs":false,"family":"Fleskes","given":"Joseph P.","affiliations":[],"preferred":false,"id":833397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kohl, Jeffery D.","contributorId":274848,"corporation":false,"usgs":false,"family":"Kohl","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":56673,"text":"California Department of Fish and Wildlife, 1010 Riverside Parkway, West Sacramento, CA 95605, USA","active":true,"usgs":false}],"preferred":false,"id":833398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833399,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833400,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248970,"text":"70248970 - 2022 - Multidisciplinary constraints on the thermal-chemical boundary between Earth's core and mantle","interactions":[],"lastModifiedDate":"2023-09-27T16:02:02.454287","indexId":"70248970","displayToPublicDate":"2022-02-04T10:57:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Multidisciplinary constraints on the thermal-chemical boundary between Earth's core and mantle","docAbstract":"Heat flux from the core to the mantle provides driving energy for mantle convection thus powering plate tectonics, and contributes a significant fraction of the geothermal heat budget. Indirect estimates of core-mantle boundary heat flow are typically based on petrological evidence of mantle temperature, interpretations of temperatures indicated by seismic travel times, experimental measurements of mineral melting points, physical mantle convection models, or physical core convection models. However, previous estimates have not consistently integrated these lines of evidence. In this work, an interdisciplinary analysis is applied to co-constrain core-mantle boundary heat flow and test the thermal boundary layer (TBL) theory. The concurrence of TBL models, energy balance to support geomagnetism, seismology, and review of petrologic evidence for historic mantle temperatures supports QCMB ∼15 TW, with all except geomagnetism supporting as high as ∼20 TW. These values provide a tighter constraint on core heat flux relative to previous work. Our work describes the seismic properties consistent with a TBL, and supports a long-lived basal mantle molten layer through much of Earth's history.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC009764","usgsCitation":"Frost, D.A., Avery, M.S., Buffett, B., Chidester, B.A., Deng, J., Dorfman, S.M., Li, Z., Liu, L., Lv, M., and Martin, J.F., 2022, Multidisciplinary constraints on the thermal-chemical boundary between Earth's core and mantle: Geochemistry, Geophysics, Geosystems, v. 23, no. 3, e2021GC009764, 34 p., https://doi.org/10.1029/2021GC009764.","productDescription":"e2021GC009764, 34 p.","ipdsId":"IP-128209","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":448892,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021gc009764","text":"External Repository"},{"id":421252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Frost, Daniel A. 0000-0001-7882-5166","orcid":"https://orcid.org/0000-0001-7882-5166","contributorId":330231,"corporation":false,"usgs":false,"family":"Frost","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":884383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avery, Margaret Susan 0000-0002-8504-7072","orcid":"https://orcid.org/0000-0002-8504-7072","contributorId":329991,"corporation":false,"usgs":true,"family":"Avery","given":"Margaret","email":"","middleInitial":"Susan","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buffett, Bruce 0000-0001-5488-7602","orcid":"https://orcid.org/0000-0001-5488-7602","contributorId":330183,"corporation":false,"usgs":false,"family":"Buffett","given":"Bruce","email":"","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":884385,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chidester, Bethany A. 0000-0002-4103-7606","orcid":"https://orcid.org/0000-0002-4103-7606","contributorId":330232,"corporation":false,"usgs":false,"family":"Chidester","given":"Bethany","email":"","middleInitial":"A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":884386,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deng, Jie 0000-0001-5441-2797","orcid":"https://orcid.org/0000-0001-5441-2797","contributorId":330233,"corporation":false,"usgs":false,"family":"Deng","given":"Jie","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":884387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dorfman, Susannah M. 0000-0002-3968-9592","orcid":"https://orcid.org/0000-0002-3968-9592","contributorId":330234,"corporation":false,"usgs":false,"family":"Dorfman","given":"Susannah","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":884388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Zhi","contributorId":330240,"corporation":false,"usgs":false,"family":"Li","given":"Zhi","email":"","affiliations":[],"preferred":false,"id":884389,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Liu, Lijun 0000-0002-3232-0151","orcid":"https://orcid.org/0000-0002-3232-0151","contributorId":330235,"corporation":false,"usgs":false,"family":"Liu","given":"Lijun","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":884390,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lv, Mingda","contributorId":330236,"corporation":false,"usgs":false,"family":"Lv","given":"Mingda","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":884391,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Martin, Joshua F.","contributorId":330237,"corporation":false,"usgs":false,"family":"Martin","given":"Joshua","email":"","middleInitial":"F.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":884392,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228091,"text":"ofr20211030F - 2022 - System characterization report on Planet’s SuperDove","interactions":[{"subject":{"id":70228091,"text":"ofr20211030F - 2022 - System characterization report on Planet’s SuperDove","indexId":"ofr20211030F","publicationYear":"2022","noYear":false,"chapter":"F","displayTitle":"System Characterization Report on Planet’s SuperDove","title":"System characterization report on Planet’s SuperDove"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2022-02-04T16:16:18.013029","indexId":"ofr20211030F","displayToPublicDate":"2022-02-03T15:53:24","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"F","displayTitle":"System Characterization Report on Planet’s SuperDove","title":"System characterization report on Planet’s SuperDove","docAbstract":"This report addresses system characterization of Planet’s SuperDove and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5.8 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 3-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SuperDove has a band-to-band geometric performance in the range of −1.701 m (−0.567 pixel) to 1.173 m (0.391 pixel) in easting and −4.950 m (−1.650 pixels) to 6.051 m (2.017 pixels) in northing, an image-to-image geometric performance of −1.17 m (−0.39 pixel) to 23.45 m (7.82 pixels) in easting and −10.61 m (−3.54 pixels) to −4.43 m (−1.48 pixels) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.043 to 0.020 in offset and 0.812 to 1.246 in slope, and a spatial performance in the range of 3.59 to 3.70 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.005 to 0.008.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030F","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on Planet’s SuperDove, chap. F of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 19 p., https://doi.org/10.3133/ofr20211030F.","productDescription":"iv, 19 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-126679","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":395388,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/f/Images"},{"id":395385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/f/coverthb.jpg"},{"id":395387,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/f/ofr20211030f.XML","size":"67.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1030–F XML"},{"id":395386,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/f/ofr20211030f.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–F"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Since they were first introduced to the United States more than 50 years ago, invasive carp have rapidly colonized rivers of the Mississippi River Basin, with detrimental effects on native aquatic species. Their continued range expansion, and potential for subsequent invasion of the Great Lakes, has led to increased concern for the susceptibility of as-yet uncompromised lotic and lentic systems in the central United States. Because invasive carp eggs and larvae must drift in the river current for the first several days following spawning, numerical drift modeling has emerged as a useful technique for determining whether certain river systems and reaches have the potential to support suspension-to-hatching survival of invasive carp eggs, a critical first step in recruitment. Here we use one such numerical modeling approach, the Fluvial Egg Drift Simulator (FluEgg), to estimate bighead carp (Hypophthalmichthys nobilis) egg hatching success and larval retention in a 47.8-kilometer (km) reach of the multi-thread St. Croix River, Minnesota and Wisconsin, United States. We explore three approaches for obtaining the hydraulic data required by FluEgg, parameterizing the model with either (a) field hydraulic data collected within the main channel during a high-flow event, or hydraulic data output from a one-dimensional hydrodynamic model with both (b) steady, and (c) unsteady flows. We find that the three approaches, along with the range of water temperatures and discharge used in simulations, produce vastly different predictions of streamwise transport and in-river egg hatching probability (0% for field data, 0 to 96% for steady-state hydraulic modeling, and 1.8 to 65% for unsteady modeling). However, all FluEgg simulations, regardless of the source of hydraulic data, predicted that no larvae reach the gas bladder inflation stage within the study reach where nursery habitat is abundant. Overall, these results indicate that the lower St. Croix River is suitable for invasive carp spawning and egg suspension until hatching for a range of discharge and water temperatures. These results highlight the role of complex channel hydraulics and morphology, particularly multi-thread reaches, and their inclusion in ecohydraulic-suitability modeling to determine susceptibility of river systems for invasive carp reproduction. Our work also emphasizes the scientific value of multi-dimensional hydrodynamic models that can capture the spatial heterogeneity of flow fields in geomorphically complex rivers. This work may help to guide management efforts based on the targeted monitoring and control and improve invasive carp egg and larvae sampling efficiency.
","language":"English","publisher":"Public Library of Science (PLOS)","doi":"10.1371/journal.pone.0263052","usgsCitation":"Kasprak, A., Jackson, P.R., Lindroth, E.M., Lund, J.W., and Ziegeweid, J.R., 2022, The role of hydraulic and geomorphic complexity in predicting invasive carp spawning potential: St. Croix River, Minnesota and Wisconsin, United States: PLoS ONE, v. 17, no. 2, e0263052, 25 p., https://doi.org/10.1371/journal.pone.0263052.","productDescription":"e0263052, 25 p.","ipdsId":"IP-128244","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448898,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0263052","text":"Publisher Index Page"},{"id":435980,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93K0UUI","text":"USGS data release","linkHelpText":"Bathymetric, water velocity, and water temperature data on the St. Croix River between St. Croix Falls, Wisconsin, and Stillwater, Minnesota, June 19-22, 2018"},{"id":395554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.84408569335938,\n 44.98714175309689\n ],\n [\n -92.59552001953125,\n 44.98714175309689\n ],\n [\n -92.59552001953125,\n 45.42062422307843\n ],\n [\n -92.84408569335938,\n 45.42062422307843\n ],\n [\n -92.84408569335938,\n 44.98714175309689\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-03","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":833274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindroth, Evan M. 0000-0002-9746-4359 elindroth@usgs.gov","orcid":"https://orcid.org/0000-0002-9746-4359","contributorId":264885,"corporation":false,"usgs":true,"family":"Lindroth","given":"Evan","email":"elindroth@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lund, J. William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":211157,"corporation":false,"usgs":true,"family":"Lund","given":"J.","email":"","middleInitial":"William","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833278,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230108,"text":"70230108 - 2022 - The occurrence of large floods in the United States in the modern hydroclimate regime: Seasonality, trends, and large-scale climate associations","interactions":[],"lastModifiedDate":"2022-03-30T16:33:28.393017","indexId":"70230108","displayToPublicDate":"2022-02-03T11:32:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The occurrence of large floods in the United States in the modern hydroclimate regime: Seasonality, trends, and large-scale climate associations","docAbstract":"Many studies investigate river floods by analyzing annual maximum series that record the largest flow of each year, including many within-bank events inconsequential for human communities. Fewer focus on larger floods, especially at the continental scale. Using 473 streamgages across the conterminous United States with near-natural flow from 1966 to 2015, we characterized the seasonality, occurrence, and climatic associations of the 10 largest and 2 largest floods at each site. These are often overbank events that have ecosystem functions and pose risks to humans. We grouped sites into 14 clusters corresponding to climatic and physiographic regions and characterized their flood seasonality at a monthly resolution using a probabilistic method. We then evaluated annual occurrence regionally and nationally, and seasonal occurrence regionally, by complementing a traditional approach to trend analyses with a novel method based on expected occurrence. Relationships between flood occurrence and climate indices were also investigated. Large floods have strong seasonality in some regions, but in areas with numerous flood-generating mechanisms, seasonality is more complex. There is little evidence nationally that large riverine floods are more or less frequent than expected in recent years and only two regions show significant trends in annual counts; few show seasonal trends. We found some regional relationships between flood counts and climate indices, annually and seasonally; nationally the Pacific North American pattern is related to annual counts of the 2 largest floods. Large-flood occurrence was generally stable across the United States in the last five decades; this may or may not continue with projected warming.
","language":"English","publisher":"Wiley","doi":"10.1029/2021WR030480","usgsCitation":"Collins, M., Hodgkins, G.A., Archfield, S.A., and Hirsch, R.M., 2022, The occurrence of large floods in the United States in the modern hydroclimate regime: Seasonality, trends, and large-scale climate associations: Water Resources Research, v. 58, e2021WR030480, 22 p., https://doi.org/10.1029/2021WR030480.","productDescription":"e2021WR030480, 22 p.","ipdsId":"IP-128386","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":435981,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QYR28M","text":"USGS data release","linkHelpText":"Ten Largest Annual Instantaneous Floods and Seasonal Signal for Reference Streamgages in the United States, Water Years 1966-2015"},{"id":397871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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MA","active":true,"usgs":false}],"preferred":false,"id":839063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":839065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":839066,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230954,"text":"70230954 - 2022 - Silicate volcanism on Europa’s seafloor and implications for habitability","interactions":[],"lastModifiedDate":"2022-04-29T12:10:55.639334","indexId":"70230954","displayToPublicDate":"2022-02-03T07:09:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Silicate volcanism on Europa’s seafloor and implications for habitability","docAbstract":"Habitable ocean environments on Europa require an influx of reactants to maintain chemical disequilibrium. One possible source of reactants is seafloor volcanism. Modeling has shown that dissipation of tidal energy in Europa's asthenosphere can generate melt, but melt formation cannot be equated with volcanism. Melt must also be transported through Europa's cold lithosphere to erupt at the seafloor. Here, we use two models of dike propagation to show that dikes can only traverse the lithosphere if either the fracture toughness of the lithosphere or the flux into the dike is large (>500 MPa m1/2 or ∼1 m2 s−1, respectively). We conclude that cyclic volcanic episodes might provide reactants to Europa's ocean if magma accumulates at the base of the lithosphere for several thousand years. However, if dikes form too frequently, or are too numerous, the magma flux into each will be insufficient, and volcanism cannot support a habitable ocean environment.
Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EA002085","usgsCitation":"Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C.S., Brown, J., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C., Warrick, J.A., and Wernette, P., 2022, Human-in-the-Loop segmentation of earth surface imagery: Earth and Space Science, v. 9, e2021EA002085, 31 p., https://doi.org/10.1029/2021EA002085.","productDescription":"e2021EA002085, 31 p.","ipdsId":"IP-132726","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":680,"text":"Woods Hole Science Center","active":false,"usgs":true}],"links":[{"id":448911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021ea002085","text":"Publisher Index Page"},{"id":397005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":240661,"corporation":false,"usgs":true,"family":"Buscombe","given":"Daniel D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldstein, Evan B. 0000-0001-9358-1016","orcid":"https://orcid.org/0000-0001-9358-1016","contributorId":184210,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":837750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bodine, Cameron S 0000-0002-1623-3920","orcid":"https://orcid.org/0000-0002-1623-3920","contributorId":288327,"corporation":false,"usgs":false,"family":"Bodine","given":"Cameron","email":"","middleInitial":"S","affiliations":[{"id":61729,"text":"School of Informatics, Computing and Cybersystems, Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":837752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Favela, Jaycee 0000-0001-9175-8324","orcid":"https://orcid.org/0000-0001-9175-8324","contributorId":288328,"corporation":false,"usgs":false,"family":"Favela","given":"Jaycee","email":"","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":837754,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fitzpatrick, Sharon 0000-0001-6513-9132","orcid":"https://orcid.org/0000-0001-6513-9132","contributorId":288329,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Sharon","email":"","affiliations":[{"id":39151,"text":"California State University Sacramento","active":true,"usgs":false}],"preferred":false,"id":837755,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837756,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Over, Jin-Si R. 0000-0001-6753-7185 jover@usgs.gov","orcid":"https://orcid.org/0000-0001-6753-7185","contributorId":260178,"corporation":false,"usgs":true,"family":"Over","given":"Jin-Si","email":"jover@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837757,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ritchie, Andrew C. aritchie@usgs.gov","contributorId":4984,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837758,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837759,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837760,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70227958,"text":"70227958 - 2022 - Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland","interactions":[],"lastModifiedDate":"2022-02-02T15:10:24.469421","indexId":"70227958","displayToPublicDate":"2022-02-02T09:04:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland","docAbstract":"Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3590","usgsCitation":"Pau, S., Nippert, J., Slapikas, R., Griffith, D.M., Bachle, S., Helliker, B., O’Connor, R., Riley, W.J., Still, C.J., and Zaricor, M., 2022, Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland: Ecology, v. 103, no. 2, e03590, https://doi.org/10.1002/ecy.3590.","productDescription":"e03590","ipdsId":"IP-123791","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":395269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"editors":[{"text":"Borer, Elizabeth T.","contributorId":45049,"corporation":false,"usgs":false,"family":"Borer","given":"Elizabeth","email":"","middleInitial":"T.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":832742,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Pau, Stephanie","contributorId":190208,"corporation":false,"usgs":false,"family":"Pau","given":"Stephanie","email":"","affiliations":[],"preferred":false,"id":832703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nippert, Jesse","contributorId":273240,"corporation":false,"usgs":false,"family":"Nippert","given":"Jesse","affiliations":[],"preferred":false,"id":832704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slapikas, Ryan","contributorId":273467,"corporation":false,"usgs":false,"family":"Slapikas","given":"Ryan","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":832741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffith, Daniel Mark 0000-0001-7463-4004","orcid":"https://orcid.org/0000-0001-7463-4004","contributorId":271033,"corporation":false,"usgs":true,"family":"Griffith","given":"Daniel","email":"","middleInitial":"Mark","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":832705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bachle, Seton","contributorId":273242,"corporation":false,"usgs":false,"family":"Bachle","given":"Seton","email":"","affiliations":[],"preferred":false,"id":832706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helliker, Brent","contributorId":273243,"corporation":false,"usgs":false,"family":"Helliker","given":"Brent","email":"","affiliations":[],"preferred":false,"id":832707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Connor, Rory","contributorId":273244,"corporation":false,"usgs":false,"family":"O’Connor","given":"Rory","affiliations":[],"preferred":false,"id":832708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riley, William J. 0000-0002-4615-2304","orcid":"https://orcid.org/0000-0002-4615-2304","contributorId":194645,"corporation":false,"usgs":false,"family":"Riley","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832709,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Still, Christopher J.","contributorId":167581,"corporation":false,"usgs":false,"family":"Still","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":24761,"text":"University of California, Santa Barbara; Oregon State University","active":true,"usgs":false}],"preferred":false,"id":832710,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zaricor, Marissa","contributorId":273245,"corporation":false,"usgs":false,"family":"Zaricor","given":"Marissa","email":"","affiliations":[],"preferred":false,"id":832711,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70248850,"text":"70248850 - 2022 - Operational assessment tool for forest carbon dynamics for the United States: A new spatially explicit approach linking the LUCAS and CBM-CFS3 models","interactions":[],"lastModifiedDate":"2023-09-22T13:35:38.842499","indexId":"70248850","displayToPublicDate":"2022-02-02T08:26:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1183,"text":"Carbon Balance and Management","active":true,"publicationSubtype":{"id":10}},"title":"Operational assessment tool for forest carbon dynamics for the United States: A new spatially explicit approach linking the LUCAS and CBM-CFS3 models","docAbstract":"Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a “gain–loss” approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3).
We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year−1. Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source.
The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.
","language":"English","publisher":"BMC","doi":"10.1186/s13021-022-00201-1","usgsCitation":"Sleeter, B.M., Frid, L., Rayfield, B., Daniel, C., Zhu, Z., and Marvin, D., 2022, Operational assessment tool for forest carbon dynamics for the United States: A new spatially explicit approach linking the LUCAS and CBM-CFS3 models: Carbon Balance and Management, v. 17, 1, 26 p., https://doi.org/10.1186/s13021-022-00201-1.","productDescription":"1, 26 p.","ipdsId":"IP-135920","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448919,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13021-022-00201-1","text":"Publisher Index Page"},{"id":435982,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QUIRNP","text":"USGS data release","linkHelpText":"Carbon stocks and fluxes 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bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":883878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frid, Leonardo","contributorId":196604,"corporation":false,"usgs":false,"family":"Frid","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":883879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rayfield, Bronwyn 0000-0003-1768-1300","orcid":"https://orcid.org/0000-0003-1768-1300","contributorId":203690,"corporation":false,"usgs":false,"family":"Rayfield","given":"Bronwyn","email":"","affiliations":[{"id":36690,"text":"Apex Resource Management Solutions","active":true,"usgs":false}],"preferred":false,"id":883880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Colin","contributorId":197531,"corporation":false,"usgs":false,"family":"Daniel","given":"Colin","affiliations":[],"preferred":false,"id":883881,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":883882,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marvin, Dave","contributorId":330032,"corporation":false,"usgs":false,"family":"Marvin","given":"Dave","email":"","affiliations":[{"id":78770,"text":"Salo Sciences","active":true,"usgs":false}],"preferred":false,"id":883883,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241787,"text":"70241787 - 2022 - Context-dependent variation in persistence of host populations in the face of disease","interactions":[],"lastModifiedDate":"2023-03-27T12:07:35.971729","indexId":"70241787","displayToPublicDate":"2022-02-02T07:06:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Context-dependent variation in persistence of host populations in the face of disease","docAbstract":"Research Highlight: Valenzuela-Sánchez, A., Azat, C., Cunningham, A. A., Delgado, S., Bacigalupe, L. D., Beltrand, J., Serrano, J. M., Sentenac, H., Haddow, N., Toledo, V., Schmidt, B. R., & Cayuela, H. (2022). Interpopulation differences in male reproductive effort drive the population dynamics of a host exposed to an emerging fungal pathogen. Journal of Animal Ecology, 00, 1– 12. https://doi.org/10.1111/1365-2656.13603. Understanding the nuances of population persistence in the face of a stressor can help predict extinction risk and guide conservation actions. However, the exact mechanisms driving population stability may not always be known. In this paper, Valenzuela-Sánchez et al. (2022) integrate long-term mark–recapture data, focal measurements of reproductive effort, a population matrix model and inferences on life-history variation to reveal differences in demographic response to disease in a susceptible frog species (Rhinoderma darwinii). Valenzuela-Sánchez et al. found that demographic compensation via recruitment explained the positive population growth rate in their high disease prevalence population whereas the low disease prevalence population did not compensate and thus had decreasing population growth. Compensatory recruitment was likely due to the high probability of males brooding, and the high number of brooded larvae in the high prevalence population compared to low prevalence and disease-free populations. Valenzuela-Sánchez et al. also document faster generation times in the high prevalence population, which may indicate a faster life history that may be contributing to the population's ability to compensate for reduced survival. Lastly, the authors find a positive relationship between disease prevalence and the proportion of juveniles in a given population that suggest that there may be a threshold for disease prevalence that triggers increased reproductive effort. Altogether, their study provides novel support for increased reproductive effort as the pathway for compensatory recruitment leading to increasing population growth despite strong negative effects of disease on adult survival. Their results also caution the overgeneralization of the effects of stressors (e.g. disease) on population dynamics, where context-dependent responses may differ among host populations of a given species.
Groundwater pumping can cause reductions in streamflow (“streamflow depletion”) that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management-focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion-related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.
Length-at-age data are commonly used to describe growth of fish, and obtaining these data typically involves estimating ages from calcified structures (e.g., fin spines or rays, otoliths, or cleithra). Verifying the accuracy of age and growth estimates for long-lived fish is often difficult because known-age fish are not available for all ages in a population. Mark–recapture methods offer nonlethal alternatives for estimating growth of fish that do not require age data. However, few studies have compared growth trajectories estimated from mark–recapture data with trajectories estimated using the standard von Bertalanffy growth function (VBGF) incorporating length-at-age data from known-age fish. We used a robust data set of Muskellunge Esox masquinongy sampled from Green Bay, Lake Michigan, during 1990–2018 to compare growth trajectories estimated from three mark–recapture models and a VBGF fitted to length-at-age data from known-age individuals. Growth trajectories estimated with mark–recapture models were similar to trajectories estimated with a VBGF using known-age fish. Our results suggest that using recapture of tagged fish provides a viable alternative for describing Muskellunge growth trajectories compared with using ages estimated from calcified structures, where incorrect age estimates represent an additional source of error.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10757","usgsCitation":"Sheffer, R., Hogler, S., and Isermann, D.A., 2022, Mark-recapture models accurately predict growth trajectories of known-age Muskellunge in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 42, no. 2, p. 410-424, https://doi.org/10.1002/nafm.10757.","productDescription":"15 p.","startPage":"410","endPage":"424","ipdsId":"IP-129450","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan, Sturgeon Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.27706007733632,\n 44.35374357415128\n ],\n [\n -86.88196993224543,\n 44.35374357415128\n ],\n [\n -86.88196993224543,\n 45.190198563184424\n ],\n [\n -88.27706007733632,\n 45.190198563184424\n ],\n [\n -88.27706007733632,\n 44.35374357415128\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sheffer, Robert J.","contributorId":349585,"corporation":false,"usgs":false,"family":"Sheffer","given":"Robert J.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogler, Steven R.","contributorId":349586,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven R.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924492,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249400,"text":"70249400 - 2022 - Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","interactions":[],"lastModifiedDate":"2023-10-05T15:52:18.805799","indexId":"70249400","displayToPublicDate":"2022-02-01T10:40:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","docAbstract":"Based on numerous studies of the relevant geodetic data, a low-angle thrusting mechanism has been assigned to the 1960 Chile earthquake. Kanamori, Rivera and Lambotte recently suggested that a component of dextral slip comparable to the thrusting be included in the mechanism to satisfy long-period, teleseismic observations. The absence of geodetic evidence for that huge strike-slip component is the subject of this comment. The geodetic data are largely measurements of coseismic uplift associated with the earthquake but include eight measurements of the coseismic change in shear strain. Because strike-slip produces relatively little uplift except near the end points of the rupture, identification of that strike-slip component in the geodetic data depends upon the measured, shear-strain change. I consider elastic, half-space models of oblique slip on the plate interface possibly supplemented by simultaneous dextral slip on the nearby, intra-arc Liquiñe-Ofqui Fault Zone. Slip is assumed to be uniform along strike. The best fits to the geodetic data for these models furnish little evidence for strike-slip on those structures. To satisfy the long-period, teleseismic data, Kanamori et al. proposed six examples, each of which requires a large amount of dextral slip. Because the long-period, teleseismic data do not define the slip distributions, I have used the best fits of those examples to the geodetic data to define those distributions. The large thrusting near the deformation front required by those slip distributions implies large uplift there, contrary to the uplift inferred from the inversion of tsunami data. However, an acceptable fit to the geodetic data and the tsunami data for the six examples suggested by Kanamori et al. can be obtained if the seismic moments specified by them are reduced by a factor ∼1.8, a factor within the uncertainties in estimating seismic moments of the 1960 Chile earthquake. The presence of strike-slip in those reduced-moment examples despite the lack of geodetic evidence for strike-slip is due to a remarkable coincidence that requires careful balancing of contributions from the shallower (depths < 70 km) coseismic sources against those from the deeper coseismic sources to nullify the geodetic evidence for strike-slip. Such balancing is possible, but it is remarkable that the balancing is so nearly perfect that it nullifies the geodetic evidence for strike-slip and thereby confounds the interpretation of the geodetic data.
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surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","interactions":[],"lastModifiedDate":"2022-05-13T14:39:58.812289","indexId":"70227892","displayToPublicDate":"2022-02-01T10:17:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","docAbstract":"Augmentation of wild populations with captive-bred individuals presents an inherent risk of co-introducing novel pathogens to naïve species, but it can be an important tool for supplementing small or declining populations. Game species used for human enterprise and recreation such as the ring-necked pheasant (Phasianus colchicus) are commonly raised in captivity and released onto public and private wildlands as a method of augmenting naturalized pheasant populations. This study presents findings on pathogen exposure from three sources of serological data collected in California during 2014–2017 including (a) 71 pen-reared pheasants sampled across seven game bird breeding farms, (b) six previously released pen-reared pheasants captured at two study sites where wild pheasants occurred and (c) 79 wild pheasants captured across six study sites. In both pen-reared and wild pheasants, antibodies were detected against haemorrhagic enteritis virus (HEV), infectious laryngotracheitis (ILT), infectious bursal disease virus (IBDV), paramyxovirus type 1 (PMV-1) and Pasteurella multocida (PM). Previously released pen-reared pheasants were seropositive for HEV, ILT, and PM. Generalized linear mixed models accounting for intraclass correlation within groups indicated that pen-reared pheasants were more than twice as likely to test positive for HEV antibodies. Necropsy and ancillary diagnostics were performed in addition to serological testing on 40 pen-reared pheasants sampled from five of the seven farms. Pheasants from three of these farms tested positive by PCR for Siadenovirus, the causative agent of both haemorrhagic enteritis in turkeys and marble spleen disease of pheasants, which are serologically indistinguishable. Following necropsy, owners from the five farms were surveyed regarding husbandry and biosecurity practices. Farms ranged in size from 10,000 to more than 100,000 birds, two farms raised other game bird species on premises, and two farms used some form of vaccination. Biosecurity practices varied by farm, but the largest farm implemented the strictest practices.
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