Metals and polycyclic aromatic hydrocarbons (PAHs) are common pollutants in urban streambed sediment, yet their occurrence is highly variable and difficult to predict. To investigate sources of PAHs and metals to streambed sediment, we sampled pavement dust, soil, and streambed sediment in 10 urban watersheds in three regions of the United States and applied a fallout-radionuclide-based sediment-source analysis to quantify the pavement dust contribution to stream sediment (%dust). We also mapped the area of sealcoated pavement in each watershed (%sealed) to investigate the role of coal-tar pavement sealant (CTS) as a PAH source. Median total and carbon-normalized total PAH concentrations were significantly higher in streambed sediment in the Northeast (54.3 mg/kg and 2.71 mg/gOC) and Southeast (5.37 mg/kg and 1.36 mg/gOC), where CTS is commonly used, than in the Northwest (2.11 mg/kg and 0.071 mg/gOC), where CTS is rarely used. Generalized additive models indicated that %sealed and in some cases %dust significantly affected total PAH concentrations in streambed sediments. The %dust was a significant variable for common urban metals: Cu, Pb, and Zn. These findings advance our quantitative understanding of the role of pavement dust as a source and a vector of contaminants to urban streams.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c00414","usgsCitation":"Van Metre, P.C., Mahler, B., Qi, S.L., Gellis, A.C., Fuller, C.C., and Schmidt, T., 2022, Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams: Environmental Science and Technology, v. 56, no. 3, p. 1615-1626, https://doi.org/10.1021/acs.est.1c00414.","productDescription":"12 p.","startPage":"1615","endPage":"1626","ipdsId":"IP-121967","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9729ZGL","text":"USGS data release","linkHelpText":"Mapped sealed and unsealed pavement and concentrations of metals, polycyclic aromatic hydrocarbons, and radionuclides for soils, pavement dust, and stream sediment for 10 urban watersheds"},{"id":408026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, New York, North Carolina, Oregon, Virginia, Washington","city":"Albany, Atlanta, Charlotte, Greensboro, Portland, Rochester, Salem, Seattle, Syracuse","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44262695312501,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.27922900257082\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n 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35.97800618085566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":853983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahler, Barbara 0000-0002-9150-9552 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dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines","interactions":[],"lastModifiedDate":"2025-01-16T17:46:45.52861","indexId":"70262394","displayToPublicDate":"2022-01-18T11:43:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines","docAbstract":"Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min–1. Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49490.1","usgsCitation":"Van Eaton, A.R., Smith, C.M., Pavolonis, M.J., and Said, R., 2022, Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines: Geology, v. 50, no. 4, p. 491-495, https://doi.org/10.1130/G49490.1.","productDescription":"5 p.","startPage":"491","endPage":"495","ipdsId":"IP-128543","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/geol.s.17265287","text":"External Repository"},{"id":466649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Taal volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 120.90268362290232,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 14.102133334852596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra M","contributorId":257012,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra","email":"","middleInitial":"M","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":924037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":924038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Said, Ryan 0000-0002-8095-4204","orcid":"https://orcid.org/0000-0002-8095-4204","contributorId":257003,"corporation":false,"usgs":false,"family":"Said","given":"Ryan","email":"","affiliations":[{"id":51953,"text":"Vaisala, Inc.","active":true,"usgs":false}],"preferred":false,"id":924039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229710,"text":"70229710 - 2022 - No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","interactions":[],"lastModifiedDate":"2022-03-16T14:50:49.888833","indexId":"70229710","displayToPublicDate":"2022-01-18T09:44:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","docAbstract":"Volcanic fallout in polar ice sheets provides important opportunities to date and correlate ice-core records as well as to investigate the environmental impacts of eruptions. Only the geochemical characterization of volcanic ash (tephra) embedded in the ice strata can confirm the source of the eruption, however, and is a requisite if historical eruption ages are to be used as valid chronological checks on annual ice layer counting. Here we report the investigation of ash particles in a Greenland ice core that are associated with a volcanic sulfuric acid layer previously attributed to the 79 CE eruption of Vesuvius. Major and trace element composition of the particles indicates that the tephra does not derive from Vesuvius but most likely originates from an unidentified eruption in the Aleutian arc. Using ash dispersal modeling, we find that only an eruption large enough to include stratospheric injection is likely to account for the sizable (24–85 µm) ash particles observed in the Greenland ice at this time. Despite its likely explosivity, this event does not appear to have triggered significant climate perturbations, unlike some other large extratropical eruptions. In light of a recent re-evaluation of the Greenland ice-core chronologies, our findings further challenge the previous assignation of this volcanic event to 79 CE. We highlight the need for the revised Common Era ice-core chronology to be formally accepted by the wider ice-core and climate modeling communities in order to ensure robust age linkages to precisely dated historical and paleoclimate proxy records.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-18-45-2022","usgsCitation":"Plunkett, G., Sigl, M., Schwaiger, H., Tomlinson, E., Toohey, M., McConnell, J.R., Pilcher, J.R., Hasegawa, T., and Siebe, C., 2022, No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology: Climate of the Past, v. 18, no. 1, p. 45-65, https://doi.org/10.5194/cp-18-45-2022.","productDescription":"21 p.","startPage":"45","endPage":"65","ipdsId":"IP-129942","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-18-45-2022","text":"Publisher Index Page"},{"id":397152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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0000-0003-1014-3454","orcid":"https://orcid.org/0000-0003-1014-3454","contributorId":288522,"corporation":false,"usgs":false,"family":"Plunkett","given":"Gill","email":"","affiliations":[{"id":61787,"text":"Queen’s University Belfast","active":true,"usgs":false}],"preferred":false,"id":838047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigl, Michael 0000-0002-9028-9703","orcid":"https://orcid.org/0000-0002-9028-9703","contributorId":288523,"corporation":false,"usgs":false,"family":"Sigl","given":"Michael","email":"","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":838048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science 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0000-0001-9051-5240","orcid":"https://orcid.org/0000-0001-9051-5240","contributorId":288526,"corporation":false,"usgs":false,"family":"McConnell","given":"Joseph","email":"","middleInitial":"R.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":838052,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilcher, Jonathan R.","contributorId":288527,"corporation":false,"usgs":false,"family":"Pilcher","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[{"id":61787,"text":"Queen’s University Belfast","active":true,"usgs":false}],"preferred":false,"id":838053,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hasegawa, Takeshi","contributorId":288528,"corporation":false,"usgs":false,"family":"Hasegawa","given":"Takeshi","email":"","affiliations":[{"id":61789,"text":"Ibaraki University","active":true,"usgs":false}],"preferred":false,"id":838054,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Siebe, Claus 0000-0002-3959-9028","orcid":"https://orcid.org/0000-0002-3959-9028","contributorId":288529,"corporation":false,"usgs":false,"family":"Siebe","given":"Claus","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":838055,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227367,"text":"sir20215119 - 2022 - Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","interactions":[],"lastModifiedDate":"2026-04-02T19:50:24.033174","indexId":"sir20215119","displayToPublicDate":"2022-01-18T09:40:00","publicationYear":"2022","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":"2021-5119","displayTitle":"Characterization of Ambient Groundwater Quality Within a Statewide, Fixed-Station Monitoring Network in Pennsylvania, 2015–19","title":"Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","docAbstract":"Pennsylvania leads the Nation in the number of individuals that use groundwater for private domestic water supply; more than 3 million rural and suburban Pennsylvania residents rely on private domestic supplies for drinking water. These supplies are not regulated nor routinely monitored; thus relevant groundwater-quality information is not widely available. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP) Safe Drinking Water Bureau, established a statewide, fixed-station ambient groundwater quality network in 2015. The goals for the Pennsylvania Groundwater Monitoring Network (GWMN) include characterizing ambient groundwater quality conditions in rural areas of the State and documenting potential changes in conditions over time. Seventeen wells were selected for monitoring at 6-month intervals beginning in 2015. Since then, several wells have been added to the GWMN, bringing the total number of wells sampled in the fall of 2019 to 28. Routinely monitored constituents included physical characteristics and chemical concentrations in filtered and unfiltered samples (major and trace elements, nutrients, and organic compounds). Samples for volatile organic compounds (VOCs), radionuclides, and dissolved hydrocarbon gases were collected during the first sampling event at each well.
To offer insights on the quality of groundwater used for domestic supply in Pennsylvania, summary statistics for the 221 GWMN samples collected during 2015–19 are compared to U.S. Environmental Protection Agency (EPA) drinking-water standards, which are applicable to public water supplies. Results show that samples across the GWMN generally meet drinking-water standards for inorganic and organic constituents; however, a percentage of samples had concentrations that exceeded maximum contaminant level (MCL) thresholds for nitrate (3 percent) and secondary maximum contaminant level (SMCL) thresholds for iron (32 percent), manganese (36 percent), and aluminum (5 percent). Radon-222 activities, which were sampled only during the initial visit to a well, exceeded the lower proposed drinking water standard of 300 picocuries per liter (pCi/L) in 64 percent of wells in the GWMN; additionally, 7 percent of wells exceeded the higher proposed standard of 4,000 pCi/L. There were no exceedances for VOCs, but one well had a tribromomethane detection. Three wells had detectable concentrations of methane, with one sample exceeding the Pennsylvania action level of 7 milligrams per liter (mg/L).
The pH and dissolved oxygen concentrations varied widely across the GWMN and were correlated with dissolved metal concentrations and other chemical characteristics of groundwater samples. Considering all samples collected for the study, the pH ranged from 4.2 to 8.3; 42 percent of pH values were either above or below the SMCL range of 6.5–8.5. The highest pH values resulted from contamination of loose grout used in the construction of one well and decreased to levels consistent with other wells in the vicinity after repeated sampling rounds. Dissolved oxygen (DO), which ranged from 0 to 13.9 mg/L, influences the mobility and prevalence of constituents with variable oxidation state, including iron, manganese, and nitrogen species. Samples with acidic pH (less than 6.5) and (or) low DO had the highest concentrations of manganese and iron, whereas those with neutral to alkaline pH values had the highest concentrations of calcium, magnesium, sodium, and other major ions. Analysis of major ions indicates that calcium/bicarbonate water types are the most common, with a few characterized as calcium/chloride or sodium/chloride, and most others as mixed water types including calcium-magnesium/bicarbonate, sodium-magnesium/bicarbonate, and sodium/bicarbonate-chloride.
Nonparametric statistical methods were used to evaluate the data for spatial and temporal trends. A principal components analysis (PCA) model developed with ranked data values for the entire network resulted in three components, (1) dissolved solids, (2) redox, and (3) sodium-chloride, which explained 74.5 percent of variance in the dataset. On the basis of individual contributions to the PCA, certain wells were identified through hierarchical cluster analysis that shared relevant water-quality characteristics. The spatial distribution of sampling locations and the temporal trends of constituent concentrations indicate that hydrogeologic setting and topographic position as defined in the PCA model are important factors affecting the spatial and temporal patterns of groundwater quality in the GWMN.
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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
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.
Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.
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":70227815,"text":"70227815 - 2022 - Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile","interactions":[],"lastModifiedDate":"2022-02-02T14:20:06.194879","indexId":"70227815","displayToPublicDate":"2022-01-15T13:52:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Individual heterogeneity influences the effects of translocation on urban dispersal of an invasive reptile","docAbstract":"Background
Invasive reptiles pose a serious threat to global biodiversity, but early detection of individuals in an incipient population is often hindered by their cryptic nature, sporadic movements, and variation among individuals. Little is known about the mechanisms that affect the movement of these species, which limits our understanding of their dispersal. Our aim was to determine whether translocation or small-scale landscape features affect movement patterns of brown treesnakes (Boiga irregularis), a destructive invasive predator on the island of Guam.
Methods
We conducted a field experiment to compare the movements of resident (control) snakes to those of snakes translocated from forests and urban areas into new urban habitats. We developed a Bayesian hierarchical model to analyze snake movement mechanisms and account for attributes unique to invasive reptiles by incorporating multiple behavioral states and individual heterogeneity in movement parameters.
Results
We did not observe strong differences in mechanistic movement parameters (turning angle or step length) among experimental treatment groups. We found some evidence that translocated snakes from both forests and urban areas made longer movements than resident snakes, but variation among individuals within treatment groups weakened this effect. Snakes translocated from forests moved more frequently from pavement than those translocated from urban areas. Snakes translocated from urban areas moved less frequently from buildings than resident snakes. Resident snakes had high individual heterogeneity in movement probability.
Conclusions
Our approach to modeling movement improved our understanding of invasive reptile dispersal by allowing us to examine the mechanisms that influence their movement. We also demonstrated the importance of accounting for individual heterogeneity in population-level analyses, especially when management goals involve eradication of an invasive species.
Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80–94%. Accounting for previously unmapped dams resulted in a 62–90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.113952","usgsCitation":"Buchanan, B., Sethi, S., Cuppett, S., Lung, M., Jackman, G., Zarri, L., Duvall, E., Dietrich, J., Sullivan, P., Dominitz, A., Archibald, J., Flecker, A., and Rahm, B., 2022, A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams: Journal of Environmental Management, v. 302, no. Part A, 113952, 11 p., https://doi.org/10.1016/j.jenvman.2021.113952.","productDescription":"113952, 11 p.","ipdsId":"IP-129279","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467204,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2021.113952","text":"Publisher Index Page"},{"id":465979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Foundry Brook, Lattintown Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.85258131006508,\n 41.29588488429172\n ],\n [\n -73.85258131006508,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.29588488429172\n ],\n [\n -73.85258131006508,\n 41.29588488429172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"302","issue":"Part A","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buchanan, Brian","contributorId":348048,"corporation":false,"usgs":false,"family":"Buchanan","given":"Brian","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":922901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cuppett, Scott","contributorId":348049,"corporation":false,"usgs":false,"family":"Cuppett","given":"Scott","affiliations":[{"id":56439,"text":"NY DEC","active":true,"usgs":false}],"preferred":false,"id":922902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lung, Megan","contributorId":348050,"corporation":false,"usgs":false,"family":"Lung","given":"Megan","affiliations":[{"id":56439,"text":"NY DEC","active":true,"usgs":false}],"preferred":false,"id":922903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackman, George","contributorId":348051,"corporation":false,"usgs":false,"family":"Jackman","given":"George","affiliations":[{"id":83297,"text":"Riverkeeper, Inc.","active":true,"usgs":false}],"preferred":false,"id":922904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zarri, Liam","contributorId":348052,"corporation":false,"usgs":false,"family":"Zarri","given":"Liam","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duvall, Ethan","contributorId":348053,"corporation":false,"usgs":false,"family":"Duvall","given":"Ethan","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dietrich, Jeremy","contributorId":348054,"corporation":false,"usgs":false,"family":"Dietrich","given":"Jeremy","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sullivan, Patrick","contributorId":348055,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922908,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dominitz, Alon","contributorId":348057,"corporation":false,"usgs":false,"family":"Dominitz","given":"Alon","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":922909,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Archibald, Josephine","contributorId":348060,"corporation":false,"usgs":false,"family":"Archibald","given":"Josephine","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":922910,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Flecker, Alexander","contributorId":348061,"corporation":false,"usgs":false,"family":"Flecker","given":"Alexander","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922911,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rahm, Brian","contributorId":348062,"corporation":false,"usgs":false,"family":"Rahm","given":"Brian","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922912,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70262536,"text":"70262536 - 2022 - Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations","interactions":[],"lastModifiedDate":"2025-01-22T23:17:51.695114","indexId":"70262536","displayToPublicDate":"2022-01-15T00:00:00","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":"Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations","docAbstract":"Historically, the Monongahela, Tygart, and Cheat River watersheds in West Virginia were impaired by acidification from acid mine drainage and Walleye Sander vitreus were extirpated from these watersheds by the 1940s. Walleye were reestablished after water quality improvements following passage of environmental legislation and subsequent reintroduction efforts. We compared population characteristics, with emphasis on growth, of Walleye and used modeling to predict the potential effects of harvest regulations in the Monongahela River and two main-stem reservoirs in the Cheat River and Tygart River watersheds. Statistical comparisons of von Bertalanffy growth curves and relative growth indices indicated that Walleye growth significantly differed across all water bodies. Relative growth index results suggested that Walleye growth was above average in Cheat Lake, average in the Monongahela River, and below average in Tygart Lake relative to other North American populations. Growth was negatively correlated with Walleye relative abundance and positively correlated with estimates of productivity (total phosphorus, chlorophyll a). Walleye diets significantly differed across all water bodies, with diets dominated by Yellow Perch Perca flavescens and Gizzard Shad Dorosoma cepedianum in Cheat Lake, where growth was fastest. Population modeling suggested that effects of exploitation on yield, spawning potential, and size structure were similar under regulations of no length limit and a minimum length limit (381 mm). Models suggested that removing length limits in Tygart Lake could increase angler harvest opportunities and pose minimal threat to the fishery. Models suggested that a protected slot limit could provide increased protection to the spawning potential of Cheat Lake and the Monongahela River populations. Additionally, models predicted that a protected slot limit could increase the number of large (>630-mm) Walleye in these waters. Our findings demonstrate the different characteristics that Walleye populations can develop after reestablishment based on abiotic and biotic conditions and the need for watershed-specific management.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10723","usgsCitation":"Smith, D., Hilling, C., Welsh, S.A., and Wellman Jr., D., 2022, Differences in population characteristics and modeled response to harvest regulations in reestablished Appalachian Walleye populations: North American Journal of Fisheries Management, v. 42, no. 3, p. 612-629, https://doi.org/10.1002/nafm.10723.","productDescription":"18 p.","startPage":"612","endPage":"629","ipdsId":"IP-127935","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Cheat Lake, Monongahela River, Tygart Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.43609032800657,\n 39.70971363776053\n ],\n [\n -80.43609032800657,\n 39.288105835150134\n ],\n [\n -79.48527811621443,\n 39.288105835150134\n ],\n [\n -79.48527811621443,\n 39.70971363776053\n ],\n [\n -80.43609032800657,\n 39.70971363776053\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Dustin M.","contributorId":349597,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":924503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hilling, Corbin D.","contributorId":349598,"corporation":false,"usgs":false,"family":"Hilling","given":"Corbin D.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":924504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welsh, Stuart A. 0000-0003-0362-054X","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":217037,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wellman Jr., David I.","contributorId":349599,"corporation":false,"usgs":false,"family":"Wellman Jr.","given":"David I.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":924505,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227372,"text":"ofr20211120 - 2022 - Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","interactions":[],"lastModifiedDate":"2026-03-25T17:51:49.580485","indexId":"ofr20211120","displayToPublicDate":"2022-01-14T14:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1120","displayTitle":"Implementation Plan of the National Cooperative Geologic Mapping Program Strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","title":"Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","docAbstract":"The U.S. Geological Survey (USGS) National Cooperative Geologic Mapping Program (NCGMP) has published a strategic plan entitled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge”. This plan provides the following vision, mission, and goals for the program for the years 2020–30:
To achieve the goal outlined in the strategic plan, the NCGMP has developed an Implementation Plan. This Implementation Plan will guide annual reviews of the FEDMAP component (that is, the component of the USGS NCGMP that funds geologic mapping by USGS geologists) of the NCGMP projects described in the plan and the development of the annual FEDMAP prospectus, which will ensure the application of the NCGMP strategy.
This publication is part of the Implementation Plan of the NCGMP strategy and addresses the following three major topics:
The regions used in this chapter correspond with physiographic divisions of the United States as defined by Fenneman. Physiographic divisions are delineated on the basis of topography, and to a lesser extent, the geologic structure and history. The physiographic divisions are subdivided into physiographic provinces, and the physiographic provinces are subdivided into physiographic sections. Fenneman’s physiographic divisions of the United States provide a robust and useful spatial organization for delineating large geographic regions of the United States for various scientific and industrial applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211120","usgsCitation":"Swezey, C.S., Blome, C.D., Kincare, K.A., Lundstrom, S.C., Stone, B.D., Sweetkind, D.S., Berg, R.C., Brown, S.E., and Yellich, J.A., 2022, Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces): U.S. Geological Survey Open-File Report 2021–1120, 24 p., https://doi.org/10.3133/ofr20211120.","productDescription":"iv, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128891","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501534,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112124.htm","linkFileType":{"id":5,"text":"html"}},{"id":394416,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211120/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":394244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1120/coverthb.jpg"},{"id":394245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.pdf","text":"Report","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1120"},{"id":394246,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1120/images/"},{"id":394247,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.XML"}],"country":"Canada, United States","otherGeospatial":"Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 34.016241889667015\n ],\n [\n -75.234375,\n 34.016241889667015\n ],\n [\n -75.234375,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
The U.S. Geological Survey (USGS) 21st-century science strategy 2020–30 promotes a bureau-wide strategy to develop and deliver an integrated, predictive science capability that works at the scales and timelines needed to inform societally relevant resource management and protection and public safety and environmental health decisions (U.S. Geological Survey, 2021). This is the overarching goal of the USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) vision, which consists of three components: (1) integrated data and information, (2) integrated predictive science, and (3) actionable information—all designed and delivered to respond to user needs. To launch this vision and help shape the design and implementation of integrated predictive science, the USGS Regional Offices each developed a set of use cases (hereafter Use Cases)—short descriptions of potential science applications that could clearly address high priority decision-making needs of our stakeholders and that align with an integrated science focus. Use Cases are not actionable science planning documents, nor stand-alone scholarly works, but should be considered as innovative, next-generation science ideas that can be considered as potential components of science plans still under development. The goal of Use Case development was to (1) identify and characterize existing USGS scientific capacities and expertise that can support science goals and products, (2) identify opportunities to leverage current capacities for next-generation science, and (3) foster engagement across the entire Bureau to further refine the USGS strategy for EarthMAP and integrated predictive science.
The Use Case development effort documented in this report was coordinated by the Use Case Development Team (UCDT), consisting of representatives from each region. The UCDT undertook five tasks: (1) develop a unified approach to engage bureau scientists consistently across all regions in aspirational thinking about what can be accomplished; (2) work with the regions and their Science Centers to generate an initial set of Use Cases, authored directly by scientists; (3) characterize, summarize, and document the initial set of Use Case submissions from authors to illuminate bureau-level demand for integrated science; (4) compare existing and needed capacities from the Use Case descriptions with preliminary results of the EarthMAP Capacity Assessment (Keisman and others, 2021); and (5) describe lessons learned from the Use Case development process and provide recommendations to inform future efforts to generate integrated science activities. This report outlines the approach the UCDT developed to solicit Use Cases from the regions and summarizes the high-level qualitative findings from this first-round effort.
The UCDT received 36 Use Cases from the regions and identified potential points of convergence and commonalities considered useful in making connections among the participating scientists. The Southwest (SW) Region and the Rocky Mountain (RM) Region asked scientists to give special consideration to Use Cases with applicability to the Colorado River Basin, and seven of the Use Cases specifically named that geographic area as a focus. Coastal hazards and coastal resilience were identified in Use Cases from the Alaska (AK), Northeast (NE), and Southeast (SE) Regions. Aspects of wildfire and post-wildfire response were part of Uses Cases from AK, RM, and SW Regions. The greatest convergence of Use Case themes was related to conservation of public lands and waters, which is a powerful linkage lending strength to future collaborative efforts.
The most common type of stakeholder decisions that would be informed by the Use Case science applications were related to adaptation, mitigation, and response (for example, how to increase the resilience of coastal communities to climate-related stressors and how to prevent or respond to harmful algal blooms). Other common types of decisions included water and land management decisions (including operational water management decisions such as reservoir operations and land use planning in the sagebrush biome), decisions about how to manage and conserve habitats and species, and risk management decisions (such as managing the post-wildfire flood risks). These decision types are not exclusive because many Use Cases cross categories.
Use Case authors identified existing and needed science and technology capabilities required for Use Case implementation, which were then aligned to capabilities assessed in the EarthMAP Capacity Assessment (Keisman and others, 2021). Strong alignment was found for data and information integration approaches, modeling and prediction approaches, and capabilities related to delivery of actionable information. A majority of Use Cases indicated insufficient current capacity for needed data collection methods, data integration, and modeling and prediction approaches, whereas only 25 percent indicated insufficient capacity for actionable information delivery. Overall, many Use Case capacity demand gaps could potentially be met by existing bureau-wide capacity. In addition, nearly half of the Use Cases could potentially be implemented within 3 years if funding, capabilities, and personnel impediments were removed and science priorities were realigned.
Several challenges emerged during the Use Case development process. The first challenge was developing an approach that was flexible enough to accommodate regional differences in planning and implementation, while also ensuring enough guidance to promote meaningful summary analyses. The UCDT encountered a strong demand for continuous communication and education to improve overall understanding of the integrated predictive science strategy. Another challenge was managing expectations about EarthMAP activities as a design effort that was not aligned to an immediate funding opportunity. Connecting the Use Cases to stakeholder needs without the opportunity for direct stakeholder engagement was also challenging. The last notable challenge was in obtaining consistent interpretation and characterization of the qualitative data housed in the narrative descriptions of Use Cases, written in different styles.
Overall, the 36 Use Cases can serve as components of a road map for advancing integrated monitoring and predictive science throughout the USGS by revealing opportunities to (1) encourage cross-region initiatives that address shared interests in common themes by integrating similar Use Cases and through direct involvement of stakeholders in identifying needs and designing effective responses, (2) leverage the Use Cases to target investments that are aligned with the Bureau and Department of the Interior (DOI) priorities, (3) connect Use Cases and the results of the companion EarthMAP Capacity Assessment (Keisman and others, 2021) to identify potential priorities for capacity building investments, and (4) raise awareness of common integrated and interdisciplinary science interests within and across the regions through Use Case and Capacity Assessment summary outreach activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211108","programNote":"Science Synthesis, Analysis and Research Program","usgsCitation":"Wilson, T.S., Wiltermuth, M.T., Jenni, K.E., Horton, R.J., Hunt, R.J., Williams, D.M., Nolan, V.P., Aumen, N.G., Brown, D.S., Blasch, K.W., and Murdoch, P.S., 2022, Use case development for earth monitoring, analysis, and prediction (EarthMAP)—A road map for future integrated predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021–1108, 137 p., https://doi.org/10.3133/ofr20211108.","productDescription":"vii, 132 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-129972","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":394402,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1108/covrthb.jpg"},{"id":394403,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394404,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.xml"},{"id":394405,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1108/images"}],"contact":"Director,
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Nutrient enrichment can simultaneously increase and destabilise plant biomass production, with co-limitation by multiple nutrients potentially intensifying these effects. Here, we test how factorial additions of nitrogen (N), phosphorus (P) and potassium with essential nutrients (K+) affect the stability (mean/standard deviation) of aboveground biomass in 34 grasslands over 7 years. Destabilisation with fertilisation was prevalent but was driven by single nutrients, not synergistic nutrient interactions. On average, N-based treatments increased mean biomass production by 21–51% but increased its standard deviation by 40–68% and so consistently reduced stability. Adding P increased interannual variability and reduced stability without altering mean biomass, while K+ had no general effects. Declines in stability were largest in the most nutrient-limited grasslands, or where nutrients reduced species richness or intensified species synchrony. We show that nutrients can differentially impact the stability of biomass production, with N and P in particular disproportionately increasing its interannual variability.
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.
Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.
We present a new field measurement and numerical interpretation method (combined termed ‘test’) to parameterize the diffusion of trichloroethene (TCE) and its biodegradation products (DPs) from the matrix of sedimentary rock. The method uses a dual-packer system to interrogate a low-permeability section of the rock matrix adjacent to a previously contaminated borehole and uses the borehole monitoring history to establish the pre-test condition. TCE and its DPs are removed from the groundwater between the packers at the onset of the testing. The parameters estimated by fitting a radial diffusion model to the concentration history and borehole concentration data, also termed back-diffusion, are the tortuosity factor and sorption coefficients of TCE and DPs in the rock matrix and the TCE and DP biodegradation rate coefficients in the borehole. We demonstrate the equipment design and the interpretive method using a borehole accessing the grey mudstone at a TCE contaminated site in the Newark Basin. In this test, both nonreactive (bromide) and reactive (trichlorofluoroethene) tracers are used to constrain the estimated parameters; however, the bromide tracer was not needed to estimate the parameters in this test. The parameters estimated from the field test are consistent with values measured independently in laboratory experiments using field samples of similar lithology. From the interpretation, we compute the TCE and DP concentration distributions in the rock matrix prior to the test to illustrate how the results can be used to enhance understanding of contaminant distribution in the rock matrix.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12495","usgsCitation":"Allen-King, R.M., Kiekhaefer, R.L., Goode, D.J., Hsieh, P.A., Lorah, M.M., and Imbrigiotta, T.E., 2022, A borehole test for chlorinated solvent diffusion and degradation rates in sedimentary rock: Groundwater Monitoring and Remediation, v. 42, no. 2, p. 23-34, https://doi.org/10.1111/gwmr.12495.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-122580","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394804,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99I50JE","text":"USGS data release","linkHelpText":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen-King, Richelle M. 0000-0001-5559-1213","orcid":"https://orcid.org/0000-0001-5559-1213","contributorId":272047,"corporation":false,"usgs":false,"family":"Allen-King","given":"Richelle","email":"","middleInitial":"M.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiekhaefer, Rebecca L.","contributorId":272048,"corporation":false,"usgs":false,"family":"Kiekhaefer","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":831402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227867,"text":"70227867 - 2022 - Quantifying regional effects of best management practices on nutrient losses from agricultural lands","interactions":[],"lastModifiedDate":"2022-02-01T18:12:27.328917","indexId":"70227867","displayToPublicDate":"2022-01-12T13:12:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying regional effects of best management practices on nutrient losses from agricultural lands","docAbstract":"Nitrogen (N) and phosphorus (P) losses from agricultural areas have degraded the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown, but assessments of their effectiveness at large spatial scales have lagged. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast United States to quantify potential regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. The BMPs included in the models were cover crops, no-till, and conservation tillage. Sensitivity testing for the BMPs on agricultural nutrient loads was done using simulations that varied the intensity of BMPs specified in each region. When the BMP intensity was increased 50% relative to the 2012 intensity, the predicted agricultural load of total P decreased across all regions (4% to 14%). The predicted reduction in average P yields in the Midwest, Northeast, and Southeast was 706, 544, and 26 kg km–2, respectively. Increasing BMPs by 50% decreased predicted agricultural total N loads by 3.5% in the Southeast but increased predicted N loads in the Midwest and Northeast by 4.7% and 1.8%, respectively. Model-predicted average N yields increased by 402 kg km–2 and 302 kg km–2 in the Midwest and Northeast, respectively, and decreased in the Southeast by 329 kg km–2. In model simulations, cover crops were more effective at reducing N and P loads than the tillage BMPs despite lower intensity of implementation in 2012. However, at the regional scale of this investigation, implementation of BMPs result in only moderate predicted effects on agricultural nutrient loads
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.2022.00162","usgsCitation":"Roland, V.L., Garcia, A.M., Saad, D.A., Ator, S., Robertson, D., and Schwarz, G.E., 2022, Quantifying regional effects of best management practices on nutrient losses from agricultural lands: Journal of Soil and Water Conservation, v. 77, no. 1, p. 15-29, https://doi.org/10.2489/jswc.2022.00162.","productDescription":"15 p.","startPage":"15","endPage":"29","ipdsId":"IP-119875","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":449184,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.2022.00162","text":"Publisher Index Page"},{"id":435998,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H2NDWU","text":"USGS data release","linkHelpText":"Nutrient Load Data used to Quantify Regional Effects of Agricultural Best Management Practices: An application of the 2012 SPARROW models for the Midwest, Northeast, and Southeast United States"},{"id":395226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Roland, Victor L. II 0000-0002-6260-9351 vroland@usgs.gov","orcid":"https://orcid.org/0000-0002-6260-9351","contributorId":212248,"corporation":false,"usgs":true,"family":"Roland","given":"Victor","suffix":"II","email":"vroland@usgs.gov","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saad, David A. 0000-0001-6559-6181 dasaad@usgs.gov","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":204667,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":220504,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":832445,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229185,"text":"70229185 - 2022 - The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-03-03T15:34:56.28404","indexId":"70229185","displayToPublicDate":"2022-01-12T09:27:17","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","docAbstract":"Depressional wetlands are extremely sensitive to changes in temperature and precipitation, so understanding how wetland inundation dynamics respond to changes in climate is essential for describing potential effects on wildlife breeding habitat. Millions of depressional basins make up the largest wetland complex in North America known as the Prairie Pothole Region (PPR). The wetland ecosystems that have formed in these basins provide important migratory-bird breeding habitat. The southeast portion of the U.S. PPR in Minnesota and Iowa has faced some of the greatest challenges in wetland conservation. Many existing prairie-pothole wetlands are small (<1 ha) and shallow (<2 m) and are typically not inundated with surface water year-round. Our goal with this project is to increase the efficacy of mapping tools used by management agencies to predict future changes in water levels in the PPR. We accomplish this goal by improving the link between existing data (about wetland water characteristics) and existing tools (mapping products). Our results successfully validated (2009-2021) the current mapping tool (a wetland hydrology model) used by the U.S. Fish and Wildlife Service (USFWS) to manage 22 wetlands in Minnesota. We were able to hindcast wetland water levels to 1984 and assess the accuracy of a satellite-derived surface water product and forecast water levels through 2099 using a suite of modeled climate data. This newly refined link between monitoring data and remote sensing tools will increase understanding and prediction for other wetlands beyond our study sites and through the Minnesota and Iowa portions of the PPR. Through conference presentations, publications, and development of an interactive climate change dashboard we are now working with managers to determine how we can help incorporate these predicted changes to waterfowl breeding habitat into their future management, acquisition, and restoration strategy.
Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves (Canis lupus). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2021.2512","usgsCitation":"Sells, S.N., Mitchell, M.S., Ausband, D.E., Luis, A.D., Emlen, D.J., Podruzny, K.M., and Gude, J., 2022, Economical defence of resources structures territorial space use in a cooperative carnivore: Proceedings of the Royal Society B: Biological Sciences, v. 289, no. 1966, 20212512, 10 p., https://doi.org/10.1098/rspb.2021.2512.","productDescription":"20212512, 10 p.","ipdsId":"IP-134147","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2021.2512","text":"Publisher Index Page"},{"id":429873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Seattle","active":true,"usgs":true}],"preferred":true,"id":902987,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luis, Angela D.","contributorId":33199,"corporation":false,"usgs":true,"family":"Luis","given":"Angela","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":902989,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emlen, Douglas J.","contributorId":338162,"corporation":false,"usgs":false,"family":"Emlen","given":"Douglas","email":"","middleInitial":"J.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":902990,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Podruzny, Kevin M.","contributorId":85865,"corporation":false,"usgs":true,"family":"Podruzny","given":"Kevin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902991,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":902992,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70252840,"text":"70252840 - 2022 - Assessment of native fish passage through Brandon Road Lock and Dam, Des Plaines River, Illinois, using fin ray microchemistry","interactions":[],"lastModifiedDate":"2024-04-09T12:25:01.204236","indexId":"70252840","displayToPublicDate":"2022-01-12T07:22:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of native fish passage through Brandon Road Lock and Dam, Des Plaines River, Illinois, using fin ray microchemistry","docAbstract":"This study examined evidence of native fish passage through Brandon Road Lock and Dam (BRLD) on the Des Plaines River, Illinois, in light of proposed modifications to prevent the upstream passage of invasive carps. Direct evidence of upstream passage by native fishes at BRLD is lacking and could help to inform assessment of the impacts of barrier technology installation. Fin ray microchemistry was used to assess upstream BRLD passage in the native taxa Centrarchidae, Catostomidae, Ictaluridae, and Lepisosteidae. The fin ray edge strontium : calcium ratio (Sr:Ca) of fish sampled from the Des Plaines River upstream of BRLD and in rivers downstream of BRLD was used to characterize ranges of river-specific fin ray Sr:Ca for each taxon. These were applied to Sr:Ca data along a transect from fin ray core to edge to infer the environmental history of individual fish that were captured upstream from BRLD and to estimate the proportion of fish that had passed upstream through BRLD. Depending on the taxon, 6–37% of individuals sampled upstream from BRLD exhibited fin ray Sr:Ca indicating prior residency in rivers downstream of BRLD and therefore upstream passage through BRLD. Upstream passage was indeterminate for 19–91% of individuals in each taxon due to uncertainty in environmental history inferred from fin ray Sr:Ca. These results provide the first definitive evidence of upstream native fish passage at BRLD and suggest that the installation of barrier technology could have an impact on native fish.
Tidal marsh survival in the face of sea level rise (SLR) and declining sediment supply often depends on the ability of marshes to build soil vertically. However, numerical models typically predict survival under rates of SLR that far exceed field-based measurements of vertical accretion. Here, we combine novel measurements from seven U.S. Atlantic Coast marshes and data from 70 additional marshes from around the world to illustrate that—over continental scales—70% of variability in marsh accretion rates can be explained by suspended sediment concentratin (SSC) and spring tidal range (TR). Apparent discrepancies between models and measurements can be explained by differing responses in high marshes and low marshes, the latter of which accretes faster for a given SSC and TR. Together these results help bridge the gap between models and measurements, and reinforce the paradigm that sediment supply is the key determinant of wetland vulnerability at continental scales.
The genetic composition of an individual can markedly affect its survival, reproduction, and ultimately fitness. As some wildlife populations become smaller, conserving genetic diversity will be a conservation challenge. Many imperiled species are already supported through population augmentation efforts and we often do not know if or how genetic diversity is maintained in translocated species. As a case study for understanding the maintenance of genetic diversity in augmented populations, I wanted to know if genetic diversity (i.e., observed heterozygosity) remained high in a population of gray wolves in the Rocky Mountains of the U.S. > 20 years after reintroduction. Additionally, I wanted to know if a potential mechanism for such diversity was individuals with below average genetic diversity choosing mates with above average diversity. I also asked whether there was a preference for mating with unrelated individuals. Finally, I hypothesized that mated pairs with above average heterozygosity would have increased survival of young. Ultimately, I found that females with below average heterozygosity did not choose mates with above average heterozygosity and wolves chose mates randomly with respect to genetic relatedness. Pup survival was not higher for mated pairs with above average heterozygosity in my models. The dominant variables predicting pup survival were harvest rate during their first year of life and years pairs were mated. Ultimately, genetic diversity was relatively unchanged > 20 years after reintroduction. The mechanism for maintaining such diversity does not appear related to individuals preferentially choosing more genetically diverse mates. Inbreeding avoidance, however, appears to be at least one mechanism maintaining genetic diversity in this population.
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\"}}]}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903384,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230638,"text":"70230638 - 2022 - Portable optically stimulated luminescence age map of a paleoseismic exposure","interactions":[],"lastModifiedDate":"2022-04-19T14:52:32.370176","indexId":"70230638","displayToPublicDate":"2022-01-11T09:43:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Portable optically stimulated luminescence age map of a paleoseismic exposure","docAbstract":"The quality and quantity of geochronologic data used to constrain the history of major earthquakes in a region exerts a first-order control on the accuracy of seismic hazard assessments that affect millions of people. However, evaluations of geochronological data are limited by uncertainties related to inherently complex depositional processes that may vary spatially and temporally. To improve confidence in models of earthquake timing, we use a high-density suite of radiocarbon and optically stimulated luminescence (OSL) ages with a grid of 342 portable OSL samples to explore spatiotemporal trends in geochronological data across an exemplary normal fault colluvial wedge exposure. The data reveal a two-dimensional age map of the paleoseismic exposure and demonstrate how vertical and horizontal trends in age relate to dominant sedimentary facies and soil characteristics at the site. Portable OSL data provide critical context for the interpretation of 14C and OSL ages, show that geochronologic age boundaries between pre- and post-earthquake deposits do not match stratigraphic contacts, and provide the basis for selecting alternate Bayesian models of earthquake timing. Our results demonstrate the potential to use emergent, portable OSL methods to dramatically improve paleoseismic constraints on earthquake timing.
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