Scientific progress depends in part on our ability to synthesize heterogeneous data and ideas into new models and paradigms. In environmental sciences, such synthesis has been particularly effective when conducted by collaborative working groups: diverse groups of researchers and practitioners brought together for a concentrated period of collaboration on key questions. Such work is often done at synthesis centres: organizations that promote, fund, organize and host working groups and other collaborative research and training activities1. However, because of the COVID-19 pandemic, synthesis centres have had to rapidly adapt to supporting fully virtual working groups; the eight centres we direct supported 68 virtual working groups in the past year. Based on this experience, we conclude — contrary to a recent editorial on conferences published in this journal2 — that virtual gatherings, while providing a bridge during the pandemic, cannot replace immersive, in-person collaborations. In-person working group meetings involve productive and varied interactions for many hours over consecutive days. We have found that virtual sessions lose effectiveness after a few hours, as participants become fatigued from staring at a screen or juggling local demands. While virtual meetings can work for short, well-delineated tasks, they are less suited for unstructured and free-flowing discussions — and thus struggle to create the social cohesion and trust known to fuel creative breakthroughs during week-long in-person meetings3.
In this article, we present an overview of the research project NGA-East, Next Generation Attenuation for Central and Eastern North America (CENA), and summarize the key methodology and products. The project was tasked with developing a new ground motion characterization (GMC) model for CENA. The final NGA-East GMC model includes a set of 17 median ground motion models (GMMs) for peak ground acceleration and velocity (PGA, PGV) and response spectral ordinates for periods ranging from 0.01 to 10 s. The NGA-East GMMs are applicable to horizontal components of ground motions on very hard rock, for the moment magnitude range of 4.0–8.2, and distances of up to 1500 km. The aleatory standard deviations of GMMs are also provided for site-specific analysis (single-station standard deviation) and for general probabilistic seismic hazard analyses (PSHA) applications (ergodic standard deviation). In addition, adjustment factors are provided for source depth and hanging-wall effects, as well as for hazard computations at sites in the Gulf Coast Region. During the course of the project, several innovative technologies were developed and implemented to increase the transparency and repeatability of the GMC building process. This involved expanding on a set of candidate median GMMs to define and capture an appropriate range of epistemic uncertainty in ground motions. We also developed a new approach for modeling the aleatory variability that was completely independent of the median GMMs. The development made extensive use of the CENA database but also borrowed data from other parts of the world when relevant and led to an integrated suite of models. Through this repeatable process, epistemic uncertainty could be quantified more objectively than before, relying less on expert opinion. The NGA-East project went through a comprehensive Seismic Senior Hazard Analysis Committee (SSHAC) Level 3 peer review process before its release.
Bat species diversity within the United States is greatest in the Southwest, with approximately 30 species present. At least 16 of these bat species hibernate and are susceptible to white-nose syndrome (WNS), which is caused by the fungus Pseudogymnoascus destructans. Since 2006, millions of bats from 35 U.S. states and 7 Canadian provinces have died from WNS. In previous studies of external surfaces of bats sampled from southwestern states, Actinobacteria were detected that were shown to have antifungal properties against P. destructans in laboratory testing. These studies motivated us to expand our research to sites that represent possible gateways for P. destructans to enter the Southwest so that we could establish a baseline of bat microbiota before the arrival of WNS. We surveyed for the presence of bats and their external microbiota at 3 national parks and monuments located in southeastern Colorado and northeastern New Mexico. Our results document new occurrence records of bat species and their external bacteria at each sampling location. Additionally, we provide insight on the composition of bat external microbiota in the absence of P. destructans, while revealing information about the Streptomyces and other possible native defenses of bats against P. destructans at a gateway into the Southwest.
Traditional ground-motion models (GMMs) are used to compute pseudo-spectral acceleration (PSA) from future earthquakes and are generally developed by regression of PSA using a physics-based functional form. PSA is a relatively simple metric that correlates well with the response of several engineering systems and is a metric commonly used in engineering evaluations; however, characteristics of the PSA calculation make application of scaling factors dependent on the frequency content of the input motion, complicating the development and adaptability of GMMs. By comparison, Fourier amplitude spectrum (FAS) represents ground-motion amplitudes that are completely independent from the amplitudes at other frequencies, making them an attractive alternative for GMM development. Random vibration theory (RVT) predicts the peak response of motion in the time domain based on the FAS and a duration, and thus can be used to relate FAS to PSA. Using RVT to compute the expected peak response in the time domain for given FAS therefore presents a significant advantage that is gaining traction in the GMM field. This article provides recommended RVT procedures relevant to GMM development, which were developed for the Next Generation Attenuation (NGA)-East project. In addition, an orientation-independent FAS metric—called the effective amplitude spectrum (EAS)—is developed for use in conjunction with RVT to preserve the mean power of the corresponding two horizontal components considered in traditional PSA-based modeling (i.e., RotD50). The EAS uses a standardized smoothing approach to provide a practical representation of the FAS for ground-motion modeling, while minimizing the impact on the four RVT properties (zeroth moment, m0; bandwidth parameter, δ; frequency of zero crossings, fz; and frequency of extrema, fe). Although the recommendations were originally developed for NGA-East, they and the methodology they are based on can be adapted to become portable to other GMM and engineering problems requiring the computation of PSA from FAS.
Habitat information for small mammals typically consists of anecdotal descriptions or infrequent analyses of habitat use, which often are reported erroneously as signifying habitat preference, requirements, or quality. Habitat preferences can be determined only by analysis of habitat selection, a behavioral process that results in the disproportionate use of one resource over other available resources and occurs in a hierarchical manner across different environmental scales. North American chipmunks (Neotamias and Tamias) are a prime example of the lack of studies on habitat selection for small mammal species. We used the Organ Mountains Colorado chipmunk (N. quadrivittatus australis) as a case study to determine whether previous descriptions of habitat in the literature were upheld in a multiscale habitat selection context. We tracked VHF radiocollared chipmunks and collected habitat information at used and available locations to analyze habitat selection at three scales: second order (i.e., home range), third order (i.e., within home range), and microhabitat scales. Mean home range was 2.55 ha ± 1.55 SD and did not differ between sexes. At the second and third order, N. q. australis avoided a coniferous forest land cover type and favored particular areas of arroyos (gullies) that were relatively steep-sided and greener and contained montane scrub land cover type. At the microhabitat scale, chipmunks selected areas that had greater woody plant diversity, rock ground cover, and ground cover of coarse woody debris. We concluded that habitat selection by N. q. australis fundamentally was different from descriptions of habitat in the literature that described N. quadrivittatus as primarily associated with coniferous forests. We suggest that arroyos, which are unique and rare on the landscape, function as climate refugia for these chipmunks because they create a cool, wet microclimate. Our findings demonstrate the importance of conducting multiscale habitat selection studies for small mammals to ensure that defensible and enduring habitat information is available to support appropriate conservation and management actions.
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III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837232,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238959,"text":"70238959 - 2021 - Toward climate change refugia conservation at an ecoregion scale","interactions":[],"lastModifiedDate":"2022-12-19T13:35:33.868455","indexId":"70238959","displayToPublicDate":"2021-07-12T07:16:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Toward climate change refugia conservation at an ecoregion scale","docAbstract":"Climate change uncertainty poses serious challenges to conservation efforts. One emerging conservation strategy is to identify and conserve climate change refugia: areas relatively buffered from contemporary climate change that enable persistence of valued resources. This management paradigm may be pursued at broad scales by leveraging existing resources and placing them into a tangible framework to stimulate further collaboration that fosters management decision-making. Here, we describe a framework for moving toward operationalizing climate change refugia conservation at an ecoregion scale with an analysis for the Sierra Nevada ecoregion (CA, USA). Structured within the Climate Change Refugia Conservation Cycle, we identify a preliminary suite of conservation priorities for the ecoregion, and demonstrate how existing mapping, data, and applications could be used for identifying, prioritizing, managing, and monitoring refugia. We focus on six stakeholder-identified conservation priorities, including two process-based refugial priorities (snow and fire), and four ecosystem-based refugial priorities (meadows, giant sequoia, old growth forests, and alpine communities). This pilot overview of concepts and resources provides a foundation for both near-term implementation and further discussion in moving from science to conservation practice. Such an approach may provide new practical insights for ecosystem management at ecoregion scales in the face of climate change.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.497","usgsCitation":"Balantic, C., Adams, A., Gross, S., Mazur, R., Sawyer, S., Tucker, J., Vernon, M., Mengelt, C., Morales, J., Thorne, J., Brown, T., Athearn, N., and Morelli, T.L., 2021, Toward climate change refugia conservation at an ecoregion scale: Conservation Science and Practice, v. 3, no. 9, e497, 24 p., https://doi.org/10.1111/csp2.497.","productDescription":"e497, 24 p.","ipdsId":"IP-128761","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":489213,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.497","text":"Publisher Index Page"},{"id":410696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada ecoregion","geographicExtents":"{\n 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Jody","contributorId":300078,"corporation":false,"usgs":false,"family":"Tucker","given":"Jody","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":859380,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vernon, Marian","contributorId":300079,"corporation":false,"usgs":false,"family":"Vernon","given":"Marian","email":"","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":859381,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mengelt, Claudia 0000-0001-7869-5170","orcid":"https://orcid.org/0000-0001-7869-5170","contributorId":147690,"corporation":false,"usgs":false,"family":"Mengelt","given":"Claudia","affiliations":[{"id":16901,"text":"National Research Council, 500 Fifth Street NW, Washington, D.C., 20001, USA","active":true,"usgs":false}],"preferred":false,"id":859382,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morales, Jennifer","contributorId":300081,"corporation":false,"usgs":false,"family":"Morales","given":"Jennifer","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":859383,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thorne, James","contributorId":52444,"corporation":false,"usgs":true,"family":"Thorne","given":"James","affiliations":[],"preferred":false,"id":859384,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Brown, Timothy","contributorId":300083,"corporation":false,"usgs":false,"family":"Brown","given":"Timothy","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":859385,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Athearn, Nicole","contributorId":300084,"corporation":false,"usgs":false,"family":"Athearn","given":"Nicole","affiliations":[{"id":28107,"text":"Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":859386,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859387,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70225605,"text":"70225605 - 2021 - Twenty-five essential research questions to inform the protection and restoration of freshwater biodiversity","interactions":[],"lastModifiedDate":"2021-10-27T12:16:04.834301","indexId":"70225605","displayToPublicDate":"2021-07-12T07:11:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Twenty-five essential research questions to inform the protection and restoration of freshwater biodiversity","docAbstract":"Extreme drought can strongly impact belowground communities and biogeochemical processes, including soil microbial community composition and extracellular enzyme activities (EEAs), which are considered key agents in ecosystem carbon (C) and nutrient cycling. However, our understanding of how seasonal timing of drought during the growing season affects soil microbial communities and their activity remains notably poor. In this study, we investigated the responses of soil physicochemical properties, EEAs, and bacterial community composition to extreme-duration drought imposed in the early-, mid-, or late-stages of the growing season in a semiarid grassland ecosystem in Inner Mongolia, China. Compared with the ambient control, the activities of C-, nitrogen (N)-, and phosphorus (P)-acquisition enzymes were significantly decreased in the mid- and/or late-stages of drought. Bacterial community diversity also significantly decreased in the mid- and late-stage drought treatments. Soil water content was the most important factor explaining changes in soil EEAs and bacterial community composition. At the end of the growing season, the activities of C-, N-, and P-acquisition enzymes had mostly recovered, while the bacterial community diversity in the mid- and late-stage drought treatments was still lower than the ambient control. Overall, our study demonstrates that the effects of extreme drought on soil EEAs and bacterial community composition depend on the timing of drought. Our results highlight that understanding the effects of extreme-duration drought at different stages of the growing season may play a vital role in predicting the responses of belowground function to global changes in grassland ecosystems.
Cultural resources in coastal parks and recreation areas are vulnerable to climate change. The US National Park Service (NPS) is facing the challenge of insufficient budget allocations for both maintenance and climate adaptation of historic structures. Research on adaptation planning for cultural resources has predominately focused on vulnerability assessments of heritage sites; however, few studies integrate multiple factors (e.g., vulnerability, cultural significance, use potential, and costs) that managers should consider when making tradeoff decisions about which cultural resources to prioritize for adaptation. Moreover, heritage sites typically include multiple types of cultural resources, and researchers have yet to examine such complex tradeoffs. This study applies the Optimal Preservation (OptiPres) Model as a decision support framework to evaluate the tradeoffs of adaptation actions among multiple types of historic structures—wooden buildings, masonry and concrete buildings, forts, and batteries—under varying budget scenarios. Results suggest that the resource values of different types of historic structures vary greatly under a range of budget scenarios, and tradeoffs have to be made among different types of historical structures to achieve optimal planning objectives. Moreover, periodic, incremental funding and partial maintenance are identified as optimal funding strategies for preservation needs of cost-intensive historic structures. Also, adaptative use of historical buildings (e.g., building occupancy) can improve the resource values when budgets are constrained. The OptiPres Model provides managers with a unique framework to inform adaptation planning efforts for a broad range of historic structures, which is transferable across coastal parks to enhance historic preservation planning under climate change.
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Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. 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Peru)","interactions":[],"lastModifiedDate":"2021-08-17T14:58:19.177734","indexId":"70222424","displayToPublicDate":"2021-07-10T06:52:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9118,"text":"South American Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The Miocene stratigraphy of the Laberinto area (Río Ica Valley) and its bearing on the geological history of the East Pisco Basin (south-central Peru)","docAbstract":"Global sea-level changes and substantial vertical displacement along the Monte Grande Fault (MGF) in the lower Río Ica Valley of south-central Peru influenced the accumulation of bioclast-bearing and diatom-bearing Miocene siliciclastic sediments in an area of the East Pisco forearc basin (EPB) colloquially known as Laberinto. Two depositional hiatuses in the Laberinto area (∼17–14 Ma, ∼12.5–10 Ma) manifest as sediment-filled erosional depressions a few kilometers in breadth. Erosion of the older depression was preceded by an ∼18-Ma massive debris flow, possibly triggered by motion on the MGF causing lower Miocene lithoclastic olistoliths of up to two hundred meters length to spill off the footwall block. Sediment shed from the same footwall block may have formed previously recognized early Miocene deltas. From 14–13 Ma, the older depression filled with sediments herein assigned to the provisionally named Laberinto, Pampa, and Naranja members of the Pisco Formation, the latter member being characterized by marine delta foreset beds. The three members are at least partly correlative with the Pisco-0 sequence of the Pisco Formation. The younger depression was overrun at 10 Ma by debris flows of lithoclastic and granitic cobbles and boulders, then filled with diatomaceous silty sand with five-meter-sized lithoclastic olistoliths. The two lithologies constitute the provisionally named Mature Formation. Radiometric and newly revised biochronological data from throughout the EPB coupled with new diatom data from the Laberinto area have provided new insights into the correlation of sequences within the Chilcatay and Pisco formations and the interaction of local and basin-wide tectonism and global eustatic sea-level events across the basin.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jsames.2021.103458","usgsCitation":"Devries, T.J., Barron, J.A., Urbina-Schmitt, M., Ochoa, D., Esperante, R., and Snee, L.W., 2021, The Miocene stratigraphy of the Laberinto area (Río Ica Valley) and its bearing on the geological history of the East Pisco Basin (south-central Peru): South American Journal of Earth Sciences, v. 111, 103458, 29 p., https://doi.org/10.1016/j.jsames.2021.103458.","productDescription":"103458, 29 p.","ipdsId":"IP-128074","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500814,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/20.500.12866/9900","text":"External Repository"},{"id":387498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"southern Peru","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5859375,\n -16.97274101999901\n ],\n [\n -71.19140625,\n -16.97274101999901\n ],\n [\n -71.19140625,\n -12.897489183755892\n ],\n [\n -75.5859375,\n -12.897489183755892\n ],\n [\n -75.5859375,\n -16.97274101999901\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Devries, Thomas J.","contributorId":261423,"corporation":false,"usgs":false,"family":"Devries","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":52853,"text":"1Burke Museum of Natural History and Culture, University of Washington","active":true,"usgs":false}],"preferred":false,"id":819999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Urbina-Schmitt, Mario","contributorId":261432,"corporation":false,"usgs":false,"family":"Urbina-Schmitt","given":"Mario","email":"","affiliations":[],"preferred":false,"id":820012,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ochoa, Diana","contributorId":261433,"corporation":false,"usgs":false,"family":"Ochoa","given":"Diana","email":"","affiliations":[],"preferred":false,"id":820013,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esperante, Raul","contributorId":261434,"corporation":false,"usgs":false,"family":"Esperante","given":"Raul","email":"","affiliations":[],"preferred":false,"id":820014,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Snee, Lawrence W.","contributorId":199028,"corporation":false,"usgs":false,"family":"Snee","given":"Lawrence","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":820015,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221906,"text":"70221906 - 2021 - Genetic structure and diversity of the mustard hill coral Porites astreoides along the Florida Keys reef tract","interactions":[],"lastModifiedDate":"2021-07-15T09:52:12.49169","indexId":"70221906","displayToPublicDate":"2021-07-09T12:10:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3883,"text":"Marine Biodiversity Records","active":true,"publicationSubtype":{"id":10}},"title":"Genetic structure and diversity of the mustard hill coral Porites astreoides along the Florida Keys reef tract","docAbstract":"Increases in local and global stressors have led to major declines in coral populations throughout the western Atlantic. While abundances of other species have declined, however, the relative abundance of the mustard hill coral, Porites astreoides, has increased. Porites astreoides is relatively resilient to some stressors, and because of its mixed reproductive strategies, its populations often recover quickly following disturbances. The ability for P. astreoides to continue as a potential “winner” in western Atlantic reefs relies on maintaining sufficient genetic variation within populations to support acclimatization and adaptation to current and future environmental change. Without high genetic diversity and gene flow within the population, it would have limited capacity for adaptation and the species’ competitive advantages could be short-lived. In this study, we determined the genetic relatedness of 37 P. astreoides colonies at four shallow reefs along the offshore Florida Keys Reef Tract (FKRT), a region particularly hard-hit by recent disturbances. Using previously designed microsatellite markers, we determined the genetic diversity and connectivity of individuals among and between sites. Our results suggest that the FKRT likely contains a single, well-mixed genetic population of P. astreoides, with high levels of gene flow and evidence for larval migration throughout the region. This suggests that regional populations of P. astreoides likely have a higher chance of maintaining resilience than many other western Atlantic species as they face current and future disturbances.
","language":"English","publisher":"Springer","doi":"10.1007/s12526-021-01196-7","usgsCitation":"Gallery, D.N., Green, M.L., Kuffner, I.B., Lenz, E.A., and Toth, L., 2021, Genetic structure and diversity of the mustard hill coral Porites astreoides along the Florida Keys reef tract: Marine Biodiversity Records, v. 51, 63, 16 p., https://doi.org/10.1007/s12526-021-01196-7.","productDescription":"63, 16 p.","ipdsId":"IP-122851","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451563,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12526-021-01196-7","text":"Publisher Index Page"},{"id":436280,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R8NZ2J","text":"USGS data release","linkHelpText":"DNA Microsatellite Markers for Mustard Hill Coral (Porites astreoides) from the Florida Keys Reef Tract"},{"id":387182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys reef tract","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.111328125,\n 24.5271348225978\n ],\n [\n -79.3212890625,\n 24.5271348225978\n ],\n [\n -79.3212890625,\n 27.488781168937997\n ],\n [\n -84.111328125,\n 27.488781168937997\n ],\n [\n -84.111328125,\n 24.5271348225978\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","noUsgsAuthors":false,"publicationDate":"2021-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallery, Dominique N. 0000-0001-9106-5421","orcid":"https://orcid.org/0000-0001-9106-5421","contributorId":261121,"corporation":false,"usgs":false,"family":"Gallery","given":"Dominique","email":"","middleInitial":"N.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":819275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Michelle L.","contributorId":261122,"corporation":false,"usgs":false,"family":"Green","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":819276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":819277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lenz, Elizabeth A.","contributorId":218227,"corporation":false,"usgs":false,"family":"Lenz","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":819278,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":819279,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221786,"text":"ofr20211064 - 2021 - Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers","interactions":[],"lastModifiedDate":"2021-07-09T18:52:23.587817","indexId":"ofr20211064","displayToPublicDate":"2021-07-09T11:55:00","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":"2021-1064","displayTitle":"Instruments, Methods, Rationale, and Derived Data Used to Quantify and Compare the Trapping Efficiencies of Four Types of Pressure-Difference Bedload Samplers","title":"Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers","docAbstract":"Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota.
Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.
Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume.
Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients.
Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler.
This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211064","usgsCitation":"Gray, J.R., Schwarz, G.E., Dean, D.J., Czuba, J.A., and Groten, J.T., 2021, Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers: U.S. Geological Survey Open-File Report 2021–1064, 61 p., https://doi.org/10.3133/ofr20211064.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098017","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386969,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1064/ofr20211064.pdf","text":"Report","size":"70.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1064"},{"id":386970,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1064/coverthb.jpg"},{"id":386971,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBB2YF","text":"USGS data release","linkHelpText":"Data describing the trapping efficiency of four types of pressure-difference bedload samplers, St. Anthony Falls Laboratory, Minneapolis, Minnesota, 2006"}],"contact":"Chief, Analysis and Prediction Branch
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 415
Reston, VA 20192
During the time period 2001–2006, the U.S. Geological Survey reported mercury-concentration measurements that exceeded the North Carolina water-quality criterion (NCWQC) of 0.012 microgram per liter for total recoverable mercury in streams and reservoirs across the Triangle Area of North Carolina. Mercury data were sparse, however, generally consisting of only one or two water samples per year. Additional monitoring and data analysis were needed to better determine the occurrence and distribution of mercury in the Triangle Area for all seasons and waterbody types as well as associations between mercury concentrations and water-quality and land-use parameters. Water at fifteen reservoir and 14 stream sites across the Triangle Area was sampled at various times between August 2007 and June 2009, with water samples collected from both the surfaces and bottoms of the water columns in reservoirs and from the surfaces of streams. A bed sediment sample was also collected at all reservoir sites and at all but one stream site. A total of 301 water samples was collected at reservoir sites. Filtered and total recoverable mercury were detected in at least one water sample collected from each reservoir site. A total of 77 water samples was collected from stream sites with filtered mercury detected in samples from one-half of these sites, and total recoverable mercury detected in at least one water sample from all but two sites. Total recoverable and filtered mercury concentrations exceeded the NCWQC for mercury more frequently in reservoir than in stream samples. Differences in sampling frequencies among seasons and between streams and reservoirs, however, may have negatively biased overall estimates of mercury concentrations in streams relative to reservoirs. Filtered mercury concentrations in surface-water samples from reservoirs and total recoverable mercury concentrations in bottom samples from reservoirs were highest in the fall, whereas no seasonal trends in filtered or total recoverable mercury were detected from stream samples. Total mercury concentrations were calculated for the bulk sample on the basis of the percentage of the grains in the bulk sample whose diameters that were smaller than 0.0625 millimeters. Total mercury concentrations in bed sediment were generally higher for samples from reservoir sites compared to streams sites, although the highest total mercury concentration in bed sediment was from a stream site. Concentrations of total recoverable mercury in water samples from stream sites all fell within the general range for streams and lakes without on-site significant anthropogenic sources (for example, mercury mines or industrial pollution), whereas samples collected from eight reservoir sites had total mercury concentrations in a range characteristic of sites affected by mercury mines or industrial pollution. Results suggested that litterfall may be a source of mercury in streams, whereas atmospheric deposition is likely a dominant source for reservoirs; however, high concentrations of filtered and total recoverable mercury concentrations in the fall season in some reservoir-water samples may warrant further analysis of potential hydrologic factors. Mercury concentrations in all water and bed sediment samples were below levels expected to cause adverse effects to humans and aquatic biota, indicating that mercury levels at the study sites in the Triangle Area were unlikely to cause an immediate health risk to humans or aquatic organisms. The high variability among several sample replicates for total recoverable mercury, however, indicated that inferences from total recoverable mercury concentrations can be tenuous.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215027","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"McKee, A.M., Fitzgerald, S., and Giorgino, M., 2021, Occurrence and distribution of mercury in streams and reservoirs in the Triangle Area of North Carolina, July 2007–June 2009: U.S. Geological Survey Scientific Investigations Report 2021–5027, 42 p., https://doi.org/10.3133/sir20215027.","productDescription":"Report: x, 42 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-114002","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":387025,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4EMC7","text":"USGS Data Release","linkHelpText":"Water and bed sediment data associated with the occurrence and distribution of mercury in streams and reservoirs in the Triangle Area of North Carolina, July 2007 -June 2009"},{"id":387023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5027/coverthb.jpg"},{"id":387024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5027/sir20215027.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5027"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.64263916015625,\n 35.31512519050729\n ],\n [\n -78.12103271484375,\n 35.31512519050729\n ],\n [\n -78.12103271484375,\n 36.49859745028132\n ],\n [\n -79.64263916015625,\n 36.49859745028132\n ],\n [\n -79.64263916015625,\n 35.31512519050729\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Coral disease is a growing problem for coral reefs globally and diseases have been linked to thermal stress, excess nutrients, overfishing and other human impacts. The Red Sea is a unique environment for corals with a strong environmental gradient characterized by temperature extremes and high salinities, but minimal terrestrial runoff or riverine input and their associated pollution. Yet, relatively little is known about coral diseases in this region. Disease surveys were conducted at 22 reefs within three regions (Yanbu, Thuwal, Al Lith) in the central Red Sea along the Saudi Arabian coast. Surveys occurred in October 2015, which coincided with a hyperthermal-induced bleaching event. Our objectives were to 1) document types, prevalence, and distribution of coral diseases in a region with minimal terrestrial input, 2) compare regional differences in diseases and bleaching along a latitudinal gradient of environmental conditions, and 3) use histopathology to characterize disease lesions at the cellular level. Coral reefs of the central Red Sea had a widespread but a surprisingly low prevalence of disease (<0.5%), based on the examination of >75,750 colonies. Twenty diseases were recorded affecting 16 coral taxa and included black band disease, white syndromes, endolithic hypermycosis, skeletal eroding band, growth anomalies and focal bleached patches. The three most common diseases were Acropora white syndrome (59.1% of the survey sites), Porites growth anomalies (40.9%), and Porites white syndrome (31.8%). Sixteen out of 30 coral genera within transects had lesions and Acropora, Millepora and Lobophyllia were the most commonly affected. Cell-associated microbial aggregates were found in four coral genera including a first report in Stylophora. Differences in disease prevalence, coral cover, amount of heat stress as measured by degree heating weeks (DHW) and extent of bleaching was evident among sites. Disease prevalence was not explained by coral cover or DHW, and a negative relationship between coral bleaching and disease prevalence was found. The northern-most sites off the coast of Yanbu had the highest average disease prevalence and highest average DHW values but no bleaching. Our study provides a foundation and baseline data for coral disease prevalence in the central Red Sea, which is projected to increase as a consequence of increased frequency and severity of ocean warming.
Winter conditions, such as ice cover and snow accumulation, are changing rapidly at northern latitudes and can have important implications for lake processes. For example, snowmelt in the watershed—a defining feature of lake hydrology because it delivers a large portion of annual nutrient inputs—is becoming earlier. Consequently, earlier and a shorter duration of snowmelt are expected to affect annual phytoplankton biomass. To test this hypothesis, we developed an index of runoff timing based on the date when 50% of cumulative runoff between January 1 and May 31 had occurred. The runoff index was computed using stream discharge for inflows, outflows, or for flows from nearby streams for 41 lakes in Europe and North America. The runoff index was then compared with summer chlorophyll-a (Chl-a) concentration (a proxy for phytoplankton biomass) across 5–53 years for each lake. Earlier runoff generally corresponded to lower summer Chl-a. Furthermore, years with earlier runoff also had lower winter/spring runoff magnitude, more protracted runoff, and earlier ice-out. We examined several lake characteristics that may regulate the strength of the relationship between runoff timing and summer Chl-a concentrations; however, our tested covariates had little effect on the relationship. Date of ice-out was not clearly related to summer Chl-a concentrations. Our results indicate that ongoing changes in winter conditions may have important consequences for summer phytoplankton biomass and production.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15797","usgsCitation":"Hrycik, A.R., Isles, P.D., Adrian, R., Albright, M., Bacon, L.C., Berger, S.A., Bhattacharya, R., Grossart, H., Hejzlar, J., Hetherington, A.L., Knoll, L.B., Laas, A., McDonald, C.P., Merrell, K., Nejstgaard, J.C., Nelson, K., Noges, P., Paterson, A.M., Pilla, R.M., Robertson, D., Rudstam, L.G., Rusak, J.A., Sadro, S., Silow, E.A., Stockwell, J.D., Yao, H., Yokota, K., and Pierson, D.C., 2021, Earlier winter/spring runoff and snowmelt during warmer winters lead to lower summer chlorophyll-a in north temperate lakes: Global Change Biology, v. 27, no. 19, p. 4615-4629, https://doi.org/10.1111/gcb.15797.","productDescription":"15 p.","startPage":"4615","endPage":"4629","ipdsId":"IP-121909","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451574,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/111965","text":"External Repository"},{"id":387703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"19","noUsgsAuthors":false,"publicationDate":"2021-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hrycik, Allison R. 0000-0002-0870-3398","orcid":"https://orcid.org/0000-0002-0870-3398","contributorId":217379,"corporation":false,"usgs":false,"family":"Hrycik","given":"Allison","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":820575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Isles, Peter D. F. 0000-0003-4446-6788","orcid":"https://orcid.org/0000-0003-4446-6788","contributorId":261751,"corporation":false,"usgs":false,"family":"Isles","given":"Peter","email":"","middleInitial":"D. 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0000-0001-6230-0146","orcid":"https://orcid.org/0000-0001-6230-0146","contributorId":204090,"corporation":false,"usgs":false,"family":"Pierson","given":"Donald","email":"","middleInitial":"C.","affiliations":[{"id":36836,"text":"Department of Ecology and Genetics, Uppsala University","active":true,"usgs":false}],"preferred":false,"id":820602,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"id":70221816,"text":"sir20215056 - 2021 - Hydraulic modeling at selected dam-removal and culvert-retrofit sites in the northeastern United States","interactions":[],"lastModifiedDate":"2021-07-09T11:58:58.971817","indexId":"sir20215056","displayToPublicDate":"2021-07-08T16:19:59","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":"2021-5056","displayTitle":"Hydraulic Modeling at Selected Dam-Removal and Culvert-Retrofit Sites in the Northeastern United States","title":"Hydraulic modeling at selected dam-removal and culvert-retrofit sites in the northeastern United States","docAbstract":"Aquatic connectivity projects, such as removing dams and modifying culverts, have substantial benefits. The restoration of natural flow conditions improves water quality, sediment transport, aquatic and riparian habitat, and fish passage. These projects can also decrease hazards faced by communities by lowering water-surface elevations of flood waters and by removing the risk of dam breaches associated with aging or inadequate infrastructure.
This report documents and provides results of one- and two-dimensional hydraulic models developed for selected rivers and streams in the northeastern United States where a dam was removed or a culvert was retrofitted. The models were developed for conditions before and after the dam removal or culvert modification. The discharges applied in the models included monthly discharges and flood discharges for the annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent.
This study, by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, demonstrates the benefits resulting from dam removal and retrofitting undersized culverts in terms of decreased water-surface elevations during flooding and improved fish passage. The U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System was used to model the sites in one- and two-dimensional hydraulics, and decreases in the 1-percent annual exceedance probability discharge water-surface elevation were found at all sites studied. The decreases in water-surface elevation at sites in which the impoundment was removed ranged from 1.3 to 10.4 feet. One site, Bradford Dam in Westerly, Rhode Island, had only a 0.2-foot decrease, but at that site the dam was replaced by a series of weirs to retain the upstream impoundment and allow fish passage.
Minimal differences were found between the water-surface elevations computed by the one- and two-dimensional models. The two-dimensional models, however, provide the additional benefit of detailed velocity and depth data throughout the channel at a resolution not possible with a one-dimensional model. These velocity and depth data allowed for assessment of the suitability for fish passage at the sites. Fish passage was improved at all the sites by removing the dams and retrofitting the culvert. Prolonged swim velocity criteria for selected fish species were maintained throughout three of the nine study sites, and burst swim velocity criteria were met at all study sites.
Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 879 million barrels of conventional oil and 286.2 trillion cubic feet of conventional gas in the eastern Mediterranean area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213032","usgsCitation":"Schenk, C.J., Mercier, T.J., Finn, T.M., Woodall, C.A., Marra, K.R., Leathers-Miller, H.M., Le, P.A., and Drake, R.M., II, 2021, Assessment of undiscovered conventional oil and gas resources in the eastern Mediterranean area, 2020 (ver. 1.1): U.S. Geological Survey Fact Sheet 2021−3032, 4 p., https://doi.org/10.3133/fs20213032.","productDescription":"Report: 4 p.; Data Release; Version History","onlineOnly":"N","ipdsId":"IP-126114","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":387002,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3032/coverthb2.jpg"},{"id":387004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3032/fs20213032.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3032"},{"id":387005,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OY2LK9","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Eastern Mediterranean Area Assessment Unit Boundaries and Assessment Input Forms"},{"id":387062,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2021/3032/versionHist.txt","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2021-3032 version history"}],"otherGeospatial":"Eastern Mediterranean Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 24.2578125,\n 30.90222470517144\n ],\n [\n 37.529296875,\n 30.90222470517144\n ],\n [\n 37.529296875,\n 38.20365531807149\n ],\n [\n 24.2578125,\n 38.20365531807149\n ],\n [\n 24.2578125,\n 30.90222470517144\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 8, 2021; Version 1.1: July 10, 2021","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
We simulated the concurrent rapid motion of landslides on an unstable slope at Barry Arm, Alaska. Movement of landslides into the adjacent fjord displaced fjord water and generated a tsunami, which propagated out of Barry Arm. Rather than assuming an initial sea surface height, velocity, and location for the tsunami, we generated the tsunami directly using a model capable of simulating the dynamics of both water and landslide material. The fjord below most of the landslide source area was occupied by the Barry Glacier until about 2012; therefore, our direct simulation of tsunami generation by landslide motion required new topographic and bathymetric data, which was collected in 2020. The topographic data also constrained landslide geometries and volumes. We considered four scenarios based on two landslide volumes and two landslide mobilities—a more mobile, contractive landslide and a less mobile, noncontractive landslide. The larger of the two volumes is 689 × 106 cubic meters (m3)—larger than the volume estimate in a previous study—and reflects the largest plausible volume given current observational data. The considered scenario that generated the largest wave heights resulted in forecast wave heights of over 200 meters (m) in the northern part of Barry Arm, adjacent to the landslide source area and runup on the opposite fjord wall in excess of 500 m. Simulated wave heights in excess of 5 m in southern Barry Arm and in Harriman Fjord occurred within 10–15 minutes (min) of landslide motion. The simulated tsunami reached Whittier, Alaska, approximately 20 min after initial rapid landslide motion, with peak heights of just over 2 m in Passage Fjord, 500 m offshore Whittier, occurring 26 min after initial rapid motion. Time of peak wave heights was consistent with previous modeling. Although results are preliminary and can be refined with additional observations and analyses, they provide a refined assessment of the upper bound of the hazard presented by the Barry Arm landslides. The results herein support the National Oceanic and Atmospheric Administration’s National Tsunami Warning Center mission to detect, forecast, and warn for tsunamis in Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211071","usgsCitation":"Barnhart, K.R., Jones, R.P., George, D.L., Coe, J.A., and Staley, D.M., 2021, Preliminary assessment of the wave generating potential from landslides at Barry Arm, Prince William Sound, Alaska: U.S. Geological Survey Open-File Report 2021–1071, 28 p., https://doi.org/10.3133/ofr20211071.","productDescription":"Report: v, 28 p.; Data Release","ipdsId":"IP-130004","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":386958,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XVJDNP","text":"USGS data release","linkHelpText":"Select model results from simulations of hypothetical rapid failures of landslides into Barry Arm Fjord, Prince William Sound, Alaska"},{"id":386957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1071/ofr20211071.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1071"},{"id":386956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1071/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm, Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.90869140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 60.77659627851085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
The widespread parthenogenetic gecko Lepidodactylus lugubris is comprised of several clonal lineages, at least one of which has been known for some time to have originated from hybridization between its maternal ancestor, Lepidodactylus moestus, and a putatively undescribed paternal ancestor previously known only from remote islands in the Central Pacific. By integrating new genetic sequences from multiple studies on Lepidodactylus and incorporating new genetic sequences from previously sampled populations, we recovered a phylogenetic tree that shows a close genetic similarity between the generally hypothesized paternal hybrid ancestor and a recently described species from Maluku (Indonesia), Lepidodactylus pantai. Our results suggest that the paternal hybrid ancestor of at least one parthenogenetic clone of L. lugubris is conspecific with L. pantai and that the range of this species extends to Palau, the Caroline Islands, the Kei Islands, Wagabu, and potentially other small islands near New Guinea. Deeper genetic structure in the western (Palau, Maluku) versus eastern (eastern Melanesia, Micronesia, Polynesia) part of this species’ range suggests that the western populations likely dispersed via natural colonization, whereas the eastern populations may be the result of human-mediated dispersal. The potential taxonomic affinities and biogeographic history should be confirmed with further morphological and genetic analyses, including research on L. woodfordi from its type locality, which would have nomenclatural priority if found to be conspecific with L. pantai. We recommend referring to the wide-ranging sexual species as Lepidodactylus pantai until such a comparison can be made.
","language":"English","publisher":"Magnolia Press","doi":"10.11646/zootaxa.4999.1.6","usgsCitation":"Karin, B.R., Oliver, P.M., Stubbs, A.L., Afirin, U., Iskandar, D.T., Arida, E., Oong, Z., McGuire, J.A., Kraus, F., Fujita, M.K., Ineich, I., Ota, H., Hathaway, S.A., and Fisher, R., 2021, Who’s your daddy? 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