Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rsta.2021.0314","usgsCitation":"Probert, W.J., Nicol, S., Ferrari, M.J., Li, S., Shea, K., Tildesley, M.J., and Runge, M.C., 2022, Vote-processing rules for combining control recommendations from multiple models: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 380, no. 2233, 20210314, 20 p., https://doi.org/10.1098/rsta.2021.0314.","productDescription":"20210314, 20 p.","ipdsId":"IP-136798","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":467169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsta.2021.0314","text":"Publisher Index Page"},{"id":405525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","issue":"2233","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William JM","contributorId":295493,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"JM","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":849595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":849596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":849597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":849598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":849599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":849600,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849601,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256627,"text":"70256627 - 2022 - Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","interactions":[],"lastModifiedDate":"2024-08-27T16:04:33.044121","indexId":"70256627","displayToPublicDate":"2022-08-13T10:55:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","docAbstract":"The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14163937","usgsCitation":"Gonzalez Perez, A., Abd-Elrahman, A., Wilkinson, B., Johnson, D.J., and Carthy, R., 2022, Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets: Remote Sensing, v. 14, no. 16, 3937, 41 p., https://doi.org/10.3390/rs14163937.","productDescription":"3937, 41 p.","ipdsId":"IP-141836","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14163937","text":"Publisher Index Page"},{"id":433203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Wolf Branch Creek Coastal Nature Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.43286683042422,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.759840853994277\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez Perez, Ali","contributorId":341416,"corporation":false,"usgs":false,"family":"Gonzalez Perez","given":"Ali","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abd-Elrahman, Amr","contributorId":341417,"corporation":false,"usgs":false,"family":"Abd-Elrahman","given":"Amr","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkinson, Benjamin","contributorId":239953,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Daniel J.","contributorId":197828,"corporation":false,"usgs":false,"family":"Johnson","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908384,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247742,"text":"70247742 - 2022 - Brittle faulting at elevated temperature and vanishing effective stress","interactions":[],"lastModifiedDate":"2023-08-15T14:20:11.412815","indexId":"70247742","displayToPublicDate":"2022-08-13T09:12:12","publicationYear":"2022","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":"Brittle faulting at elevated temperature and vanishing effective stress","docAbstract":"If brittle fault strength depends only on friction, slip instability is discouraged at low effective normal stress, σ. Stress drop and the critical stiffness necessary for unstable sliding both vanish with σ; small earthquakes cannot occur. Very low σ is inferred in the source region of low-frequency earthquakes (LFEs) on the San Andreas fault (SAF). Moreover, if pore pressure, p, is undrained at low σ, then instabilities are prevented at all scales. This is due to dilatant strengthening which arises due to a dependence of porosity on strain rate. Dilatant strengthening is σ-independent and dominates at low σ. Undrained p is inferred over time scales of less than a few days for the SAF LFEs. Based on experiments that measure rapid contact overgrowth between 350 and 530°C at very low σ, fault failure controlled by time-dependent cementation is invoked as an explanation for the SAF LFEs. Because this “cohesion” is σ-independent, stress drops can occur at σ = 0. If in addition cohesion exceeds any dilatant strengthening during slip, cohesion dominates strength at low σ. Dilatancy measured in prior faulting and shear experiments indicate that at all stress levels steady-state porosity depends on σ in addition to strain rate. Moreover, porosity at low σ depends elastically on the confining and differential stresses. A model with these additional pore pressure effects, friction, and time-dependent cohesion, applied to the SAF LFEs produces stress drops, slip speeds, and durations that are consistent with the observations, when the shear-induced dilatancy is not extreme.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024335","usgsCitation":"Beeler, N.M., 2022, Brittle faulting at elevated temperature and vanishing effective stress: JGR Solid Earth, v. 127, no. 9, e2022JB024335, 30 p., https://doi.org/10.1029/2022JB024335.","productDescription":"e2022JB024335, 30 p.","ipdsId":"IP-132369","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":880227,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238501,"text":"70238501 - 2022 - Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","interactions":[],"lastModifiedDate":"2022-11-28T12:26:40.467915","indexId":"70238501","displayToPublicDate":"2022-08-13T06:21:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","docAbstract":"Identifying how natural (i.e., unaltered by human activity) and anthropogenic landscape variables influence contemporary functional connectivity in terrestrial organisms can elucidate the genetic consequences of environmental change. We examine population genetic structure and functional connectivity among populations of a declining species, the Blainville's horned lizard (Phrynosoma blainvillii), in the urbanized landscape of the Greater Los Angeles Area in Southern California, USA. Using single nucleotide polymorphism data, we assessed genetic structure among populations occurring at the interface of two abutting evolutionary lineages, and at a fine scale among habitat fragments within the heavily urbanized area. Based on the ecology of P. blainvillii, we predicted which environmental variables influence population structure and gene flow and used gravity models to distinguish among hypotheses to best explain population connectivity. Our results show evidence of admixture between two evolutionary lineages and strong population genetic structure across small habitat fragments. We also show that topography, microclimate, and soil and vegetation types are important predictors of functional connectivity, and that anthropogenic disturbance, including recent fire history and urban development, are key factors impacting contemporary population dynamics. Examining how natural and anthropogenic sources of landscape variation affect contemporary population genetics is critical to understanding how to best manage sensitive species in a rapidly changing landscape.
Habitat loss due to increasing anthropogenic disturbance is the major driver for bird population declines across the globe. Within the Eastern Ghats of India, shrubland bird communities are threatened by shrinking of suitable habitats due to increased anthropogenic disturbance and climate change. The development of an effective habitat management strategy is hampered by the absence of data for this bird community. To address this knowledge gap, we examined foraging sites for 14 shrubland bird species, including three declining species, in three study areas representing the shrubland type of forest community in the Eastern Ghats. We recorded microhabitat features within an 11 m radius of observed foraging points and compared these data with similar data from random plots. We used chi-square to test the association between plant species and bird species for sites where they were observed foraging. We observed significant differences between foraging sites of all the study species and random plots, thus indicating selection for foraging habitat. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were shrub density, vegetational height, vertical foliage stratification, grass height, and percent rock cover. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species (e.g., ground-foraging species differed significantly from other species). Except for two species, all focal birds were associated with at least one plant species. The plant-bird association was based on foraging, structural, or behavioral preferences. Several key factors affecting foraging habitat such as shrub density can be actively managed at the local scale. Strategic and selective harvesting of forest products and a spatially and temporally controlled livestock grazing regime may allow regeneration of scrubland and create conditions favorable to birds.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9192","usgsCitation":"Deshwall, A., Stephenson, S., Panwar, P., DeGregorio, B.A., Kannan, R., and Willson, J., 2022, Foraging habitat selection of shrubland bird community in tropical dry forest: Ecology and Evolution, v. 12, no. 8, e9192, 12 p., https://doi.org/10.1002/ece3.9192.","productDescription":"e9192, 12 p.","ipdsId":"IP-119426","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":486945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9192","text":"Publisher Index Page"},{"id":433371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Andhra Pradesh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 79.37736382663809,\n 13.491137039228363\n ],\n [\n 78.55592471748383,\n 13.448724063058705\n ],\n [\n 78.57234419763398,\n 13.03288769532432\n ],\n [\n 79.44277297167974,\n 13.149403444594242\n ],\n [\n 79.37736382663809,\n 13.491137039228363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Deshwall, A.","contributorId":341560,"corporation":false,"usgs":false,"family":"Deshwall","given":"A.","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":908618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, S.L.","contributorId":341562,"corporation":false,"usgs":false,"family":"Stephenson","given":"S.L.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panwar, P.","contributorId":341564,"corporation":false,"usgs":false,"family":"Panwar","given":"P.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kannan, R.","contributorId":341561,"corporation":false,"usgs":false,"family":"Kannan","given":"R.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willson, J.D.","contributorId":341563,"corporation":false,"usgs":false,"family":"Willson","given":"J.D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908621,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234573,"text":"70234573 - 2022 - Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?","interactions":[],"lastModifiedDate":"2022-08-12T13:49:44.69867","indexId":"70234573","displayToPublicDate":"2022-08-12T08:37:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?","docAbstract":"In 2018 Kīlauea volcano erupted a decade’s worth of basalt, given estimated magma supply rates, triggering caldera collapse. Yet, less than 2.5 years later Kīlauea re-erupted. At the 2018 eruption onset, pressure within the summit reservoir was ~20 MPa above magmastatic. By the onset of collapse this decreased by ~17 MPa. Analysis of magma surges at the 2018 fissures, following collapse events, implies excess pressure at the eruption end of only ~1 MPa. Given the new vent elevation, ∼11 − 12 MPa pressure increase was required to bring magma to the surface in December 2020. Analysis of GPS data between 8/2018 and 12/2020 shows there was a 73% probability that this condition was met at the onset of the 2020 eruption. Given a plausible range of possible vent elevations, there was a 40 to 88% probability of sufficient pressure to bring magma to the surface 100 days before the eruption.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL099270","usgsCitation":"Segall, P., Anderson, K.R., and Wang, T., 2022, Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?: Geophysical Research Letters, v. 49, no. 15, e2022GL099270, 9 p., https://doi.org/10.1029/2022GL099270.","productDescription":"e2022GL099270, 9 p.","ipdsId":"IP-139845","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446792,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl099270","text":"Publisher Index Page"},{"id":405114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kı̄lauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29149055480957,\n 19.397144361572217\n ],\n [\n -155.29020309448242,\n 19.396658607621543\n ],\n [\n -155.28565406799316,\n 19.3992492786023\n ],\n [\n -155.2806758880615,\n 19.400058854824785\n ],\n [\n -155.27252197265625,\n 19.39803490671575\n ],\n [\n -155.26857376098633,\n 19.399654067217075\n ],\n [\n -155.26694297790527,\n 19.402163734150218\n ],\n [\n -155.26007652282715,\n 19.405240047251386\n ],\n [\n -155.25441169738767,\n 19.409611549990895\n ],\n [\n -155.25097846984863,\n 19.410178217668324\n ],\n [\n -155.24763107299805,\n 19.40839725546121\n ],\n [\n -155.24127960205078,\n 19.40928773900289\n ],\n [\n -155.23818969726562,\n 19.411716305695418\n ],\n [\n -155.2393054962158,\n 19.415035288178863\n ],\n [\n -155.2434253692627,\n 19.419325579756944\n ],\n [\n -155.24934768676758,\n 19.423291974992168\n ],\n [\n -155.25441169738767,\n 19.42806771016873\n ],\n [\n -155.25870323181152,\n 19.4303341116379\n ],\n [\n -155.26196479797363,\n 19.43081976498137\n ],\n [\n -155.26780128479004,\n 19.43041505396265\n ],\n [\n -155.27398109436035,\n 19.431791067311856\n ],\n [\n -155.27887344360352,\n 19.429929399409083\n ],\n [\n -155.28050422668457,\n 19.428391483743205\n ],\n [\n -155.28788566589355,\n 19.42078263415394\n ],\n [\n -155.29458045959473,\n 19.415844785823072\n ],\n [\n -155.29458045959473,\n 19.412849624405244\n ],\n [\n -155.29698371887207,\n 19.40977345524309\n ],\n [\n -155.2954387664795,\n 19.405240047251386\n ],\n [\n -155.29329299926758,\n 19.40192086484868\n ],\n [\n -155.29252052307126,\n 19.400544598624666\n ],\n [\n -155.29149055480957,\n 19.397144361572217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Segall, Paul","contributorId":223199,"corporation":false,"usgs":false,"family":"Segall","given":"Paul","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":848871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":848872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Taiyi","contributorId":241095,"corporation":false,"usgs":false,"family":"Wang","given":"Taiyi","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":848873,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234571,"text":"70234571 - 2022 - Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba","interactions":[],"lastModifiedDate":"2022-08-12T13:33:40.507397","indexId":"70234571","displayToPublicDate":"2022-08-12T08:23:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11602,"text":"Ciência e Natura","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba","docAbstract":"The main goal of this investigation is analyzing the impact of insert the ocean-atmosphere coupling into the very short range prediction system of Cuba. The ocean-atmosphere coupled components of the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System are used for this purpose and the hurricane Matthew is selected as study case. Two experiments are performed: first, using a dynamic sea surface temperature, updated daily in the atmospheric model WRF; and second using adynamic coupling between the atmospheric and an oceanic models. For the simulated track, the best results are obtained with the coupled system. The impact of coupling on the maximum wind velocities and minimum central pressure is studied. In the coupled system the sea surface temperature has more influence in the surface latent heat fluxes. Also, with this methodology the dry footprint and the behavior of the precipitation field in the presence of a hurricane are studied. This analysis shows that the hurricane acts like an open and self-sustained system in the numerical experiments. The highest differences in the precipitation simulations are in the significant convective area inside the hurricane.","language":"English","publisher":"Ciência e Natura","doi":"10.5902/2179460X66169","usgsCitation":"Vazquez Proveyer, L., Sierra Lorenzo, M., Cruz Rodriguez, R.C., and Warner, J.C., 2022, Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba: Ciência e Natura, v. 44, no. 3, 24 p., https://doi.org/10.5902/2179460X66169.","productDescription":"24 p.","ipdsId":"IP-122772","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5902/2179460x66169","text":"Publisher Index Page"},{"id":405113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cuba","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-82.26815,23.18861],[-81.40446,23.11727],[-80.61877,23.10598],[-79.67952,22.7653],[-79.28149,22.3992],[-78.34743,22.51217],[-77.9933,22.27719],[-77.14642,21.65785],[-76.52382,21.20682],[-76.19462,21.22057],[-75.59822,21.01662],[-75.67106,20.73509],[-74.9339,20.69391],[-74.17802,20.28463],[-74.29665,20.05038],[-74.96159,19.92344],[-75.63468,19.87377],[-76.32366,19.95289],[-77.75548,19.85548],[-77.08511,20.41335],[-77.49265,20.67311],[-78.13729,20.73995],[-78.48283,21.02861],[-78.71987,21.59811],[-79.285,21.55918],[-80.21748,21.82732],[-80.51753,22.03708],[-81.82094,22.19206],[-82.16999,22.38711],[-81.795,22.63696],[-82.7759,22.68815],[-83.49446,22.16852],[-83.9088,22.15457],[-84.05215,21.91058],[-84.54703,21.80123],[-84.97491,21.89603],[-84.44706,22.20495],[-84.23036,22.56575],[-83.77824,22.78812],[-83.26755,22.98304],[-82.51044,23.07875],[-82.26815,23.18861]]]},\"properties\":{\"name\":\"Cuba\"}}]}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Vazquez Proveyer, Liset","contributorId":293212,"corporation":false,"usgs":false,"family":"Vazquez Proveyer","given":"Liset","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":848867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sierra Lorenzo, Maibys","contributorId":293213,"corporation":false,"usgs":false,"family":"Sierra Lorenzo","given":"Maibys","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":848868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cruz Rodriguez, Roberto Carlos","contributorId":293214,"corporation":false,"usgs":false,"family":"Cruz Rodriguez","given":"Roberto","email":"","middleInitial":"Carlos","affiliations":[{"id":63247,"text":"Department of Atmospheric Physics, National Autonomous University of Mexico, Av. Universidad 3000, 04510, DF, Mexico","active":true,"usgs":false}],"preferred":false,"id":848869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248350,"text":"70248350 - 2022 - Plague and trace metals in natural systems","interactions":[],"lastModifiedDate":"2023-09-08T12:53:03.970706","indexId":"70248350","displayToPublicDate":"2022-08-12T07:42:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16704,"text":"International Journal of Environmental Research and Public Health.","active":true,"publicationSubtype":{"id":10}},"title":"Plague and trace metals in natural systems","docAbstract":"All pathogenic organisms are exposed to abiotic influences such as the microclimates and chemical constituents of their environments. Even those pathogens that exist primarily within their hosts or vectors can be influenced directly or indirectly. Yersinia pestis, the flea-borne bacterium causing plague, is influenced by climate and its survival in soil suggests a potentially strong influence of soil chemistry. We summarize a series of controlled studies conducted over four decades in Russia by Dr. Evgeny Rotshild and his colleagues that investigated correlations between trace metals in soils, plants, and insects, and the detection of plague in free-ranging small mammals. Trace metal concentrations in plots where plague was detected were up to 20-fold higher or lower compared to associated control plots, and these differences were >2-fold in 22 of 38 comparisons. The results were statistically supported in eight studies involving seven host species in three families and two orders of small mammals. Plague tended to be positively associated with manganese and cobalt, and the plague association was negative for copper, zinc, and molybdenum. In additional studies, these investigators detected similar connections between pasturellosis and concentrations of some chemical elements. A One Health narrative should recognize that the chemistry of soil and water may facilitate or impede epidemics in humans and epizootics in non-human animals.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph19169979","usgsCitation":"Kosoy, M., and Biggins, D.E., 2022, Plague and trace metals in natural systems: International Journal of Environmental Research and Public Health., v. 19, no. 16, 9979, 16 p., https://doi.org/10.3390/ijerph19169979.","productDescription":"9979, 16 p.","ipdsId":"IP-139292","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph19169979","text":"Publisher Index Page"},{"id":420659,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan, Mongolia, Russia, Uzbekistan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 89.61243249363457,\n 48.466692874478724\n ],\n [\n 97.55135950387046,\n 47.62550521415008\n ],\n [\n 100.05863199276087,\n 45.61730314876178\n ],\n [\n 107.59703605851308,\n 45.61644272490017\n ],\n [\n 117.91939263544919,\n 50.016697469514526\n ],\n [\n 114.52594957787551,\n 53.226451135161994\n ],\n [\n 104.46048981621641,\n 53.650228892772446\n ],\n [\n 93.26601227091152,\n 53.58743226668929\n ],\n [\n 86.19871833540293,\n 50.60502492758772\n ],\n [\n 89.61243249363457,\n 48.466692874478724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 131.52445743282328,\n 42.959534327424876\n ],\n [\n 133.9963905892347,\n 42.029488763129535\n ],\n [\n 140.3914092267985,\n 47.95819400683317\n ],\n [\n 134.99148683585338,\n 48.88738814243581\n ],\n [\n 133.08363821719092,\n 44.63086350828141\n ],\n [\n 131.70164687710013,\n 44.96600756264411\n ],\n [\n 131.52445743282328,\n 42.959534327424876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 53.655114406350464,\n 45.481667787108734\n ],\n [\n 57.50335847359082,\n 46.82513492063478\n ],\n [\n 58.23209315029882,\n 50.60820394065402\n ],\n [\n 54.78716384083083,\n 50.839955137287575\n ],\n [\n 51.03936589302583,\n 51.86712489459745\n ],\n [\n 46.9304799930151,\n 50.38992855031401\n ],\n [\n 46.094105965124754,\n 48.256122327868354\n ],\n [\n 48.57953872080762,\n 45.92189885652772\n ],\n [\n 51.235842842518906,\n 46.87566419536793\n ],\n [\n 52.97267495391941,\n 46.82532952286226\n ],\n [\n 53.655114406350464,\n 45.481667787108734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 61.93106513607361,\n 43.52165246683586\n ],\n [\n 60.98567078825775,\n 41.00558370742854\n ],\n [\n 62.64289594949247,\n 39.713860066117434\n ],\n [\n 64.25179698173943,\n 39.272687744425355\n ],\n [\n 66.9157819290491,\n 40.774219802330464\n ],\n [\n 65.46901711769192,\n 43.7560452565958\n ],\n [\n 61.93106513607361,\n 43.52165246683586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Kosoy, Michael","contributorId":329584,"corporation":false,"usgs":false,"family":"Kosoy","given":"Michael","email":"","affiliations":[{"id":78666,"text":"KB One Health, LLC","active":true,"usgs":false}],"preferred":false,"id":882651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236729,"text":"70236729 - 2022 - Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","interactions":[],"lastModifiedDate":"2022-09-16T12:22:53.172839","indexId":"70236729","displayToPublicDate":"2022-08-12T07:16:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7514,"text":"Journal of Geophysical Research - Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","docAbstract":"Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.
Glen Torridon (GT) is a recessive-trough feature on the northwestern slope of “Mt. Sharp” in Gale crater, Mars with the highest Fe-/Mg-phyllosilicates abundances detected by the Curiosity rover to date. Understanding the origin of these clay minerals and their relationship with diagenetic processes is critical for reconstructing the nature and habitability of past surface and subsurface environments in Gale crater. We aim to constrain the distribution and extent of diagenesis using compositional and morphological trends observed by visible-to-near infrared reflectance spectra in GT from Mastcam and ChemCam, supported by high-resolution images from the Mars Hand Lens Imager. Spectral features consistent with nontronite and fine-grained red hematite are ubiquitous throughout lower GT, and are strongest where diagenetic features are limited, suggesting that both were formed early, before burial. Diagenetic features increase in both abundance and diversity farther up-section, and we observe morphologic evidence for multiple episodes of diagenesis, with the edge of a diagenetic front partially preserved in the middle stratigraphic member, Knockfarril Hill. Near the contact between GT and the overlying Greenheugh pediment capping unit, we observe a lack of clay minerals with signatures consistent instead with coarse-grained gray hematite, likely formed through late-diagenetic alteration. We hypothesize that the sandstone-dominant Stimson formation acted as a conduit for diagenetic fluid flow into the area and that the clay-rich impermeable GT slowed the flow of those fluids, leading to enhanced alteration surrounding the clay-rich portions of GT, including within the nearby Vera Rubin ridge.
The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225082","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5082, 15 p., https://doi.org/10.3133/sir20225082.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130129","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":404967,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5082"},{"id":405037,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":404966,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5082/images/"},{"id":404965,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.XML"},{"id":404964,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":404963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.pdf","text":"Report","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5082"},{"id":404962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5082/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Oyster Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.50883483886719,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.58606020705239\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Latest climate models project conditions for the end of this century that are generally outside of the human experience. These future conditions affect the resilience and sustainability of ecosystems, alter biogeographic zones, and impact biodiversity. Deep-time records of paleoclimate provide insight into the climate system over millions of years and provide examples of conditions very different from the present day, and in some cases similar to model projections for the future. In addition, the deep-time paleoecologic and sedimentologic archives provide insight into how species and habitats responded to past climate conditions. Thus, paleoclimatology provides essential context for the scientific understanding of climate change needed to inform resource management policy decisions. The Pliocene Epoch (5.3–2.6 Ma) is the most recent deep-time interval with relevance to future global warming. Analysis of marine sediments using a combination of paleoecology, biomarkers, and geochemistry indicates a global mean annual temperature for the Late Pliocene (3.6–2.6 Ma) ∼3°C warmer than the preindustrial. However, the inability of state-of-the-art climate models to capture some key regional features of Pliocene warming implies future projections using these same models may not span the full range of plausible future climate conditions. We use the Late Pliocene as one example of a deep-time interval relevant to management of biodiversity and ecosystems in a changing world. Pliocene reconstructed sea surface temperatures are used to drive a marine ecosystem model for the North Atlantic Ocean. Given that boundary conditions for the Late Pliocene are roughly analogous to present day, driving the marine ecosystem model with Late Pliocene paleoenvironmental conditions allows policymakers to consider a future ocean state and associated fisheries impacts independent of climate models, informed directly by paleoclimate information.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.972179","usgsCitation":"Dowsett, H.J., Jacobs, P., and de Mutsert, K., 2022, Using paleoecological data to inform decision making: A deep-time perspective: Frontiers in Ecology and Evolution, v. 10, 972179, 8 p., https://doi.org/10.3389/fevo.2022.972179.","productDescription":"972179, 8 p.","ipdsId":"IP-141870","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":446807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.972179","text":"Publisher Index Page"},{"id":407619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":269579,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":848146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobs, Peter","contributorId":248861,"corporation":false,"usgs":false,"family":"Jacobs","given":"Peter","email":"","affiliations":[],"preferred":false,"id":848147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Mutsert, Kim","contributorId":194503,"corporation":false,"usgs":false,"family":"de Mutsert","given":"Kim","email":"","affiliations":[],"preferred":false,"id":853377,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252444,"text":"70252444 - 2022 - Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","interactions":[],"lastModifiedDate":"2024-03-25T13:52:21.885788","indexId":"70252444","displayToPublicDate":"2022-08-11T08:42:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","title":"Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","docAbstract":"We released nearly 1.0 million 1-day post-hatch (dph) and 5-dph pallid sturgeon (Scaphirhynchus albus) free embryos in the Missouri River on 1 July 2019 and sequentially captured survivors at multiple sites through a 240-km river reach to quantify daily growth and survival rates during the early life stages. Genetic analysis was used to assign captured fish to released family lots and known ages. Growth rate was similar (0.74–0.75 mm day−1) between the 1- and 5-dph age groups during the 3–4-day dispersal period when water temperature averaged 16.8 °C. Daily survival rate was 0.64 during 1–4 dph for the original 1-dph age group and 0.80 during 5–7 dph for the original 5-dph age group. Total survival during free embryo dispersal (hatch to 9 dph) was estimated as 0.0437. The transition from dispersing as free embryos to settling as benthic larvae was verified for fish originally released as 5 dph. Growth of settled larvae was quantified with a Gompertz model through 75 dph (9 September; 112 mm) when water temperature was 18.8–21.0 °C in the rearing areas. Settled larvae had an estimated daily survival rate of 0.96, and estimated total survival during 9–75 dph was 0.0714. This study provides the first empirical survival estimates for pallid sturgeon early life stages in natural settings and is one of few studies reporting similar information for other sturgeon species. Applications of this work extend to pallid sturgeon restoration programs where population models are being developed to predict recruitment potential and population responses to river management alternatives.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-022-01294-w","usgsCitation":"Braaten, P., Holm, R., Powell, J.A., Heist, E., Buhman, A.C., Holley, C.T., Delonay, A.J., Haddix, T., Wilson, R., and Jacobson, R., 2022, Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota: Environmental Biology of Fishes, v. 105, no. 8, p. 993-1014, https://doi.org/10.1007/s10641-022-01294-w.","productDescription":"12 p.; Data Release","startPage":"993","endPage":"1014","ipdsId":"IP-135304","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446810,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10641-022-01294-w","text":"Publisher Index Page"},{"id":435731,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N2MFV8","text":"USGS data release","linkHelpText":"Pallid sturgeon free embryo drift and dispersal experiment data from the Upper Missouri River, Montana and North Dakota, 2019"},{"id":426965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.89968504532749,\n 48.5\n ],\n [\n -106.89968504532749,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 48.5\n ],\n [\n -106.89968504532749,\n 48.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"105","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm, R.J.","contributorId":334977,"corporation":false,"usgs":false,"family":"Holm","given":"R.J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, J. A.","contributorId":69916,"corporation":false,"usgs":false,"family":"Powell","given":"J.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":897181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heist, E.J.","contributorId":334978,"corporation":false,"usgs":false,"family":"Heist","given":"E.J.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhman, Amy C.","contributorId":334979,"corporation":false,"usgs":false,"family":"Buhman","given":"Amy","email":"","middleInitial":"C.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897179,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haddix, T.M.","contributorId":334982,"corporation":false,"usgs":false,"family":"Haddix","given":"T.M.","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":897186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, R.H.","contributorId":334984,"corporation":false,"usgs":false,"family":"Wilson","given":"R.H.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897185,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236791,"text":"70236791 - 2022 - One shell of a problem: Cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents","interactions":[],"lastModifiedDate":"2023-06-08T14:55:58.163785","indexId":"70236791","displayToPublicDate":"2022-08-11T06:58:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"One shell of a problem: Cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents","docAbstract":"Power line corridors have been repeatedly assessed as habitat for wild bees; however, few studies have examined them as bee habitat relative to nearby crop fields and surrounding landscape context.
We surveyed bee communities in power line corridors near to and isolated from lowbush blueberry fields in two landscape contexts in Maine, U.S.A. We examined the influences of blooming plant abundance and diversity and bee life-history traits including sociality, nesting preference, and body size.
We surveyed wild bees and blooming plants in power line corridors from 2013 to 2015. We calculated landscape composition surrounding sites at multiple scales and gathered bee trait information from the literature. We assessed differences in bee communities owing to landscape context with generalized linear models.
We collected 125 wild bee species and observed a rare plant-pollinator relationship within power line corridors. We found greater bee abundance and species richness throughout a complex, resource-rich landscape, while mass-flowering lowbush blueberry fields enhanced bee species richness only in a simple, resource-poor landscape. Landscape composition and blooming plant diversity varied with landscape context, though only landscape composition influenced bee communities. Solitary and ground-nesting species were more sensitive to landscape context than social or cavity-nesting species.
Power line corridors provide crucial refugia for crop pollinating wild bees in agricultural landscapes with resource-poor natural habitat, while bees may selectively forage in power line corridors within agricultural landscapes containing resource-rich natural habitat. We found high-quality forage within corridors; quantifying nesting resources could clarify corridor use by wild bees.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01495-9","usgsCitation":"Du Clos, B., Drummond, F.A., and Loftin, C., 2022, Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context: Landscape Ecology, v. 37, p. 2619-2634, https://doi.org/10.1007/s10980-022-01495-9.","productDescription":"16 p.","startPage":"2619","endPage":"2634","ipdsId":"IP-124416","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":428535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Du Clos, Brianne","contributorId":336548,"corporation":false,"usgs":false,"family":"Du Clos","given":"Brianne","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Francis A.","contributorId":336549,"corporation":false,"usgs":false,"family":"Drummond","given":"Francis","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":900269,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235722,"text":"70235722 - 2022 - Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","interactions":[],"lastModifiedDate":"2022-08-16T11:50:42.860448","indexId":"70235722","displayToPublicDate":"2022-08-11T06:46:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","docAbstract":"Stream morphology is affected by changes on the surrounding landscape. Understanding the effects of urbanization on stream morphology is a critical factor for land managers to maintain and improve vulnerable stream corridors in urbanizing landscapes. Stormwater practices are used in urban landscapes to manage runoff volumes and peak flows, potentially mitigating alterations to the flow regime that drive changes in channel morphology. However, there remains a paucity of long-term studies assessing watershed-scale relationships between urbanization and effects on stream morphology where green stormwater infrastructure exists in high densities. This paper evaluates the geomorphic changes across four headwater catchments in the Chesapeake Bay Watershed over the course of >10 yr and relates these changes to urban development. Annual cross-sectional surveys conducted from 2002 to 2019 in one forested catchment, one agricultural catchment, and two treatment catchments were used to understand the relationship between urbanization and changes in stream morphology. Six cross-sectional geomorphic metrics were calculated and compared with development timelines and high flow events. A channel evolution model was then used to understand the status of morphologic stability at sites within the study. Results suggest downstream environments in developing areas are more impacted during early phases of suburban construction. Channel change during construction could be a result of sediment and erosion control efforts' limitations on preventing and controlling overland sediment mobilization or of increased discharge causing widening and thus bank-derived sediment to move to the streambed. Results demonstrate that geomorphic metrics are highly variable within a small area and are not always accurate representations of broader landscape changes but rather of the more localized environment at a specific stream segment. Despite a high density of stormwater management facilities in urban catchments, substantial alterations to cross sections were found at multiple locations in each catchment including the controls.
This study utilizes instruments from the Curiosity rover payload to develop an integrated paleoenvironmental and compositional reconstruction for the 65-m thick interval of stratigraphy comprising the Hartmann's Valley and Karasburg members of the Murray formation, Gale crater, Mars. The stratigraphy consists of cross-stratified sandstone (Facies 1), planar-laminated sandstone (Facies 2), and planar-laminated mudstone (Facies 3). Facies 1 is composed of sandstone showing truncated sets of concave-curvilinear laminae stacked into cosets. Sets are estimated to be meter-to sub-meter-scale, consistent with low-height dunes. Thin stratigraphic intervals of Facies 1 and stacking patterns with Facies 2 and 3 support a wet aeolian dune interpretation. Meter-thick packages of planar-laminated sandstone (Facies 2) are interpreted to represent interfingering dune-interdune strata. Facies 3 consists of meter-thick packages of planar-laminated mudstone interpreted to represent lacustrine deposition with persistent standing water. Integration of geochemistry with each facies reveals some compositional control based on the depositional process. Models for source rock composition from Alpha Particle X-Ray Spectrometer measurements show that facies derived from a basaltic source. Alteration indices and geochemical trends provide evidence that moderate chemical weathering occurred before compositional changes due to diagenesis. Differences in wt% FeO(T) and TiO2 between facies are minimal, though trends point to sediment sorting in transport. Comparisons to terrestrial basaltic sedimentary systems indicate that the Hartmann's Valley and Karasburg facies reflect deposition in an environment where diverse subaqueous and subaerial facies persisted adjacent to a long-lived body of water.
Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns.
Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation.
We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014.
Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring.
Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01499-5","usgsCitation":"Enwright, N., Darnell, K.M., and Carter, G.A., 2022, Lacunarity as a tool for assessing landscape configuration over time and informing long-term monitoring: An example using seagrass: Landscape Ecology, v. 37, p. 2689-2705, https://doi.org/10.1007/s10980-022-01499-5.","productDescription":"17 p.; Data Release","startPage":"2689","endPage":"2705","ipdsId":"IP-138701","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":435734,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QT07CZ","text":"USGS data release","linkHelpText":"Seagrass map, Cat Island and Ship Island, Mississippi, 2014"},{"id":405251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417831,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TO5P3R"}],"country":"United States","state":"Mississippi","otherGeospatial":"Cat Island, Ship Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.15748596191406,\n 30.19439868711761\n ],\n [\n -88.85948181152344,\n 30.19439868711761\n ],\n [\n -88.85948181152344,\n 30.261439550638762\n ],\n [\n -89.15748596191406,\n 30.261439550638762\n ],\n [\n -89.15748596191406,\n 30.19439868711761\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217766,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":849155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darnell, Kelly M.","contributorId":272888,"corporation":false,"usgs":false,"family":"Darnell","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":849156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Greg A. 0000-0001-8033-0090","orcid":"https://orcid.org/0000-0001-8033-0090","contributorId":295311,"corporation":false,"usgs":false,"family":"Carter","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":12460,"text":"The University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":849157,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239771,"text":"70239771 - 2022 - Navigating the space between policy and practice: Toward a typology of collaborators in a federal land management agency","interactions":[],"lastModifiedDate":"2023-01-19T12:41:47.667595","indexId":"70239771","displayToPublicDate":"2022-08-11T06:40:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3405,"text":"Society and Natural Resources","active":true,"publicationSubtype":{"id":10}},"title":"Navigating the space between policy and practice: Toward a typology of collaborators in a federal land management agency","docAbstract":"Navigating the space between policy and on-the-ground natural resource management presents unique challenges. We interviewed 22 U.S. Bureau of Land Management Field Office Managers to understand their perceptions toward, and applications of, collaboration with public and private stakeholders. Interviews were transcribed and open-coded using qualitative data analysis software. Then, each interview was represented visually using the MaxQDA MaxMaps feature. We deductively coded each visual model and created a typology based on a mix of salient traits exhibited by each group. Differences emerged in each group’s approach to teaching and learning; communication style; attitude toward collaboration; attention to relational and substantive outcomes; and the ability to create space within the agency mission to achieve mutually beneficial goals. Findings can help agencies navigate the challenges associated with aligning agency directives with on-the-ground realities in different contexts when collaborators exhibit different traits.
The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.
Mineral commodity supply disruptions have the potential to ripple through and impact the economy in many ways. Industrial vulnerability is a crucial component of mineral commodity criticality tools as it provides guidance on the economic importance of these commodities to regional criticality indices. Using an economic model that links mineral commodity end-use data to input-output tables and a linear optimization routine, reductions in economic output of individual industries and of the overall economy may be calculated. Such a model can also help to identify industries, be they direct or indirect consumers of the mineral commodities in question, that are most vulnerable to specific mineral commodity supply disruptions at different disruption magnitudes. In this assessment, 56 commodities’ end-use data for the year 2012 were paired with the United States’ detail-level Benchmark Input-Output accounts to build an industrial vulnerability model. The model does not evaluate the likelihood of specific supply disruptions but can be used to assess potential industry impacts for a range of scenarios. The model findings indicate that when the supplies of mineral commodities such as mica, lithium, and fluorspar were disrupted, large overall economic decline was paired with a large decline in many industries. On the other hand, gold, lead, and rhenium disruptions resulted in low declines and few disrupted industries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2022.102889","usgsCitation":"Manley, R., Alonso, E., and Nassar, N.T., 2022, A model to assess industry vulnerability to disruptions in mineral commodity supplies: Resources Policy, v. 78, 102889, 10 p., https://doi.org/10.1016/j.resourpol.2022.102889.","productDescription":"102889, 10 p.","ipdsId":"IP-135103","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":446828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2022.102889","text":"Publisher Index Page"},{"id":408479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manley, Ross 0000-0002-3341-4766","orcid":"https://orcid.org/0000-0002-3341-4766","contributorId":223012,"corporation":false,"usgs":true,"family":"Manley","given":"Ross","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236343,"text":"70236343 - 2022 - Introduction to the special issue of the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) international guidelines for applying noninvasive geophysical techniques to characterize seismic site conditions","interactions":[],"lastModifiedDate":"2022-09-02T14:21:31.688613","indexId":"70236343","displayToPublicDate":"2022-08-10T09:19:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to the special issue of the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) international guidelines for applying noninvasive geophysical techniques to characterize seismic site conditions","docAbstract":"Knowledge about local seismic site conditions provides critical information to account for site effects that are commonly observed in strong motion recordings. Certainly, other wave propagation effects can influence these observations, which are attributable to variations in material properties of the paths traveled by the waves, as well as the characteristics of the seismic source. However, local geologic conditions, particularly, when under shear-wave excitation, are known to have a strong influence on the behavior of ground shaking in the frequency range that is expected to directly affect the built environment. Thus, shear waves traveling in the shallow subsurface—defined here as tens to hundreds of meters beneath the ground surface—are the main foci for application and research in the earthquake engineering community.
","language":"English","publisher":"Springer","doi":"10.1007/s10950-022-10104-w","usgsCitation":"Yong, A., Askan, A., Cassidy, J., D’Amico, S., Parolai, S., Pilz, M., and Stephenson, W.J., 2022, Introduction to the special issue of the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) international guidelines for applying noninvasive geophysical techniques to characterize seismic site conditions: Journal of Seismology, v. 26, p. 557-566, https://doi.org/10.1007/s10950-022-10104-w.","productDescription":"10 p.","startPage":"557","endPage":"566","ipdsId":"IP-141164","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":446830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10950-022-10104-w","text":"Publisher Index Page"},{"id":406137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Askan, Aysegul 0000-0003-4827-9058","orcid":"https://orcid.org/0000-0003-4827-9058","contributorId":296102,"corporation":false,"usgs":false,"family":"Askan","given":"Aysegul","email":"","affiliations":[{"id":49823,"text":"Middle East Technical University","active":true,"usgs":false}],"preferred":false,"id":850673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cassidy, John","contributorId":175493,"corporation":false,"usgs":false,"family":"Cassidy","given":"John","affiliations":[{"id":7219,"text":"Natural Resources Canada","active":true,"usgs":false}],"preferred":false,"id":850674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"D’Amico, Sebastiano 0000-0001-7429-4767","orcid":"https://orcid.org/0000-0001-7429-4767","contributorId":296104,"corporation":false,"usgs":false,"family":"D’Amico","given":"Sebastiano","email":"","affiliations":[{"id":63987,"text":"University of Malta","active":true,"usgs":false}],"preferred":false,"id":850675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parolai, Stefano 0000-0002-9084-7488","orcid":"https://orcid.org/0000-0002-9084-7488","contributorId":296105,"corporation":false,"usgs":false,"family":"Parolai","given":"Stefano","email":"","affiliations":[{"id":63989,"text":"Instituto Nazionale di Oceonografia","active":true,"usgs":false}],"preferred":false,"id":850676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilz, Marco","contributorId":264169,"corporation":false,"usgs":false,"family":"Pilz","given":"Marco","email":"","affiliations":[],"preferred":false,"id":850677,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":850678,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70234373,"text":"70234373 - 2022 - Diatom influence on the production characteristics of hydrate-bearing sediments: Examples from Ulleung Basin, offshore South Korea","interactions":[],"lastModifiedDate":"2022-08-15T11:31:18.549912","indexId":"70234373","displayToPublicDate":"2022-08-10T08:58:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2382,"text":"Journal of Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Diatom influence on the production characteristics of hydrate-bearing sediments: Examples from Ulleung Basin, offshore South Korea","docAbstract":"The Ulleung Basin Gas Hydrate field expeditions in 2007 (UBGH1) and 2010 (UBGH2) sought to assess the Basin's gas hydrate resource potential. Coring operations in both expeditions recovered evidence of gas hydrate, primarily as fracture-filling (or vein type) morphologies in mainly silt-sized, fine-grained sediment, but also as pore-occupying hydrate in the coarser-grained layers of interbedded sand and fine-grained systems. A commonality across many of these occurrences is the presence of diatoms in the fine-grained sediment. Here we tested fine-grained sediment (median grain size <12.5 μm) associated with hydrate occurrences at four UBGH2 sites (UBGH2-2-2, UBGH2-3, UBGH2-6 and UBGH2-11) to investigate potential impacts of diatoms on efforts to extract methane from hydrate, or to tap hydrocarbon reservoirs beneath hydrate-bearing sediment. Two key considerations are: the extent to which diatoms control sediment mechanical properties, and the extent to which pore-water freshening, which occurs as gas hydrate breaks down during resource extraction, alters the diatom control on sediment mechanical properties. We conducted experiments to measure sediment index properties, sedimentation behavior and compressibility to address these considerations. We relied on scanning electron microscope (SEM) imagery and X-ray powder diffraction (XRD) to characterize the sediment mineralogy. Our high-level findings are that at the ∼20–45% (by volume) diatom concentrations observed at these UBGH2 sites, sediment compressibility increases with diatom content, but diatoms only appear to increase porosity and permeability at the highest diatom concentration (∼45%). Our measurements suggest in situ compression indices of 0.35–0.55 and permeabilities on the order of 0.01milliDarcies (1 × 10−17 m2) can be anticipated at these sites. Importantly, these properties are not expected to vary significantly upon pore water freshening that accompanies gas hydrate dissociation during production.