Rapids habitats are critical spawning and nursery grounds for multiple Laurentian Great Lakes fishes of ecological importance such as lake sturgeon, walleye, and salmonids. However, river modifications have destroyed important rapids habitat in connecting channels by modifying flow profiles and removing large quantities of cobble and gravel that are preferred spawning substrates of several fish species. The conversion of rapids habitat to slow moving waters has altered fish assemblages and decreased the spawning success of lithophilic species. The St. Marys River is a Great Lakes connecting channel in which the majority of rapids habitat has been lost. However, rapids habitat was restored at the Little Rapids in 2016 to recover important spawning habitat in this river. During the restoration, flow and substrate were recovered to rapids habitat. We sampled the fish community (pre- and post-restoration), focusing on age-0 fishes in order to characterize the response of the fish assemblage to the restoration, particularly for species of importance (e.g. lake whitefish, walleye, Atlantic salmon). Following restoration, we observed a 40% increase in age-0 fish catch per unit effort, increased presence of rare species, and a shift in assemblage structure of age-0 fishes (higher relative abundance of Salmonidae, Cottidae, and Gasterosteidae). We also observed a “transition” period in 2017, in which the assemblage was markedly different from the pre- and post-restoration assemblages and was dominated by Catostomidae. Responses from target species were mixed, with increased Atlantic salmon abundance, first documented presence of walleye and no presence of lake sturgeon or Coregoninae.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.05.009","usgsCitation":"Molina-Moctezuma, A., Godby, N., Kapuscinski, K., Roseman, E., Skubik, K., and Moerke, A., 2021, Response of fish assemblages to restoration of rapids habitat in a Great Lakes connecting channel: Journal of Great Lakes Research, v. 47, no. 4, p. 1182-1191, https://doi.org/10.1016/j.jglr.2021.05.009.","productDescription":"10 p.","startPage":"1182","endPage":"1191","ipdsId":"IP-126170","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":451907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2021.05.009","text":"Publisher Index Page"},{"id":388884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.37362670898438,\n 46.150345757336574\n ],\n [\n -83.9190673828125,\n 46.150345757336574\n ],\n [\n -83.9190673828125,\n 46.538082005463075\n ],\n [\n -84.37362670898438,\n 46.538082005463075\n ],\n [\n -84.37362670898438,\n 46.150345757336574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Molina-Moctezuma, A.","contributorId":247565,"corporation":false,"usgs":false,"family":"Molina-Moctezuma","given":"A.","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":822595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Godby, N.","contributorId":265347,"corporation":false,"usgs":false,"family":"Godby","given":"N.","affiliations":[{"id":6983,"text":"Michigan DNR","active":true,"usgs":false}],"preferred":false,"id":822596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kapuscinski, K.","contributorId":247567,"corporation":false,"usgs":false,"family":"Kapuscinski","given":"K.","email":"","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":822597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skubik, K.","contributorId":265348,"corporation":false,"usgs":false,"family":"Skubik","given":"K.","email":"","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":822599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moerke, A.","contributorId":247569,"corporation":false,"usgs":false,"family":"Moerke","given":"A.","affiliations":[{"id":49581,"text":"Lake Superior State Univ.","active":true,"usgs":false}],"preferred":false,"id":822600,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222099,"text":"70222099 - 2021 - Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","interactions":[],"lastModifiedDate":"2021-07-20T12:18:00.475837","indexId":"70222099","displayToPublicDate":"2021-06-12T07:15:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2476,"text":"Journal of Thermal Biology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","docAbstract":"Under a warmer future climate, thermal refuges could facilitate the persistence of species relying on cold-water habitat. Often these refuges are small and easily missed or smoothed out by averaging in models. Thermal infrared (TIR) imagery can provide empirical water surface temperatures that capture these features at a high spatial resolution (<1 m) and over tens of kilometers. Our study examined how TIR data could be used along with spatial stream network (SSN) models to characterize thermal regimes spatially in the Middle Fork John Day (MFJD) River mainstem (Oregon, USA). We characterized thermal variation in seven TIR longitudinal temperature profiles along the MFJD mainstem and compared them with SSN model predictions of stream temperature (for the same time periods as the TIR profiles). TIR profiles identified reaches of the MFJD mainstem with consistently cooler temperatures across years that were not consistently captured by the SSN prediction models. SSN predictions along the mainstem identified ~80% of the 1-km reach scale temperature warming or cooling trends observed in the TIR profiles. We assessed whether landscape features (e.g., tributary junctions, valley confinement, geomorphic reach classifications) could explain the fine-scale thermal heterogeneity in the TIR profiles (after accounting for the reach-scale temperature variability predicted by the SSN model) by fitting SSN models using the TIR profile observation points. Only the distance to the nearest upstream tributary was identified as a statistically significant landscape feature for explaining some of the thermal variability in the TIR profile data. When combined, TIR data and SSN models provide a data-rich evaluation of stream temperature captured in TIR imagery and a spatially extensive prediction of the network thermal diversity from the outlet to the headwaters.
At present, the most reliable information for inferring storm-time ground electric fields along electrical transmission lines comes from coarsely sampled, national-scale magnetotelluric (MT) data sets, such as that provided by the EarthScope USArray program. An underlying assumption in the use of such data is that they adequately sample the spatial heterogeneity of the surface relationship between geomagnetic and geoelectric fields. Here, we assess the degree to which the density of MT data sampling affects geoelectric hazard assessments. For electrical transmission networks in each of four focus regions across the contiguous United States, we perform two parallel band-limited (101–103 s) hazard analyses: one using only USArray-style (∼70-km station spacing) MT data, and one incorporating denser (≪70-km station spacing) MT data. We find that the use of USArray-style MT sampling alone provides a useful first-order estimate of integrated geoelectric fields along electrical transmission lines. However, we also find that the use of higher density MT data can in some areas lead to order-of-magnitude differences in line-averaged electric field estimates at the level of individual transmission lines and can also yield significant differences in subregional hazard patterns. As we demonstrate using variogram plots, these differences reflect short-spatial-scale variability in Earth conductivity, which in turn reflects regional lithotectonic structure and history. We also provide a cautionary example in the use of electrical conductivity models to predict dense MT data; although valuable for hazard applications, models may only be able to reproduce surface geoelectric fields as captured by the MT data from which they were derived.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002693","usgsCitation":"Murphy, B.S., Lucas, G., Love, J.J., Kelbert, A., Bedrosian, P.A., and Rigler, E.J., 2021, Magnetotelluric sampling and geoelectric hazard estimation: Are national-scale surveys sufficient?: AGU Space Weather, v. 19, no. 7, e2020SW002693, 24 p., https://doi.org/10.1029/2020SW002693.","productDescription":"e2020SW002693, 24 p.","ipdsId":"IP-128631","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002693","text":"Publisher Index Page"},{"id":387180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203124999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 39.70718665682654\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.140625,\n 37.23032838760387\n ],\n [\n -94.921875,\n 37.23032838760387\n ],\n [\n -94.921875,\n 41.64007838467894\n ],\n [\n -99.140625,\n 41.64007838467894\n ],\n [\n -99.140625,\n 37.23032838760387\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.833984375,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 33.211116472416855\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.240234375,\n 44.276671273775186\n ],\n [\n -83.671875,\n 44.276671273775186\n ],\n [\n -83.671875,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 44.276671273775186\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":223556,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg M.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":819287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819291,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263493,"text":"70263493 - 2021 - The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data","interactions":[],"lastModifiedDate":"2025-02-13T14:57:02.527753","indexId":"70263493","displayToPublicDate":"2021-06-11T08:25:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data","docAbstract":"The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source-based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity (IMMI) using velocity and acceleration data, leveraging co-located sensors to avoid problematic signals. An event detection is issued when the observed IMMI exceeds a given threshold(s). We find a two-station detection method using IMMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes (N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify IMMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB021053","usgsCitation":"Kilb, D., Bunn, J.J., Saunders, J.K., Cochran, E.S., Minson, S.E., Baltay Sundstrom, A.S., O’Rourke, C.T., Hoshiba, M., and Kodera, Y., 2021, The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data: JGR Solid Earth, v. 126, no. 7, e2020JB021053, 25 p., https://doi.org/10.1029/2020JB021053.","productDescription":"e2020JB021053, 25 p.","ipdsId":"IP-127285","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487640,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb021053","text":"Publisher Index Page"},{"id":481971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n 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]\n}","volume":"126","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kilb, Debi","contributorId":206552,"corporation":false,"usgs":false,"family":"Kilb","given":"Debi","affiliations":[{"id":37339,"text":"Scripps/UCSD","active":true,"usgs":false}],"preferred":false,"id":927145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunn, Julian J 0000-0002-3798-298X","orcid":"https://orcid.org/0000-0002-3798-298X","contributorId":297107,"corporation":false,"usgs":false,"family":"Bunn","given":"Julian","email":"","middleInitial":"J","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":927146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saunders, Jessie Kate 0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baltay, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Rourke, Colin T 0000-0001-5403-4685","orcid":"https://orcid.org/0000-0001-5403-4685","contributorId":290635,"corporation":false,"usgs":true,"family":"O’Rourke","given":"Colin","email":"","middleInitial":"T","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927151,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoshiba, Mitsuyuki","contributorId":216382,"corporation":false,"usgs":false,"family":"Hoshiba","given":"Mitsuyuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":927152,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kodera, Yuki","contributorId":290636,"corporation":false,"usgs":false,"family":"Kodera","given":"Yuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":927153,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223335,"text":"70223335 - 2021 - Abundance of Gulf Coast Waterdogs (Necturus beyeri) along Bayou Lacombe, Saint Tammany Parish, Louisiana","interactions":[],"lastModifiedDate":"2023-06-09T14:10:31.349995","indexId":"70223335","displayToPublicDate":"2021-06-11T08:18:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Abundance of Gulf Coast Waterdogs (Necturus beyeri) along Bayou Lacombe, Saint Tammany Parish, Louisiana","docAbstract":"Few ecological studies have been conducted on Gulf Coast Waterdogs (Necturus beyeri), and published studies have focused on relatively small stream sections of 125 m to 1.75 km. In 2015, we sampled 25 sites along a 13.4-km stretch of Bayou Lacombe (Saint Tammany Parish, Louisiana, USA) to better understand factors that may influence the distribution of Gulf Coast Waterdogs within streams. We checked 250 unbaited traps once per week for 3 weeks, capturing 170 Gulf Coast Waterdogs at 18 of 25 sites. We used hierarchical models of abundance to estimate abundance at each site, as a function of site covariates including pH, turbidity, and distance from headwaters. The abundance of Gulf Coast Waterdogs within Bayou Lacombe was highest toward the center of the sampled stream segment, but we found no evidence that pH or turbidity affected abundance within our study area. Site-level abundance estimates of Gulf Coast Waterdogs ranged from 0 to 82, and we estimated there were 767 (95% Bayesian credible interval [CRI]: 266–983) Gulf Coast Waterdogs summed across all 25 sampling sites. We derived an estimate of 6,321 (95% CRI: 2,139–15,922) Gulf Coast Waterdogs for the entire 13.4-km stream section, which includes our 25 sites and the adjoining stream reaches between sites. Our results suggest that Gulf Coast Waterdogs may be uncommon or absent in the headwaters, possibly because of shallow water and swift currents with limited preferred habitats. Gulf Coast Waterdogs favor the middle stream reaches with adequate depth and abundant preferred microhabitats.
Isolated, detached sands provide opportunities for large-volume stratigraphic traps in many deepwater petroleum systems. Here we provide a review of the different types of sandbody detachments based on published data from the modern-day seafloor and recent (generally Quaternary-present), shallow-buried strata. Detachment mechanisms can be classified based on their timing of formation relative to deposition of the detached sandbody as well as their process of formation. Syndepositional detachment mechanisms include flow transformation associated with slope failure (Class 1), turbidity current erosion (Class 2), and contourite deposition (Class 3). Post-depositional detachment is related to subsequent erosive processes and truncation of the pre-existing sandbody, either by submarine channels (Class 4), mass-transport events (Class 5), post-depositional sliding or faulting (Class 6) or bottom currents (Class 7). Examples of each of these mechanisms are identified on the modern seafloor, and show that detached sandbodies can form at different locations along the continental slope and rise (from upper slope to basin floor), and between or within different architectural elements (i.e., canyon, channels and lobes). This variation in formation style results in detached sands of highly variable sizes (tens to hundreds of kilometres) and geometries across and along the depositional profile, which are dependent upon the erosive and/or depositional processes involved, as well as the seafloor topography of the area in question. Whilst modern seafloor systems may not always represent the final stratigraphic architecture in the subsurface, they provide important insights into the development of detached sandbodies and therefore serve as potential analogues for subsurface stratigraphic traps.
Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.
Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.
For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.
Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00264-8","usgsCitation":"Buderman, F.E., Gingery, T.M., Diefenbach, D.R., Gigliotti, L., Begley-Miller, D., McDill, M.E., Wallingford, B., Rosenberry, C., and Drohan, P.J., 2021, Caution is warranted when using animal space-use and movement to infer behavioral states: Movement Ecology, v. 9, 30, 12 p., https://doi.org/10.1186/s40462-021-00264-8.","productDescription":"30, 12 p.","ipdsId":"IP-126195","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00264-8","text":"Publisher Index Page"},{"id":396898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Buderman, Frances E.","contributorId":171634,"corporation":false,"usgs":false,"family":"Buderman","given":"Frances","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gingery, Tess M.","contributorId":204865,"corporation":false,"usgs":false,"family":"Gingery","given":"Tess","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":837601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":837599,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gigliotti, Laura C.","contributorId":204828,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":837602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Begley-Miller, Danielle","contributorId":288270,"corporation":false,"usgs":false,"family":"Begley-Miller","given":"Danielle","affiliations":[{"id":54482,"text":"Teatown Lake Reservation","active":true,"usgs":false}],"preferred":false,"id":837603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDill, Marc E.","contributorId":264499,"corporation":false,"usgs":false,"family":"McDill","given":"Marc","email":"","middleInitial":"E.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":837654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wallingford, Bret D.","contributorId":276207,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret D.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":837604,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rosenberry, Christopher S.","contributorId":276209,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":837605,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Drohan, Patrick J.","contributorId":190141,"corporation":false,"usgs":false,"family":"Drohan","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":837655,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221322,"text":"sir20215051 - 2021 - Estimating Piacenzian sea surface temperature using an alkenone-calibrated transfer function","interactions":[],"lastModifiedDate":"2021-06-11T22:34:30.076675","indexId":"sir20215051","displayToPublicDate":"2021-06-10T13:12:07","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5051","displayTitle":"Estimating Piacenzian Sea Surface Temperature Using an Alkenone-Calibrated Transfer Function","title":"Estimating Piacenzian sea surface temperature using an alkenone-calibrated transfer function","docAbstract":"Stationarity of environmental preferences is a primary assumption required for any paleoenvironmental reconstruction using fossil materials based upon calibration to modern organisms. Confidence in this assumption decreases the further back in time one goes, and the validity of the assumption that species temperature tolerances have not changed over time has been challenged in Pliocene studies. We use paired UK′37 (unsaturated ketones with 37 carbon atoms) sea surface temperature (SST) and faunal assemblage data to directly calibrate North Atlantic Piacenzian planktonic foraminifer assemblages to Piacenzian alkenone paleotemperature estimates to provide an alternative paleoceanographic reconstruction approach that does not rely on stationarity. In doing so, we extend Pliocene SST estimates to sites where only quantitative faunal assemblage data were previously available and improve the spatial resolution of the North Atlantic SST reconstruction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215051","usgsCitation":"Dowsett, H.J., Robinson, M.M., and Foley, K.M., 2021, Estimating Piacenzian sea surface temperature using an alkenone-calibrated transfer function: U.S. Geological Survey Scientific Investigations Report 2021–5051, 17 p., https://doi.org/10.3133/sir20215051.","productDescription":"Report: vi, 17 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114329","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386375,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.1038/sdata.2015.76","text":"Scientific Data","linkHelpText":"— A global planktic foraminifer census data set for the Pliocene ocean"},{"id":386376,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7959G1S","text":"USGS data release","linkHelpText":"PRISM late Pliocene (Piacenzian) alkenone-derived SST data"},{"id":386374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5051/sir20215051.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5051"},{"id":386373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5051/coverthb.jpg"},{"id":386377,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://www.ncdc.noaa.gov/paleo/study/27310","text":"National Oceanic and Atmospheric Administration, National Centers for Environmental Information","linkHelpText":"— A global planktic foraminifer census data set for the Pliocene ocean, addendum"}],"contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.
Coseismic landslides are a major source of transportation disruption in mountainous areas, but few approaches exist for rapidly estimating impacts to road networks. We develop a model that links the U.S. Geological Survey (USGS) near-real-time earthquake-triggered landslide hazard model with Open Street Map (OSM) road network data to rapidly estimate segment-level obstruction risk following major earthquake activity worldwide. To train and validate the model, we process OSM data for 15 historical earthquakes and calculate the average segment-level landslide hazard from the USGS model for each event. We then fit a multivariate adaptive regression spline model for the probability of road obstruction as a function of road segment length and landslide hazard, using a training and validation dataset derived from the intersections of road networks with earthquake-triggered landslide inventories. The resulting probabilistic model is well calibrated across a range of earthquake events, with estimated obstruction probabilities matching the relative frequency of potential road obstructions. The model runs quickly and is capable of producing road segment-level obstruction estimates within minutes to hours of a major earthquake. However, in near-real-time application, the accuracy of the obstruction estimates will be dependent on the quality of the ShakeMap shaking estimates, which often improves with time as more information becomes available after the earthquake. By providing a rapid first-order translation of landslide hazard into potential infrastructure impacts, this model helps provide emergency responders with tangible information on initial areas of concern.
Applying models to developed agricultural regions remains a difficult problem because there are no existing modeling codes that represent both the complex physics of the hydrology and anthropogenic manipulations to water distribution and consumption. We apply an integrated groundwater – surface water and hydrologic river operations model to an irrigated river valley in northwestern Nevada/northern California, United States to evaluate the impacts of climate change on snow-fed agricultural systems that use surface water and groundwater conjunctively. We explicitly represent individual surface water rights within the hydrologic model and allow the integrated code to change river diversions in response to earlier snowmelt runoff and water availability. Historically under-used supplemental groundwater rights are dynamically activated within the model to offset diminished surface water deliveries. The model accounts for feedbacks between the natural hydrology and anthropogenic stresses, which is a first-of-its-kind assessment of the impacts of climate change on individual water rights, and more broadly on river basin operations. Earlier snowmelt decreases annual surface water deliveries to all water rights, not just the junior water rights, owing to a lack of surface water storage in the upper river basin capable of capturing earlier runoff. Conversely, downstream irrigators with access to reservoir storage benefit from earlier runoff flowing past upstream points of diversion prior to the start of the irrigation season. Despite regional shifts toward greater reliance on groundwater for irrigation, crop consumption (a common surrogate for crop yield) decreases due to spatiotemporal changes in water supply that preferentially impact a subset of growers in the region.
This final report summarizes activities, outcomes, and lessons learned from a 3-year project titled “Climate Change Adaptation for Coastal National Wildlife Refuges” with the Cape Romain National Wildlife Refuge (NWR) and local partners in the surrounding South Carolina Lowcountry. The Lowcountry is classified as the 10-county area encompassing the coastal plain of South Carolina (this report specifically focuses on Berkeley, Charleston, and Georgetown Counties). The goals of this work, sponsored by the U.S. Geological Survey’s Southeast Climate Adaptation Science Center (SECASC), were to foster active engagement with stakeholders; to develop a comprehensive definition of adaptation problems faced by agencies, organizations, and individuals near the Cape Romain NWR that accounts for global change, local values, knowledge and perceptions; and to encourage social learning and building of effective networks and trust across South Carolina Lowcountry organizations and individuals. Although project scoping began at the scale of the Atlantic seaboard, by engaging with NWRs from Massachusetts to Florida, participating refuge personnel eventually selected the Cape Romain NWR to serve as a case study for testing our goals. The Cape Romain Partnership for Coastal Conservation was established to address global change impacts at a regional level and includes representation from Federal and State resource agencies, local conservation nongovernmental organizations, and organizations representing underserved community interests. Research topics, originating from discussions with Cape Romain Partnership for Coastal Conservation members, focused on quantifying key drivers of change including localized sea-level rise (SLR) predictions, estimates of coastal hurricane inundation as amplified by SLR, and urban growth trends and forecasts. These key drivers provided a foundation to engage stakeholders in planning exercises to begin a process of collective understanding and collaborative decision making. The goal of this process was to develop collective strategies of adaptation to enhance community and ecosystem resilience in the South Carolina Lowcountry.
South Carolina’s Lowcountry is experiencing rapid environmental and social transformation because of SLR rates approaching twice the global average, chronic tidal flooding and catastrophic storm surges, erosion and loss of habitats that provide essential services to wildlife and humans, and increasing social polarization fueled by aggressive low-density urban growth and other forms of land conversion. To support characterizations of plausible future scenarios, we used available or, in some cases, developed new models to project future conditions of key environmental and social-economic drivers. Because of the imprecision of mean global SLR projections, the SECASC commissioned a climatological study to account for local conditions and multiple representative concentration pathways to project a tailored distribution of future sea levels. These projections were matched to SLR scenarios provided by existing models to anticipate the range of future coastal habitat changes in the South Carolina Lowcountry. SLR scenarios were also incorporated into existing storm-surge models, which do not account for alternate baseline sea levels, to project the local effects of future hurricanes. To evaluate the extent and effects of population growth and urban expansion, we relied on an existing urban-growth model to map the spatial distribution of land-conversion probabilities, the total area of which is predicted to increase twofold to threefold over the next 60 years. In addition to this simplified model, an econometric model is in development to account for nonlinear feedback dynamics in land value, land use, and ecosystem service production. Although not yet completed, the goals of this model are to produce more-detailed projections of growth dynamics and to allow predictions of development patterns resulting from alternate land-use planning policies and incentives.
Collaborative planning for an uncertain future requires more than providing decision makers with information on future physical and ecological conditions; developing effective and consensual strategies must also integrate sociological values, multiple cultural perspectives, and an understanding of human behavior. To support broad stakeholder engagement in integrative approaches to adaptation planning, emphasis was placed on the importance of considering differences in how individuals perceive their environment and create meaning. Because cultural frameworks form the basis for perceptions and, ultimately, the behaviors of individuals and institutions, we describe a model of human behavior and how it can be used to understand the effect of cultural complexity and variation in perception on choices, behavioral change, and long-term maintenance of behaviors. We consider a model commonly used in the field of behavioral health that accommodates variation in human perception when describing stages of behavior and the dynamics of behavioral change. Tailoring communication and engagement activities to targeted stakeholders is likely to benefit from increased understanding of behavioral change processes.
The complex nature of this problem limited the usefulness of a traditional decision-analytic approach, we explored alternative methods for engagement, collaborative learning and decision making. Recognizing that project partners and Lowcountry stakeholders may be at different stages of preparedness and interest level for modifying behavior as a function of global change, we facilitated a scenario-planning exercise to familiarize partners with this well-established approach for communicating the opportunities and threats arising under alternative, plausible futures. We developed narratives for four alternative South Carolina Lowcountry scenarios to be used in later strategic planning that focus on quantitative trends for three primary drivers with high impact and high uncertainty: manifestations of climate change, social-political shifts at a global level, and forces of local value and power structures. This scenario-planning exercise underscored the complex relation between the temporospatial scale of the production of ecological goods and services and the institutional scale at which they are managed. We then guided the partners through an assessment of the relevant strengths and weaknesses of the Cape Romain Partnership for Coastal Protection, using the threats and opportunities characterized by each scenario to understand how the partnership might respond when attempting to meet conservation and societal objectives. The partnership identified key strengths including partnership experience, outreach and technical capacities, a substantial conservation land base, and high social cohesion in the South Carolina Lowcountry. Limited communication expertise, institutional inertia, and insufficient staffing and funding were recognized as important weaknesses across the partnership. By examining and scoring combinations of internal strengths and weaknesses and external threats and opportunities, the partnership developed sets of prioritized strategies to consider in the context of a given scenario. Although we had insufficient time to examine all scenarios in detail, the intent was to identify a portfolio of strategic actions to address threats and opportunities represented in multiple plausible futures. Top-ranking strategies encompassed a range of actions that focused on strengthening the conservation community and communicating the benefits of nature (that is, ecosystem services) to leveraging partnerships to expand land protection.
This report also details the methods and preliminary results of several models developed or applied in support of this project. Two parcel-selection algorithms were used to evaluate anticipated habitat changes and patterns of urban growth to guide decisions on optimal conservation reserve design to protect habitat communities. One approach used a widely available planning software (MARXAN) to maximize conservation benefits near the Cape Romain NWR, whereas the other approach was a novel application of economic theory to account for uncertainty in future conditions and for the risks of unanticipated habitat loss. This latter model applies modern portfolio theory to estimate the risk of investing in any portfolio of land parcels (that is, candidate “reserves”) under climate-change uncertainty by quantifying the variation and spatial correlation of conservation benefits derived from each portfolio. We expanded the range of actions beyond simply whether or not to invest in a set of land parcels, an approach commonly used in spatial conservation planning, to also include consideration of divestment from currently protected lands. Such refinements allow for better accounting of system dynamics and can evaluate the benefits of flexible conservation tools such as rolling easements. Model results were conditional on a decision maker’s risk tolerance but highlighted general strategies of land conservation to increase future habitat representation beyond what is expected under the current protected land base. We built models that may help inform coastal planning by estimating salinity dynamics and the performance of oyster reef restoration efforts to predict the combined effects of global change and management of freshwater flows on coastal habitats and the processes that contribute to their resilience. These models can support restoration decisions by evaluating the expected benefits of site locations for shoreline protection and fisheries production. Lastly, we developed a spatially explicit economic model that predicts feedback dynamics among land value, land-use change, and effects on ecosystem service provision to explore zoning policies and incentives on urban growth and ecosystem services.
We summarize these efforts with insights and considerations for the Cape Romain Partnership for Coastal Protection to continue to engage stakeholders in effective adaptation planning. First, notions of place attachment (referred to as sense of place), and the role of culture in social discourse are increasingly being used to understand the complex interactions between society and the environment and how societies respond and adapt to climate change. Sense of place was a unifying theme whenever the future of the South Carolina Lowcountry was discussed. The contribution of the South Carolina Lowcountry’s environmental wealth, rich cultural heritage, and quality of life to sense of place has important implications for how adaptation planning might best be pursued. More community-based governance of the commons (in other words, natural and cultural resources held in common), in which broad stakeholder participation and power sharing are key elements, is considered important. This devolution of governance is characterized by polycentric institutions and self-organizing social networks that promote a local culture of knowledge sharing, problem solving, and learning. These so-called bridging organizations (or individuals) often provide the leadership necessary to bring together potentially disparate Government agencies and institutions, private organizations, and individuals in a collective process of problem solving. Our observations also suggest that the conservation community in the South Carolina Lowcountry views its activities as integral to the broader governance of social-ecological systems, in which responses to the forces of global change are mediated through culture, economics, and politics. Rather than directly competing with other interests, the South Carolina Lowcountry conservation community seems to embrace an interpretation of conservation in which the fundamental objective is the quality of human life rather than environmental protection.
Fundamental to the types of governance reforms described above is the notion of coproduction, in which experts and users collaborate to develop a shared body of knowledge. In this approach, scientists work with stakeholders to help frame questions, design research, and collect and analyze data. Such sustained collaborations are increasingly believed to be an effective way to produce useable (or actionable) science. The emphasis on social learning, leveraging strong social networks, coordinating and deliberating among diverse stakeholders, and applying principles of adaptive management is an essential contribution to adaptive capacity. The diverse and robust set of scientific approaches, methods to help stakeholders collaborate in effective and goal-driven planning processes, and decision tools resulting from this project hopefully will assist Cape Romain NWR and its partners prepare for climatic, ecological, and social changes over the coming decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211021","usgsCitation":"Eaton, M.J., Johnson, F.A., Mikels-Carrasco, J., Case, D.J., Martin, J., Stith, B., Yurek, S., Udell, B., Villegas, L., Taylor, L., Haider, Z., Charkhgard, H., and Kwon, C., 2021, Cape Romain Partnership for Coastal Protection: U.S. Geological Survey Open-File Report 2021–1021, 158 p., https://doi.org/10.3133/ofr20211021.","productDescription":"xii, 158 p.","numberOfPages":"174","onlineOnly":"Y","ipdsId":"IP-100705","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":386276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1021/coverthb.jpg"},{"id":386277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1021/ofr20211021.pdf","text":"Report","size":"33.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1021"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.8431396484375,\n 32.78842902722552\n ],\n [\n -79.815673828125,\n 32.765336175015776\n ],\n [\n -79.63577270507811,\n 32.85421076375021\n ],\n [\n -79.55886840820312,\n 32.92455477363828\n ],\n [\n -79.47784423828125,\n 33.00981511270531\n ],\n [\n -79.3487548828125,\n 33.0063602132054\n ],\n [\n -79.27047729492188,\n 33.12490094278685\n ],\n [\n -79.34600830078125,\n 33.16169660598766\n ],\n [\n -79.50393676757812,\n 33.060471419708115\n ],\n [\n -79.60968017578125,\n 32.99599470276581\n ],\n [\n -79.6673583984375,\n 32.93838636388491\n ],\n [\n -79.68658447265625,\n 32.91533251206152\n ],\n [\n -79.8431396484375,\n 32.78842902722552\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Southeast Climate Adaptation Science Center
U.S. Geological Survey
127 David Clark Labs
Raleigh, NC 27695
The Fluvial Egg Drift Simulator (FluEgg) was developed to simulate the transport and dispersion of invasive carp eggs and larvae in a river. FluEgg currently (2020) supports modeling of bighead carp (Hypophthalmichthys nobilis), silver carp (H. molitrix), and grass carp (Ctenopharyngodon idella), with the planned addition of black carp (Mylopharyngodon piceus) once developmental data are available. FluEgg integrates the biological development of invasive carp eggs and larvae with a particle transport model that simulates the advection and dispersion of the eggs and larvae based on user-supplied one-dimensional hydraulic conditions. FluEgg can be used to evaluate the hydrodynamic suitability of a river for invasive carp spawning, to inform sampling and monitoring efforts, and to identify the most likely spawning areas of captured eggs or larvae.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211052","usgsCitation":"Domanski, M.M., LeRoy, J.Z., Berutti, M., and Jackson, P.R., 2021, Fluvial Egg Drift Simulator (FluEgg) user’s manual: U.S. Geological Survey Open-File Report 2021–1052, 30 p., https://doi.org/10.3133/ofr20211052.","productDescription":"Report: vii, 30 p.; Software Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-120778","costCenters":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1052/coverthb.jpg"},{"id":386270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1052/ofr20211052.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1052"},{"id":386273,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P93UCQR2","text":"USGS software release","linkHelpText":"— FluEgg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Management of imperiled species facing spatiotemporally dynamic threats is difficult. Systems thinking can inform their management by quantifying the impacts that they face. We apply systems thinking to the Northern Gulf of Mexico (NGM) loggerhead (Caretta caretta) Recovery Unit (RU), one of the smallest subpopulations of loggerheads nesting in the USA. We characterized disturbances to nests, management actions, and hatchling production across 12 nesting beaches used by this RU to explore how hatchling production would increase if disturbances were mitigated. Annual hatchling production at sites ranged from 470 to 18,191 hatchlings/year. Washovers (19.3% nests/year), washouts (17.9% nests/year), and predation (13% nests/year) were the most common annual disturbances across sites. Focusing on the most impactful disturbances at just five sites could increase annual NGM RU hatchling production by 2.2–6.7%. Efforts to mitigate washovers and washouts are ongoing in Alabama, but these may be futile against tropical cyclones, which accounted for >80% of washouts in the present study, and further require careful examination of associated adverse side-effects. Efforts to mitigate predation are common throughout this RU, but require improved knowledge of predator ecology to reach full potential. Systems thinking allowed us to create a simple model for assessing disturbances and management strategies in terms of hatchling sea turtles. This model can be augmented to run dynamic simulations of how disturbances and management actions impact hatchling production, and can be applied to other species with similar reproductive strategies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109201","usgsCitation":"Silver-Gorges, I., Ceriani, S.A., Ware, M., Lamb, M., Lamont, M., Becker, J., Carthy, R., Matechik, C., Mitchell, J.C., Pruner, R., Reynolds, M., Smith, B., Snyder, C., and Fuentes, M., 2021, Using systems thinking to inform management of imperiled species: A case study with sea turtles: Biological Conservation, v. 260, 109201, 9 p., https://doi.org/10.1016/j.biocon.2021.109201.","productDescription":"109201, 9 p.","ipdsId":"IP-124524","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":387226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": 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C.","contributorId":205168,"corporation":false,"usgs":false,"family":"Mitchell","given":"Joseph","email":"","middleInitial":"C.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":819431,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pruner, Raya","contributorId":261184,"corporation":false,"usgs":false,"family":"Pruner","given":"Raya","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":819432,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Reynolds, Mike","contributorId":261185,"corporation":false,"usgs":false,"family":"Reynolds","given":"Mike","email":"","affiliations":[{"id":52767,"text":"Share the Beach","active":true,"usgs":false}],"preferred":false,"id":819433,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, Bradley","contributorId":244348,"corporation":false,"usgs":false,"family":"Smith","given":"Bradley","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":819434,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Snyder, Caitlyn","contributorId":261186,"corporation":false,"usgs":false,"family":"Snyder","given":"Caitlyn","email":"","affiliations":[{"id":52763,"text":"Florida Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":819435,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fuentes, Mariana M. P. B.","contributorId":261187,"corporation":false,"usgs":false,"family":"Fuentes","given":"Mariana M. P. B.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":819436,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70222512,"text":"70222512 - 2021 - Riparian forest cover modulates phosphorus storage and nitrogen cycling in agricultural stream sediments","interactions":[],"lastModifiedDate":"2021-08-03T12:03:58.961776","indexId":"70222512","displayToPublicDate":"2021-06-08T09:08:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Riparian forest cover modulates phosphorus storage and nitrogen cycling in agricultural stream sediments","docAbstract":"Watershed land cover affects in-stream water quality and sediment nutrient dynamics. The presence of natural land cover in the riparian zone can reduce the negative effects of agricultural land use on water quality; however, literature evaluating the effects of natural riparian land cover on stream sediment nutrient dynamics is scarce. The objective of this study was to assess if stream sediment phosphorus retention and nitrogen removal varies with riparian forest cover in agricultural watersheds. Stream sediment nutrient dynamics from 28 sites with mixed land cover were sampled three times during the growing season. Phosphorus dynamics and nitrification rates did not change considerably throughout the study period. Sediment total phosphorus concentrations and nitrification rates decreased as riparian forest cover increased likely due to a decline in fine, organic material. Denitrification rates were strongly correlated to surface water nitrate concentrations. Denitrification rate and denitrification enzyme activity decreased with an increase in forest cover during the first sampling period only. The first sampling period coincided with the greatest connectivity between the watershed and in-stream processing, indicating that riparian forest cover indirectly decreased denitrification rates by reducing the concentrations of dissolved nutrients entering the stream. This reduction in load may allow the sediment to maintain greater nitrogen removal efficiency, because bacteria are not saturated with nitrogen. Riparian forest cover also appeared to lessen the effect of agriculture in the watershed by decreasing the amount of fine material in the stream, resulting in reduced phosphorus storage in the stream sediment.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-021-01484-9","usgsCitation":"Kreiling, R.M., Bartsch, L., Perner, P.M., Hlavacek, E., and Christensen, V., 2021, Riparian forest cover modulates phosphorus storage and nitrogen cycling in agricultural stream sediments: Environmental Management, v. 68, p. 279-293, https://doi.org/10.1007/s00267-021-01484-9.","productDescription":"15 p.","startPage":"279","endPage":"293","ipdsId":"IP-115956","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436324,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PAS8DP","text":"USGS data release","linkHelpText":"Great Lakes Restoration Initiative Fox River Basin 2018 Data"},{"id":387625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.14306640625,\n 43.43696596521823\n ],\n [\n -87.1875,\n 43.43696596521823\n ],\n [\n -87.1875,\n 45.91294412737392\n ],\n [\n -89.14306640625,\n 45.91294412737392\n ],\n [\n -89.14306640625,\n 43.43696596521823\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"68","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perner, Patrik Mathis 0000-0002-6142-518X","orcid":"https://orcid.org/0000-0002-6142-518X","contributorId":261675,"corporation":false,"usgs":true,"family":"Perner","given":"Patrik","email":"","middleInitial":"Mathis","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820393,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221326,"text":"70221326 - 2021 - Developing a strategy for the national coordinated soil moisture monitoring network","interactions":[],"lastModifiedDate":"2021-08-03T16:23:29.131132","indexId":"70221326","displayToPublicDate":"2021-06-08T07:46:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Developing a strategy for the national coordinated soil moisture monitoring network","docAbstract":"Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.
We derive a detailed earthquake catalogue and Vp, Vs and Vp/Vs models for the region around the 2019 Mw 6.4 and Mw7.1 Ridgecrest, California, earthquake sequence using data recorded by rapid-response, densely deployed sensors following the Ridgecrest main shock and the regional network. The new catalogue spans a 4-month period, starting on 1 June 2019, and it includes nearly 95 000 events detected and located with iterative updates to our velocity models. The final Vp and Vs models correlate well with surface geology in the top 4 km of the crust and spatial seismicity patterns at depth. Joint interpretation of the derived catalogue, velocity models, and surface geology suggests that (i) a compliant low-velocity zone near the Garlock Fault arrested the Mw 7.1 rupture at the southeast end; (ii) a stiff high-velocity zone beneath the Coso Mountains acted as a strong barrier that arrested the rupture at the northwest end and (iii) isolated seismicity on the Garlock Fault accommodated transtensional-stepover strain triggered by the main events. The derived catalogue and velocity models can be useful for multiple future studies, including further analysis of seismicity patterns, derivations of accurate source properties (e.g. focal mechanisms) and simulations of earthquake processes and radiated seismic wavefields.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab224","usgsCitation":"White, M., Fang, H., Catchings, R.D., Goldman, M., Steidl, J.H., and Ben-Zion, Y., 2021, Detailed traveltime tomography and seismic catalog around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data: Geophysical Journal International, v. 227, no. 1, p. 204-227, https://doi.org/10.1093/gji/ggab224.","productDescription":"24 p.","startPage":"204","endPage":"227","ipdsId":"IP-124945","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.33486333179661,\n 36.08725945725399\n ],\n [\n -118.33486333179661,\n 35.3380510630776\n ],\n [\n -117.28305638115125,\n 35.3380510630776\n ],\n [\n -117.28305638115125,\n 36.08725945725399\n ],\n [\n -118.33486333179661,\n 36.08725945725399\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"227","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Malcolm 0000-0001-7543-3896","orcid":"https://orcid.org/0000-0001-7543-3896","contributorId":351476,"corporation":false,"usgs":false,"family":"White","given":"Malcolm","affiliations":[{"id":47795,"text":"USC","active":true,"usgs":false}],"preferred":false,"id":928573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fang, Hongjian","contributorId":351481,"corporation":false,"usgs":false,"family":"Fang","given":"Hongjian","affiliations":[{"id":47799,"text":"MIT","active":true,"usgs":false}],"preferred":false,"id":928584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ben-Zion, Yehuda 0000-0002-9602-2014","orcid":"https://orcid.org/0000-0002-9602-2014","contributorId":350966,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":928578,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263747,"text":"70263747 - 2021 - Migration efficiency sustains connectivity across agroecological networks supporting sandhill crane migration","interactions":[],"lastModifiedDate":"2025-02-21T15:50:46.112638","indexId":"70263747","displayToPublicDate":"2021-06-08T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Migration efficiency sustains connectivity across agroecological networks supporting sandhill crane migration","docAbstract":"Preserving avian flyway connectivity has long been challenged by our capacity to meaningfully quantify continental habitat dynamics and bird movements at temporal and spatial scales underlying long-distance migrations. Waterbirds migrating hundreds or thousands of kilometers depend on networks of wetland stopover sites to rest and refuel. Entire populations may rely on discrete wetland habitats, particularly in arid landscapes where the loss of limited stopover options can have disproportionately high impacts on migratory cost. Here, we examine flyway connectivity in water-limited ecosystems of western North America using 108 GPS tagged greater sandhill cranes. Bird movements were used to reconstruct wetland stopover networks across three geographically unique sub-populations spanning 12 US-Mexican states and Canadian provinces. Networks were monitored with remote sensing to identify long-term (1988-2019) trends in wetland and agricultural resources supporting migration and evaluated using network theory and centrality metrics as a measure of stopover site importance to flyway connectivity. Sandhill crane space-use was analyzed in stopover locations to identify important ownership and landscape factors structuring bird distributions. Migratory efficiency was the primary mechanism underpinning network function. A small number of key stopover sites important to minimizing movement cost between summering and wintering locations were essential to preserving flyway connectivity. Localized efficiencies were apparent in stopover landscapes given prioritization of space-use by birds where the proximity of agricultural food resources and flooded wetlands minimized daily movements. Model depictions showing wetland declines from 16-18% likely reflect a new normal in landscape drying that could decouple agriculture-waterbird relationships as water scarcity intensifies. Sustaining network resilience will require conservation strategies to balance water allocations preserving agricultural and wetlands on private lands that accounted for 67-96% of habitat use. Study outcomes provide new perspectives of agroecological relationships supporting continental waterbird migration needed to prioritize conservation of landscapes vital to maintaining flyway connectivity.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3543","usgsCitation":"Donnelly, J.P., King, S.L., Knetter, J., Gammonley, J., Dreitz, V., Grisham, B., Nowak, M., and Collins, D., 2021, Migration efficiency sustains connectivity across agroecological networks supporting sandhill crane migration: Ecosphere, v. 12, no. 6, e03543, 22 p., https://doi.org/10.1002/ecs2.3543.","productDescription":"e03543, 22 p.","ipdsId":"IP-122194","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3543","text":"Publisher Index Page"},{"id":482335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","otherGeospatial":"western North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.6397170378491,\n 49.746443185590465\n ],\n [\n -123.6397170378491,\n 27.52099622519954\n ],\n [\n -103.13075969268448,\n 27.52099622519954\n ],\n [\n -103.13075969268448,\n 49.746443185590465\n ],\n [\n -123.6397170378491,\n 49.746443185590465\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, J. Patrick","contributorId":266037,"corporation":false,"usgs":false,"family":"Donnelly","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":54869,"text":"Intermountain West Joint Venture – U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knetter, Jeff","contributorId":351173,"corporation":false,"usgs":false,"family":"Knetter","given":"Jeff","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":928105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gammonley, James H.","contributorId":351174,"corporation":false,"usgs":false,"family":"Gammonley","given":"James H.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928106,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dreitz, Victoria J.","contributorId":351175,"corporation":false,"usgs":false,"family":"Dreitz","given":"Victoria J.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":928107,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grisham, Blake A.","contributorId":341793,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":928108,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nowak, M. Cathy","contributorId":351176,"corporation":false,"usgs":false,"family":"Nowak","given":"M. Cathy","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928109,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collins, Daniel P.","contributorId":351177,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928110,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228558,"text":"70228558 - 2021 - Using structured decision making to incorporate ecological and social values into harvest decisions: Case studies of white-tailed deer and walleye","interactions":[],"lastModifiedDate":"2022-02-14T16:30:15.25773","indexId":"70228558","displayToPublicDate":"2021-06-07T10:27:48","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Using structured decision making to incorporate ecological and social values into harvest decisions: Case studies of white-tailed deer and walleye","docAbstract":"Harvest decisions for fish and wildlife populations often include conflicting ecological, economic, and social values. Using decision analysis, such as structured decision making and adaptive management, as a framework to aid decision makers in multi-objective decision making for setting harvest regulations can lead to a more transparent and resilient decision. The process includes opportunities for inclusion of stakeholders’ concerns, either through multi-party workshops or the use of social science techniques to elicit objectives (i.e., values) and predict consequences of management actions. The authors present two case studies of using decision analysis to determine stakeholders’ objectives, identify alternative harvest strategies, predict the consequences of these alternatives on all objectives, and analyze tradeoffs among objectives. A case study of white-tailed deer (Odocoileus virginianus) in New York State provides an example of combining predictive population modeling and implementation of survey instruments statewide to determine optimal region-specific harvest regulations. Harvest management of walleye (Sander vitreus) provides an example of the inclusion of commercial and recreational angler groups in a series of workshops to make decisions about harvest quotas for one of the world’s largest freshwater fisheries.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Harvest of fish and wildlife: New paradigms for sustainable management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","usgsCitation":"Robinson, K., Fuller, A.K., and Jones, M., 2021, Using structured decision making to incorporate ecological and social values into harvest decisions: Case studies of white-tailed deer and walleye, chap. 9 of Harvest of fish and wildlife: New paradigms for sustainable management, 15 p.","productDescription":"15 p.","ipdsId":"IP-117474","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kelly F.","contributorId":276131,"corporation":false,"usgs":false,"family":"Robinson","given":"Kelly F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":834588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Michael","contributorId":276132,"corporation":false,"usgs":false,"family":"Jones","given":"Michael","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":834589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221212,"text":"ofr20211030I - 2021 - System characterization report on the WorldView-3 Imager","interactions":[{"subject":{"id":70221212,"text":"ofr20211030I - 2021 - System characterization report on the WorldView-3 Imager","indexId":"ofr20211030I","publicationYear":"2021","noYear":false,"chapter":"I","displayTitle":"System Characterization Report on the WorldView-3 Imager","title":"System characterization report on the WorldView-3 Imager"},"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":"2023-01-27T14:35:39.264069","indexId":"ofr20211030I","displayToPublicDate":"2021-06-07T09:23:24","publicationYear":"2021","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":"I","displayTitle":"System Characterization Report on the WorldView-3 Imager","title":"System characterization report on the WorldView-3 Imager","docAbstract":"This report addresses system characterization of the Maxar WorldView-3 satellite 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 in 2020. 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.
WorldView-3 is a high-resolution multispectral satellite launched in 2014 by Maxar Technologies on an Atlas V launch vehicle from Vandenberg Air Force Base in California for Earth resources monitoring. WorldView-3 provides substantial technical improvements to previous WorldView satellites, including spectral bands, ground sample distance, and swath. The WorldView-3 satellite was designed and built by Lockheed Martin for Maxar Technologies using the BCP–5000 bus with the WorldView-3 Imager and the Clouds, Aerosols, Vapors, Ice, and Snow sensor. The high-resolution WorldView-3 Imager is the main instrument, and the Clouds, Aerosols, Vapors, Ice, and Snow sensor provides additional data on obscurants and other atmospheric effects used in data production. More information on Maxar WorldView satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.maxar.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 WorldView-3 has a range of interior geometric performance of −0.09 (−0.07 pixel) to 0.24 meter (0.19 pixel) in band-to-band registration; an exterior geometric performance in the range of a −21.10- (−2.11 pixels) to 28.23-meter (2.82 pixels) offset in comparison to Sentinel-2; a radiometric performance in the range of −0.121 to 1.420 (offset and slope); and a spatial performance in the range of 1.2 to 1.7 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.093 to 0.185.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030I","usgsCitation":"Cantrell, S.J., Christopherson, J.B., Anderson, C., Stensaas, G.L., Ramaseri Chandra, S.N., Kim, M., and Park, S., 2021, System characterization report on the WorldView-3 Imager (ver. 1.1, October 2021), chap. I of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 29 p., https://doi.org/10.3133/ofr20211030I.","productDescription":"Report: v, 29 p.; Version History","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126804","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":391140,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/i/versionHist.txt","text":"Version History","size":"878 B","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2021–1030I Version History"},{"id":391139,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/i/ofr20211030i_ver1.1.pdf","text":"Report","size":"20.1 MB","description":"OFR 2021–1030I"},{"id":391138,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/i/images"},{"id":391137,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/i/ofr20211030i.xml"},{"id":386257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/i/coverthb_2.jpg"}],"edition":"Version 1.0: June 2021; Version 1.1: October 2021","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of Gaofen-1 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 in 2020. These reports present the detail 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.
Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at http://www.cnsageo.com/#/detailIndex?secondIndex=2&id=3&code=8.
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 Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030B","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on the Gaofen-1, chap. B of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 11 p., https://doi.org/10.3133/ofr20211030B.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-126808","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/b/coverthb.jpg"},{"id":386256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/b/ofr20211030b.pdf","text":"Report","size":"3.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030B"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) 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 the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions.
In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html.
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 DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030A","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., and Anderson, C., 2021, System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS), chap. A of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 9 p., https://doi.org/10.3133/ofr20211030A.","productDescription":"iv, 9 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-126586","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/a/coverthb.jpg"},{"id":386253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/a/ofr20211030a.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030A"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198