Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22185","usgsCitation":"Schmidt, J., Wilson, T.L., Thompson, W., and Mangipane, B., 2022, Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration: Journal of Wildlife Management, v. 86, no. 3, e22185, 15 p., https://doi.org/10.1002/jwmg.22185.","productDescription":"e22185, 15 p.","ipdsId":"IP-130320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.33642551397307,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 61.82002560280111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Joshua H.","contributorId":349537,"corporation":false,"usgs":false,"family":"Schmidt","given":"Joshua H.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, William L.","contributorId":349538,"corporation":false,"usgs":false,"family":"Thompson","given":"William L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":349540,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924370,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236994,"text":"70236994 - 2022 - The occurrence and hazards of great subduction zone earthquakes","interactions":[],"lastModifiedDate":"2022-09-27T12:06:13.316143","indexId":"70236994","displayToPublicDate":"2022-01-18T07:03:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"The occurrence and hazards of great subduction zone earthquakes","docAbstract":"Subduction zone earthquakes result in some of the most devastating natural hazards on Earth. Knowledge of where great (moment magnitude M ≥ 8) subduction zone earthquakes can occur and how they rupture is critical to constraining future seismic and tsunami hazards. Since the occurrence of well-instrumented great earthquakes, such as the 2004 M9.1 Sumatra–Andaman and 2011 M9.1 Tohoku earthquakes, the hypotheses that plate age and convergence rate influence the ability of subduction zones to host large earthquakes have been dispelled. In this Review, we highlight how certain subduction zone properties might influence the location and characteristics of great earthquake rupture and impact seismic and tsunami hazard. The rupture characteristics of great earthquakes that most heavily impact earthquake hazards include the rupture extent (seaward and landward), location of strong motion-generating areas and earthquake recurrence. By contrast, large slip or displacement at the seafloor is one of the major controls of tsunami hazard. Future improvements in addressing hazards posed by subduction zones depend heavily on sustained geophysical monitoring in subduction zone systems (both onshore and offshore), expanded development of palaeoseismic data sets and improved integration of observations and models across disciplines and timescales.
Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.
BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s
Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid.
As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.
","language":"English","publisher":"BioMed Central Ltd.","doi":"10.1186/s40317-022-00273-3","usgsCitation":"Nebiolo, K., and Castro-Santos, T.R., 2022, BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data: Animal Biotelemetry, v. 10, p. 1-16, https://doi.org/10.1186/s40317-022-00273-3.","productDescription":"2, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-122256","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-022-00273-3","text":"Publisher Index Page"},{"id":394441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nebiolo, Kevin","contributorId":271123,"corporation":false,"usgs":false,"family":"Nebiolo","given":"Kevin","email":"","affiliations":[{"id":56294,"text":"Kleinschmidt Associates, Essex, CT","active":true,"usgs":false}],"preferred":false,"id":830917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":830918,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227452,"text":"70227452 - 2022 - Risk-based prioritization of organic chemicals and locations of ecological concern in sediment from Great Lakes tributaries","interactions":[],"lastModifiedDate":"2022-03-28T16:40:43.379038","indexId":"70227452","displayToPublicDate":"2022-01-17T08:45:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Risk-based prioritization of organic chemicals and locations of ecological concern in sediment from Great Lakes tributaries","docAbstract":"With improved analytical techniques, environmental monitoring studies are increasingly able to report the occurrence of tens or hundreds of chemicals per site, making it difficult to identify the most relevant chemicals from a biological standpoint. For this study, organic chemical occurrence was examined, individually and as mixtures, in the context of potential biological effects. Sediment was collected at 71 Great Lakes tributary sites and analyzed for 87 chemicals. Multiple risk-based lines of evidence were used to prioritize chemicals and locations, including comparing sediment concentrations and estimated porewater concentrations to established whole-organism benchmarks (i.e., sediment and water quality criteria and screening values) and to high-throughput toxicity screening data from the U.S. Environmental Protection Agency's ToxCast database, estimating additive effects of chemical mixtures on common ToxCast endpoints, and estimating toxic equivalencies for mixtures of alkylphenols and polycyclic aromatic hydrocarbons (PAHs). This multiple-lines-of-evidence approach enabled the screening of more chemicals, mitigated the uncertainties of individual approaches, and strengthened common conclusions. Collectively, at least one benchmark/screening value was exceeded for 54 of the 87 chemicals, with exceedances observed at all 71 of the monitoring sites. Chemicals with the greatest potential for biological effects, both individually and as mixture components, were bisphenol A, 4-nonylphenol, indole, carbazole, and several polycyclic aromatic hydrocarbons (PAHs). Potential adverse outcomes based on ToxCast gene targets and putative adverse outcome pathways relevant to individual chemicals and chemical mixtures included tumors, skewed sex ratios, reproductive dysfunction, hepatic steatosis, and early mortality, among others. Results provide a screening level prioritization of chemicals with the greatest potential for adverse biological effects and an indication of sites where they are most likely to occur.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5286","usgsCitation":"Baldwin, A.K., Corsi, S., Stefaniak, O.M., Loken, L.C., Villeneuve, D.L., Ankley, G., Blackwell, B., Lenaker, P.L., Nott, M.A., and Mills, M.A., 2022, Risk-based prioritization of organic chemicals and locations of ecological concern in sediment from Great Lakes tributaries: Environmental Toxicology and Chemistry, v. 41, no. 4, p. 1016-1041, https://doi.org/10.1002/etc.5286.","productDescription":"26 p.","startPage":"1016","endPage":"1041","ipdsId":"IP-129929","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449145,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/etc.5286","text":"External 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Center","active":true,"usgs":true}],"preferred":true,"id":830958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stefaniak, Owen M. 0000-0001-5394-8338 ostefaniak@usgs.gov","orcid":"https://orcid.org/0000-0001-5394-8338","contributorId":271143,"corporation":false,"usgs":true,"family":"Stefaniak","given":"Owen","email":"ostefaniak@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":197436,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":830962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":830963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blackwell, Brett R.","contributorId":173601,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":830964,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830965,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nott, Michelle A. 0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830966,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":830967,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227766,"text":"70227766 - 2022 - Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty","interactions":[],"lastModifiedDate":"2023-06-09T13:51:25.428617","indexId":"70227766","displayToPublicDate":"2022-01-14T06:44:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty","docAbstract":"Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.
Prior to the emergence of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 influenza A virus, the long-held and well-supported paradigm was that highly pathogenic avian influenza (HPAI) outbreaks were restricted to poultry, the result of cross-species transmission of precursor viruses from wild aquatic birds that subsequently gained pathogenicity in domestic birds. Therefore, management agencies typically adopted a prevention, control, and eradication strategy that included strict biosecurity for domestic bird production, isolation of infected and exposed flocks, and prompt depopulation. In most cases, this strategy has proved sufficient for eradicating HPAI. Since 2002, this paradigm has been challenged with many detections of viral descendants of the Gs/GD lineage among wild birds, most of which have been associated with sporadic mortality events. Since the emergence and evolution of the genetically distinct clade 2.3.4.4 Gs/GD lineage HPAI viruses in approximately 2010, there have been further increases in the occurrence of HPAI in wild birds and geographic spread through migratory bird movement. A prominent example is the introduction of clade 2.3.4.4 Gs/GD HPAI viruses from East Asia to North America via migratory birds in autumn 2014 that ultimately led to the largest outbreak of HPAI in the history of the United States. Given the apparent maintenance of Gs/GD lineage HPAI viruses in a global avian reservoir; bidirectional virus exchange between wild and domestic birds facilitating the continued adaptation of Gs/GD HPAI viruses in wild bird hosts; the current frequency of HPAI outbreaks in wild birds globally, and particularly in Eurasia where Gs/GD HPAI viruses may now be enzootic; and ongoing dispersal of AI viruses from East Asia to North America via migratory birds, HPAI now represents an emerging disease threat to North American wildlife. This recent paradigm shift implies that management of HPAI in domestic birds alone may no longer be sufficient to eradicate HPAI viruses from a given country or region. Rather, agencies managing wild birds and their habitats may consider the development or adoption of mitigation strategies to minimize introductions to poultry, to reduce negative impacts on wild bird populations, and to diminish adverse effects to stakeholders using wildlife resources. The main objective of this review is, therefore, to provide information that will assist wildlife managers in developing mitigation strategies or approaches for dealing with outbreaks of Gs/GD HPAI in wild birds in the form of preparedness, surveillance, research, communications, and targeted management actions. Resultant outbreak response plans and actions may represent meaningful steps of wildlife managers toward the use of collaborative and multi-jurisdictional One Health approaches when it comes to the detection, investigation, and mitigation of emerging viruses at the human-domestic animal-wildlife interface.
The 2004 magnitude (M) 9.1 Sumatra-Andaman Islands earthquake in the Indian Ocean triggered the deadliest tsunami ever, killing more than 230,000 people. In response, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) established three additional Intergovernmental Coordination Groups (ICGs) for the Tsunami and Other Coastal Hazards Early Warning System: for the Caribbean and Adjacent Regions (ICG/CARIBE-EWS), for the Indian Ocean, and for the Northeastern Atlantic, Mediterranean, and Connected Seas. Along with the ICG for the Pacific Ocean, which was established in 1965, one of the goals of the new ICGs was to improve earthquake and tsunami monitoring and early warning. This need was further demonstrated by the 2011 Great East Japan (Tōhoku-oki) earthquake and tsunami, which killed more than 20,000 people, and other destructive tsunamis that occurred in the Solomon Islands, Samoa, Tonga, Chile, Indonesia, and Peru.
In response to the call to action by the UN Decade of Ocean Science for Sustainable Development (2021–2030), as well as the desired safe ocean outcome (von Hillebrandt-Andrade et al., 2021), the Intergovernmental Oceanographic Commission (IOC) of UNESCO approved the Ocean Decade Tsunami Programme in June 2021. One of its goals is to develop the capability to issue actionable alerts for tsunamis from all sources with minimum uncertainty within 10 minutes (Angove et al., 2019). While laudable, this goal presents complexities. Currently, warning depends on quick detection as well as the location and initial magnitude estimates of an earthquake that may generate a tsunami. Other factors that affect tsunamis, such as the faulting mechanism (how the faults slide past each other) and areal extent of the earthquake, currently take at least 20–30 minutes to forecast and are still subject to large uncertainties. Hence, agencies charged with tsunami early warning need to broadcast public alerts within minutes after an earthquake occurs but may struggle to meet this 10-minute goal without further technological advances, some of which are outlined in this article.
To reduce loss of life through adequate tsunami warning requires global ocean-based seismic, sea level, and geodetic initiatives to detect high-impact earthquakes and tsunamis, combined with sufficient communication and education so that people know how to respond when they receive alerts and warnings. The United Nations International Strategy for Disaster Reduction defines an early warning system as “a set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities, and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss” (UNISDR, 2012). In short, a successful early warning system requires technology coupled with human factors (Kelman and Glantz, 2014).
In this article, we explore case studies from Japan and Canada, where scientists are leading the way in incorporating ocean observing capabilities in their early warning systems. We also explore advancements and challenges in the Caribbean, an area with a complex tectonic environment that would benefit greatly from increased global ocean observing capabilities. We also explore physical and social science interventions necessary to reduce loss of life.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2021.supplement.02-27","usgsCitation":"Sumy, D.F., McBride, S., von Hillebrandt-Andrade, C., Kohler, M.D., Orcutt, J., Kodaira, S., Moran, K., McNamara, D., Hori, T., Vanacore, E., Pirenne, B., and Collins, J., 2022, Long-term ocean observing for international capacity development around tsunami early warning: Oceanography, v. 34, no. 4, p. 70-77, https://doi.org/10.5670/oceanog.2021.supplement.02-27.","productDescription":"8 p.","startPage":"70","endPage":"77","ipdsId":"IP-135818","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":449228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2021.supplement.02-27","text":"Publisher Index Page"},{"id":398108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, 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jcollins@whoi.edu","contributorId":177449,"corporation":false,"usgs":false,"family":"Collins","given":"John A.","email":"jcollins@whoi.edu","affiliations":[{"id":6706,"text":"Woods Hole Oceanographic Institution,","active":true,"usgs":false}],"preferred":false,"id":839640,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70227460,"text":"70227460 - 2022 - gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring","interactions":[],"lastModifiedDate":"2022-01-18T13:22:54.043797","indexId":"70227460","displayToPublicDate":"2022-01-07T07:18:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring","docAbstract":"gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.
Widespread amphibian declines were well documented at the end of the 20th century, raising concerns about the need to identify individual and interactive contributors to this global trend. At the same time, there was growing interest in the use of amphibians as ecological indicators. In the United States, wetland and amphibian monitoring programs were launched in some national parks as a necessary first step to evaluating the status and trends of amphibian populations within some of North America’s most protected areas. In Grand Teton and Yellowstone national parks, a multi-species amphibian monitoring program was launched by many of the authors in 2006 and continues to this day. This Viewpoint Article serves as a self-evaluation of our journey from conception through implementation of an ongoing, long-term monitoring program. This self-evaluation should provide a framework and guidance for other monitoring programs. We address whether we are fulfilling the program’s main objective of describing status and trends of the four amphibian species, discuss how a one-size-fits-all monitoring approach does not serve all species equally, and describe opportunities to bolster our core work using emerging statistical approaches and thoughtful integration of remote sensing and molecular tools. We also describe how the data generated over the program’s first 15 years have been useful beyond our initial goal of characterizing status and trend. Notably, our integration of climate datasets has allowed us to describe wetland and species-specific amphibian responses to variations in climate drivers. Documenting climate links to amphibian occurrence and their primary habitats has allowed us to identify which species, habitat types, and subregions within this large, protected landscape are most vulnerable to anticipated climate change. Recognizing that tools and threats change over time, it will be important to adapt our original monitoring design to maximize opportunities and use of resulting information. Maintaining engagement by multiple stakeholders and expanding our funding portfolio will also be necessary to sustain our program into the future. Finally, collaboration has become standard for long-term, cross-jurisdictional, landscape-scale monitoring. We argue that collaborative monitoring facilitates resource sharing, leveraging of limited funds, completion of work, and mutual learning. Such collaboration also increases the efficacy of conservation.
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.
The genus Hornibrookina consists of enigmatic calcareous nannofossils that first appeared shortly after the K-Pg mass extinction. Due to their relative paucity in most published sections, specimens of this genus have not been previously studied in detail and their paleobiogeographic preferences and evolutionary history have been poorly understood. Biostratigraphic and morphometric analyses of Hornibrookina specimens from outcrops and cores from the Atlantic Ocean, the North Sea, the Southern Ocean, the Indian Ocean, North America, South America, Africa, and New Zealand resulted in a comprehensive and detailed documentation of this group of calcareous nannofossils. Biostratigraphic ranges for each species are refined and a hypothetical evolutionary lineage for this genus is proposed. Two new species (Hornibrookina gracila and Hornibrookina indistincta), two new combinations (Hornibrookina elegans and Hornibrookina australis arca) and one new subspecies (Hornibrookina australis australis) are described. Morphometric analyses prove that Hornibrookina edwardsii and Hornibrookina teuriensis are distinctly different species with biostratigraphically useful ranges. Hornibrookina apellanizii is shown to be invalid.
","language":"English","publisher":"Micropaleontology Press","doi":"10.47894/mpal.68.1.04","usgsCitation":"Self-Trail, J., Watkins, D.K., Pospichal, J.J., and Seefelt, E., 2022, Evolution and taxonomy of the Paleogene calcareous nannofossil genus Hornibrookina: Micropaleontology, v. 68, no. 1, p. 85-113, https://doi.org/10.47894/mpal.68.1.04.","productDescription":"29 p.","startPage":"85","endPage":"113","ipdsId":"IP-126381","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":393849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":830004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watkins, David K.","contributorId":270769,"corporation":false,"usgs":false,"family":"Watkins","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":830005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pospichal, James J.","contributorId":270770,"corporation":false,"usgs":false,"family":"Pospichal","given":"James","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":830006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seefelt, Ellen 0000-0001-6822-7402 eseefelt@usgs.gov","orcid":"https://orcid.org/0000-0001-6822-7402","contributorId":2953,"corporation":false,"usgs":true,"family":"Seefelt","given":"Ellen","email":"eseefelt@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":830007,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234293,"text":"70234293 - 2022 - An assessment of uncertainties in VS profiles obtained from microtremor observations in the phased 2018 COSMOS blind trials","interactions":[],"lastModifiedDate":"2022-09-01T14:54:40.235385","indexId":"70234293","displayToPublicDate":"2022-01-01T06:17:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"An assessment of uncertainties in VS profiles obtained from microtremor observations in the phased 2018 COSMOS blind trials","docAbstract":"Site response is a critical consideration when assessing earthquake hazards. Site characterization is key to understanding site effects as influenced by seismic site conditions of the local geology. Thus, a number of geophysical site characterization methods were developed to meet the demand for accurate and cost-effective results. As a consequence, a number of studies have been administered periodically as blind trials to evaluate the state-of-practice on-site characterization. We present results from the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) blind trials, which used data recorded from surface-based microtremor array methods (MAM) at four sites where geomorphic conditions vary from deep alluvial basins to an alpine valley. Thirty-four invited analysts participated. Data were incrementally released to 17 available analysts who participated in all four phases: (1) two-station arrays, (2) sparse triangular arrays, (3) complex nested triangular or circular arrays, and (4) all available geological control site information including drill hole data. Another set of 17 analysts provided results from two sites and two phases only. Although data from one site consisted of recordings from three-component sensors, the other three sites consisted of data recorded only by vertical-component sensors. The sites cover a range of noise source distributions, ranging from one site with a highly directional microtremor wave field to others with omni-directional (azimuthally distributed) wave fields. We review results from different processing techniques (e.g., beam-forming, spatial autocorrelation, cross-correlation, or seismic interferometry) applied by the analysts and compare the effectiveness between the differing wave field distributions. We define the M index as a quality index based on estimates of the time-averaged shear-wave velocity of the upper 10 (VS10), 30 (VS30), 100 (VS100), and 300 (VS300) meters and show its usefulness in quantitative comparisons of VS profiles from multiple analysts. Our findings are expected to aid in building an evidence-based consensus on preferred cost-effective arrays and processing methodology for future studies of seismic site effects.
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2021.3114635","usgsCitation":"Zolfaghari, K., Pahlevan, N., Binding, C., Gurlin, D., Simis, S.G., Verdu, A.R., Li, L., Crawford, C., VanderWoude, A., Errera, R., Zastepa, A., and Duguay, C.R., 2022, Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment: IEEE Transactions in Geoscience and Remote Sensing, v. 60, 5515520, 20 p., https://doi.org/10.1109/TGRS.2021.3114635.","productDescription":"5515520, 20 p.","ipdsId":"IP-132686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449319,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/tgrs.2021.3114635","text":"Publisher Index Page"},{"id":390590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zolfaghari, Kiana","contributorId":267804,"corporation":false,"usgs":false,"family":"Zolfaghari","given":"Kiana","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":825333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":267805,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":825334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binding, Caren","contributorId":267806,"corporation":false,"usgs":false,"family":"Binding","given":"Caren","affiliations":[],"preferred":false,"id":825335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gurlin, Daniela","contributorId":267807,"corporation":false,"usgs":false,"family":"Gurlin","given":"Daniela","email":"","affiliations":[],"preferred":false,"id":825336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simis, Stefan G.H.","contributorId":267808,"corporation":false,"usgs":false,"family":"Simis","given":"Stefan","email":"","middleInitial":"G.H.","affiliations":[],"preferred":false,"id":825337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Verdu, Antonio Ruiz","contributorId":267809,"corporation":false,"usgs":false,"family":"Verdu","given":"Antonio","email":"","middleInitial":"Ruiz","affiliations":[],"preferred":false,"id":825338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Lin","contributorId":267810,"corporation":false,"usgs":false,"family":"Li","given":"Lin","email":"","affiliations":[],"preferred":false,"id":825339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":825340,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VanderWoude, Andrea","contributorId":267811,"corporation":false,"usgs":false,"family":"VanderWoude","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":825341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Errera, Reagan","contributorId":267812,"corporation":false,"usgs":false,"family":"Errera","given":"Reagan","email":"","affiliations":[],"preferred":false,"id":825342,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zastepa, Arthur","contributorId":267813,"corporation":false,"usgs":false,"family":"Zastepa","given":"Arthur","email":"","affiliations":[],"preferred":false,"id":825343,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duguay, Claude R.","contributorId":267814,"corporation":false,"usgs":false,"family":"Duguay","given":"Claude","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":825344,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70236051,"text":"70236051 - 2022 - Relational database for horizontal‐to‐vertical spectral ratios","interactions":[],"lastModifiedDate":"2022-08-26T12:01:07.832992","indexId":"70236051","displayToPublicDate":"2021-12-29T06:57:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Relational database for horizontal‐to‐vertical spectral ratios","docAbstract":"Frequency‐dependent horizontal‐to‐vertical spectral ratios (HVSRs) of Fourier amplitudes from three‐component recordings can provide useful information for site response modeling. However, such information is not incorporated into most ground‐motion models, including those from Next‐Generation Attenuation projects, which instead use the time‐averaged shear‐wave velocity (
The uranium (U) content, and more recently, the ratio between 238U and 235U in black shales are commonly applied as a proxy to determine redox conditions and infer organic-richness. Uranium contents typically display a linear relationship with total organic carbon (TOC) in shales. This relationship is due to the processes and mechanisms responsible for the incorporation of U into the sediment during the deposition and remineralization of organic matter. This U/TOC relationship can vary, however, and some shales display uncharacteristically low U content despite having high TOC content, while others show large enrichments of U relative to TOC. Here we examine the U to TOC ratios and U-isotope compositions of three Upper Devonian-Lower Mississippian shales: the Woodford Shale, the Cleveland Shale, and the Bakken Shale, with two study sites in Oklahoma, one site in eastern Kentucky, and three sites in eastern Montana and western North Dakota, respectively. The U/TOC ratios of each shale are distinct from one another exhibiting average ratios ranging from 3 in the Cleveland Shale, to over 10 in the Bakken Shale. The distinct geochemical composition of the three shales suggests that, although lithologically similar, each study site represents a markedly different and dynamic depositional environment. The low average U/TOC (~3) along with the relatively high δ238U values (~0.03‰) of the Cleveland Shale core suggests deposition along the basin margin under normal marine conditions with periods of reduced bottom water oxygenation, likely due to fluctuations in the location of the pycnocline. The Woodford Shale on the other hand, shows higher U/TOC ratios (~4, George core, ~9, Poe core) and δ238U (~0.02‰ average, George core, ~0.06‰ average, Poe core), which suggests an unrestricted setting with intermittent euxinic conditions. In contrast, high U/TOC ratios (2–15), and very high δ238U values (up to 0.55‰) in the Bakken Shale cores indicate intense metal draw-down into sediments under sulfidic waters. The results show that when the U/TOC ratios and U-isotopic compositions of each studied shale are compared to modern anoxic basins and upwelling areas, it allows for an enhanced understanding of the paleoenvironmental conditions such as basin restriction and redox state of waters within the Late Devonian epicontinental seas of North America.
Over 11 years of data acquired by the Diviner Lunar Radiometer Experiment instrument aboard Lunar Reconnaissance Orbiter have been compiled into a comprehensive data set of surface temperatures in the Lacus Mortis region which includes the landing ellipse of the Astrobotic Mission One lander mission. These data provide diurnal brightness temperatures at 128 pixels per degree (ppd) spatial resolution and 0.1 hr of local time resolution. From this data set, we highlight several features that display variations in radiative and thermophysical properties in the Lacus Mortis region and characterize the temperatures of the Astrobotic Mission One landing ellipse. We identify distinctly contrasting properties of materials in the walls of Bürg crater, hummocks of materials on the southeast margin of the mare basalts, and materials exposed or excavated by impacts. Additionally, we describe an exceptionally rocky fault scarp that predates the formation of Bürg crater suggesting the observed boulders are replenished on a timescale <1 Ga. Within the Astrobotic landing ellipse, temperatures are observed to range from ∼88 to ∼359 K with sunrise and sunset local times constrained to 5.8–6.3 hr and 17.8 and 18.1 hr respectively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EA001966","usgsCitation":"Williams, J., Greenhagen, B.T., Bennett, K.A., Paige, D., Kumari, N., Ahrens, C., Rubanenko, L., Powell, T., Prem, P., Blewett, D.T., Russell, P., Hayne, P.O., and Sullivan, M., 2022, Temperatures of the Lacus Mortis region of the Moon: Earth and Space Science, v. 9, no. 2, e2021EA001966, 17 p., https://doi.org/10.1029/2021EA001966.","productDescription":"e2021EA001966, 17 p.","ipdsId":"IP-130804","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":449361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021ea001966","text":"Publisher Index Page"},{"id":400575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lacus Mortis region, Moon","volume":"9","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, J.-P.","contributorId":291741,"corporation":false,"usgs":false,"family":"Williams","given":"J.-P.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":842946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greenhagen, Benjamin T","contributorId":174124,"corporation":false,"usgs":false,"family":"Greenhagen","given":"Benjamin","email":"","middleInitial":"T","affiliations":[{"id":27364,"text":"Johns Hopkins Univ Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":842947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Kristen A. 0000-0001-8105-7129","orcid":"https://orcid.org/0000-0001-8105-7129","contributorId":237068,"corporation":false,"usgs":true,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":842948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paige, David","contributorId":291742,"corporation":false,"usgs":false,"family":"Paige","given":"David","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":842949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumari, Nandita","contributorId":291743,"corporation":false,"usgs":false,"family":"Kumari","given":"Nandita","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":842950,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahrens, Caitlin","contributorId":176926,"corporation":false,"usgs":false,"family":"Ahrens","given":"Caitlin","email":"","affiliations":[],"preferred":false,"id":842951,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubanenko, Lior","contributorId":291744,"corporation":false,"usgs":false,"family":"Rubanenko","given":"Lior","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":842952,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Powell, Tyler","contributorId":291745,"corporation":false,"usgs":false,"family":"Powell","given":"Tyler","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":842953,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prem, Parvathy","contributorId":291747,"corporation":false,"usgs":false,"family":"Prem","given":"Parvathy","email":"","affiliations":[{"id":32873,"text":"Johns Hopkins University, Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":842954,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Blewett, David T.","contributorId":127835,"corporation":false,"usgs":false,"family":"Blewett","given":"David","email":"","middleInitial":"T.","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":842955,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Russell, Patrick","contributorId":206094,"corporation":false,"usgs":false,"family":"Russell","given":"Patrick","affiliations":[{"id":37243,"text":"SI","active":true,"usgs":false}],"preferred":false,"id":842956,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hayne, Paul O.","contributorId":174125,"corporation":false,"usgs":false,"family":"Hayne","given":"Paul","email":"","middleInitial":"O.","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":842957,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sullivan, Mark","contributorId":291750,"corporation":false,"usgs":false,"family":"Sullivan","given":"Mark","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":842958,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70227511,"text":"70227511 - 2022 - Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","interactions":[],"lastModifiedDate":"2022-01-20T14:20:59.532904","indexId":"70227511","displayToPublicDate":"2021-12-24T08:15:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","docAbstract":"Cyanobacteria can produce numerous secondary metabolites (cyanotoxins) with various toxicities, yet data on cyanotoxins in many lakes are limited. Moreover, little research is available on complex relations among cyanobacteria that produce toxins. Therefore, we studied cyanobacteria and 19 cyanotoxins at three sites with recurring blooms in Kabetogama Lake (USA). Seven of 19 toxins were detected in various combinations. Anabaenopeptin A and B were detected in every sample. Microcystin-YR was detected more frequently than microcystin-LR, unlike other lakes in the region. Microcystin-YR concentrations, however, generally were low; two samples exceeded drinking water guidelines and no samples exceeded recreational guidelines. Anabaenopeptins correlated with six cyanobacterial taxa, most of which lack available literature on peptide production. The potential toxin producing cyanobacteria, Microcystis, was significantly correlated to microcystin-YR. Pseudanabaena sp. and Synechococcus sp. had strong negative correlations with several toxins that may indicate competition or stress between organisms. Non-metric multidimensional scaling identified three cyanobacterial pairs that may reflect symbiotic or antagonistic relations. This study highlights interactions among cyanobacteria and multiple cyanotoxins and the methods used may be useful for uncovering additional patterns in cyanobacteria communities in other systems, leading to further understanding of how those interactions lead to toxin production.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.128142","usgsCitation":"Christensen, V., Olds, H., Norland, J.E., and Khan, E., 2022, Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake: Journal of Hazardous Materials, v. 427, 128142, 12 p., https://doi.org/10.1016/j.jhazmat.2021.128142.","productDescription":"128142, 12 p.","ipdsId":"IP-128039","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":394575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.11805725097656,\n 48.4105166936892\n ],\n [\n -92.779541015625,\n 48.4105166936892\n ],\n [\n -92.779541015625,\n 48.537977131982025\n ],\n [\n -93.11805725097656,\n 48.537977131982025\n ],\n [\n -93.11805725097656,\n 48.4105166936892\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"427","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":831206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":831207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":831208,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254832,"text":"70254832 - 2022 - Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream","interactions":[],"lastModifiedDate":"2024-06-11T14:12:25.531179","indexId":"70254832","displayToPublicDate":"2021-12-23T09:03:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream","docAbstract":"Research on fishes sometimes requires that individual fish be captured and subjected to invasive procedures multiple times over a relatively short time span. Electrofishing is one of the most common techniques used to capture fish, and it is known to cause injury to fish under certain circumstances. We evaluated the relationship of growth rates in Columbia River Redband Trout Oncorhynchus mykiss gairdneri to the number of times that they were captured via electrofishing and gastrically lavaged during the summer of 2018 in a mountainous, headwater stream. We captured fish between two and seven times over the course of 86 d using continuous (smooth) DC backpack electrofishing. We observed no relationship between the growth rate of Columbia River Redband Trout and the number of times that they were captured or gastrically lavaged. Although these findings contrast with hatchery electrofishing experiments, they may represent the greater resiliency of wild fish. It appears that researchers can use electrofishing and gastric lavage in cold waters at least once per month, and potentially up to twice per month, without greatly affecting the growth of wild Columbia River Redband Trout.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10728","usgsCitation":"Clancy, N.G., Dunnigan, J.L., and Budy, P., 2022, Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream: North American Journal of Fisheries Management, v. 42, no. 1, p. 109-114, https://doi.org/10.1002/nafm.10728.","productDescription":"6 p.","startPage":"109","endPage":"114","ipdsId":"IP-133486","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bear Creek, Libby Creek, Ramsey Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.6766783082558,\n 48.4467644853234\n ],\n [\n -115.6766783082558,\n 48.14538875631595\n ],\n [\n -115.30322838074561,\n 48.14538875631595\n ],\n [\n -115.30322838074561,\n 48.4467644853234\n ],\n [\n -115.6766783082558,\n 48.4467644853234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Clancy, Niall G.","contributorId":337769,"corporation":false,"usgs":false,"family":"Clancy","given":"Niall","email":"","middleInitial":"G.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunnigan, James L.","contributorId":337770,"corporation":false,"usgs":false,"family":"Dunnigan","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227319,"text":"70227319 - 2022 - Automated detection of clipping in broadband earthquake records","interactions":[],"lastModifiedDate":"2022-03-15T16:51:56.806273","indexId":"70227319","displayToPublicDate":"2021-12-22T07:32:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Automated detection of clipping in broadband earthquake records","docAbstract":"Because the amount of available ground‐motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground‐motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near‐real‐time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.
Distinguishing between seismic and aseismic fault slip in the geologic record is difficult, yet fundamental to estimating the seismic potential of faults and the likelihood of multi-fault ruptures. We integrated chirp sub-bottom imaging with targeted cross-fault coring and core analyses of sedimentary proxy data to characterize vertical deformation and slip behavior within an extensional fault bend along the Hayward-Rodgers Creek fault system in northern San Pablo Bay. We identified and traced four key seismic horizons (R1–R4), all younger than approximately 1400 CE, that cross the fault and extend throughout the basin. A stratigraphic age model was developed using detailed down-core radiocarbon and radioisotope dating combined with measurements of anthropogenic metal concentrations. The onset of hydraulic mining within the Sierra Nevada in 1852 CE left a clear geochemical and magnetic signature within core samples. This key time horizon was used to calculate a local reservoir correction and reduce uncertainty in radiocarbon age calibration and models. Vertical fault offset of strata younger than the most recent surface-rupturing earthquake on the Hayward fault in 1868 CE suggest near-surface vertical creep is occurring along the fault in northern San Pablo Bay at a rate of approximately 0.4 mm/yr. In addition, we present evidence of at least one and possibly two coseismic events associated with growth strata above horizons R1 and R2, with median event ages estimated to be 1400 CE and 1800 CE, respectively. The timing of both these events overlaps with paleoseismic events on adjacent fault sections, suggesting the possibility of multi-fault rupture.
The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.
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Center","active":true,"usgs":true}],"preferred":true,"id":828570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krause, Kevin P. 0000-0002-0255-7027","orcid":"https://orcid.org/0000-0002-0255-7027","contributorId":218454,"corporation":false,"usgs":true,"family":"Krause","given":"Kevin","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":828572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":828573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":828574,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true}],"preferred":true,"id":828575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828576,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238958,"text":"70238958 - 2022 - Translational science education through citizen science","interactions":[],"lastModifiedDate":"2022-12-19T14:45:01.797613","indexId":"70238958","displayToPublicDate":"2021-12-14T08:34:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Translational science education through citizen science","docAbstract":"Guided by the six elements of Translational Ecology (TE; i.e., decision-framing, collaboration, engagement, commitment, process, and communication), we showcase the first explicit example of a Translational Science Education (TSE) effort in the coastal redwood ecosystem of Humboldt County, CA. Using iNaturalist, a flexible and free citizen science/crowdsourcing app, we worked with students from grade school through college, and their teachers and community, to generate species lists for comparison among 19 school and non-profit locations spanning a range of urbanization. Importantly, this TSE effort resulted in both learning and data generation, highlighting the ability of a TSE framework to connect and benefit both students and researchers. Our data showed that, regardless of the age of the observers, holding organized BioBlitzes added substantially more species to local biodiversity lists than would have been generated without them. In support of current ecological theory, these data showed an urbanization gradient among sites, with rural sites containing fewer non-native species than urban ones. On the education side, qualitative assessments revealed students and educators remained engaged throughout the project. Future projects would also benefit by establishing quantifiable metrics for assessing student learning from project conception. Throughout the project, the fundamentals of TE were followed with repeated interactions and shared objectives developed over time within trusted community relationships. Such positive human interactions can lead new naturalists to think of themselves as champions of their local biodiversity (i.e., as land stewards). We anticipate that such newly empowered and locally expert naturalists will remain committed to land stewardship in perpetuity and that other scientists and educators are inspired to conduct similar work.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2021.800433","usgsCitation":"Young, A.M., van Mantgem, E., Garretson, A., Noel, C., and Morelli, T.L., 2022, Translational science education through citizen science: Frontiers in Environmental Science, v. 9, 800433, 15 p., https://doi.org/10.3389/fenvs.2021.800433.","productDescription":"800433, 15 p.","ipdsId":"IP-134950","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":449413,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2021.800433","text":"Publisher Index Page"},{"id":410706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Humboldt County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.9,\n 40.77\n ],\n [\n -123.9,\n 40.7\n ],\n [\n -123.84181204127015,\n 40.7\n ],\n [\n -123.84181204127015,\n 40.77\n ],\n [\n -123.9,\n 40.77\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Allison M.","contributorId":300069,"corporation":false,"usgs":false,"family":"Young","given":"Allison","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":859370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Elizabeth F.","contributorId":300070,"corporation":false,"usgs":false,"family":"van Mantgem","given":"Elizabeth F.","affiliations":[{"id":65009,"text":"Sequoia Park Zoo","active":true,"usgs":false}],"preferred":false,"id":859371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garretson, Alexis","contributorId":300071,"corporation":false,"usgs":false,"family":"Garretson","given":"Alexis","email":"","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":859372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noel, Christine","contributorId":300072,"corporation":false,"usgs":false,"family":"Noel","given":"Christine","email":"","affiliations":[{"id":65009,"text":"Sequoia Park Zoo","active":true,"usgs":false}],"preferred":false,"id":859373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":859374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226895,"text":"70226895 - 2022 - Parameterizing an aeolian erosion model for rangelands","interactions":[],"lastModifiedDate":"2021-12-20T12:28:08.433345","indexId":"70226895","displayToPublicDate":"2021-12-13T06:26:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"Parameterizing an aeolian erosion model for rangelands","docAbstract":"Aeolian processes are fundamental to arid and semi-arid ecosystems, but modeling approaches are poorly developed for assessing impacts of management and environmental change on sediment transport rates over meaningful spatial and temporal scales. For model estimates to provide value, estimates of sediment flux that encapsulate intra- and inter-annual and spatial variability are needed. Further, it is important to quantify and communicate transparent estimates of model uncertainty to users. Here, we present a wind erosion and dust emission model parameterized for rangelands using a Generalized Likelihood Uncertainty Estimation framework. Modeled horizontal sediment flux was calibrated using data from five diverse grassland and shrubland sites from the USDA National Wind Erosion Research Network. Observations of wind speed, vegetation height, length of gaps between vegetation, and percent bare ground were used as model inputs. Horizontal sediment flux estimates from 10,000 independently selected parameter sets were compared to flux observations from 44 ∼ month-long collection periods to calculate a likelihood measure for each model. Results show good agreement for individual sampling periods across sites with few observations falling outside prediction bounds and a one-to-one relationship between median predictions and observations. Additionally, combined distributions of sediment flux estimates from all sample periods for a given site closely approximated the probability of observing a given flux at that site. These results suggest AERO effectively represents temporal variability in aeolian transport rates at rangeland sites and provides robust assessments suitable for assessing land health and better predicting changes in air quality and the impacts of land management activities.
Understanding the controls on the amount and persistence of soil organic carbon (C) is essential for predicting its sensitivity to global change. The response may depend on whether C is unprotected, isolated within aggregates, or protected from decomposition by mineral associations. Here, we present a global synthesis of the relative influence of environmental factors on soil organic C partitioning among pools, abundance in each pool (mg C g−1 soil), and persistence (as approximated by radiocarbon abundance) in relatively unprotected particulate and protected mineral-bound pools. We show that C within particulate and mineral-associated pools consistently differed from one another in degree of persistence and relationship to environmental factors. Soil depth was the best predictor of C abundance and persistence, though it accounted for more variance in persistence. Persistence of all C pools decreased with increasing mean annual temperature (MAT) throughout the soil profile, whereas persistence increased with increasing wetness index (MAP/PET) in subsurface soils (30–176 cm). The relationship of C abundance (mg C g−1 soil) to climate varied among pools and with depth. Mineral-associated C in surface soils (<30 cm) increased more strongly with increasing wetness index than the free particulate C, but both pools showed attenuated responses to the wetness index at depth. Overall, these relationships suggest a strong influence of climate on soil C properties, and a potential loss of soil C from protected pools in areas with decreasing wetness. Relative persistence and abundance of C pools varied significantly among land cover types and soil parent material lithologies. This variability in each pool's relationship to environmental factors suggests that not all soil organic C is equally vulnerable to global change. Therefore, projections of future soil organic C based on patterns and responses of bulk soil organic C may be misleading.
We present a geostatistics-based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS-ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time-lapse salinity modeling through Bayesian updating.