The behavior of the suspension bridge in Carquinez, CA, during the Mw6.0 24 August 2014 South Napa, CA earthquake is studied. Utilizing data from an extensive array of accelerometers that recorded the earthquake-excited motions, dynamic characteristics such as modes, corresponding frequencies and damping are identified and compared with previous studies that used ambient data of the deck only plus mathematical models. Data are systematically analyzed for vertical, transverse and torsional motions of the deck, and transverse, longitudinal and torsional motions of the towers. The transverse and vertical fundamental mode frequencies of the deck are the same (0.17 Hz) due to coupling. Higher frequencies for transverse and vertical coupled modes are also the same at 0.46 Hz and 0.98 Hz. Tower translational frequencies are 0.39 Hz in the transverse direction and 0.46 Hz in the longitudinal direction, and are also coupled with those of the deck. Coupling of torsional modes of the tower and deck is also identified. A beating effect is observed, particularly for torsional motions.
Mobile organisms in marine environments are expected to modify their behavior in response to external stressors. Among environmental drivers of animal movement are long-term climatic indices influencing organism distribution and short-term meteorological events anticipated to alter acute movement behavior. However, few studies exist documenting the response of vagile species to meteorological anomalies in coastal and marine systems.
Here we examined the movements of Eastern brown pelicans (Pelecanus occidentalis carolinensis) in the South Atlantic Bight in response to the passage of three separate hurricane events in 2 years. Pelicans (n = 32) were tracked with GPS satellite transmitters from four colonies in coastal South Carolina, USA, for the entirety of at least one storm event. An Expectation Maximization binary Clustering algorithm was used to discretize pelican behavioral states, which were pooled into ‘active’ versus ‘inactive’ states. Multinomial logistic regression was used to assess behavioral state probabilities in relation to changes in barometric pressure and wind velocity.
Individual pelicans were more likely to remain inactive during tropical cyclone passage compared to baseline conditions generally, although responses varied by hurricane. When inactive, pelicans tended to seek shelter using local geomorphological features along the coastline such as barrier islands and estuarine systems.
Our telemetry data showed that large subtropical seabirds such as pelicans may mitigate risk associated with spatially-extensive meteorological events by decreasing daily movements. Sheltering may be related to changes in barometric pressure and wind velocity, and represents a strategy common to several other classes of marine vertebrate predators for increasing survival probabilities.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-019-0178-0","usgsCitation":"Wilkinson, B.P., Satge, Y.G., Lamb, J.S., and Jodice, P.G., 2019, Tropical cyclones alter short-term activity patterns of a coastal seabird: Movement Ecology, v. 7, 30, 11 p., https://doi.org/10.1186/s40462-019-0178-0.","productDescription":"30, 11 p.","ipdsId":"IP-108429","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459342,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-019-0178-0","text":"Publisher Index Page"},{"id":437290,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D5IP0G","text":"USGS data release","linkHelpText":"Movement ecology of Brown Pelican in the South Atlantic Bight, 2017-2019"},{"id":394817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.5947265625,\n 25.839449402063185\n ],\n [\n -75.6298828125,\n 25.839449402063185\n ],\n [\n -75.6298828125,\n 35.88905007936091\n ],\n [\n -84.5947265625,\n 35.88905007936091\n ],\n [\n -84.5947265625,\n 25.839449402063185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2019-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, B. P.","contributorId":272128,"corporation":false,"usgs":false,"family":"Wilkinson","given":"B.","email":"","middleInitial":"P.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":831568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Satge, Y. G.","contributorId":272129,"corporation":false,"usgs":false,"family":"Satge","given":"Y.","email":"","middleInitial":"G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":831569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, J. S.","contributorId":272130,"corporation":false,"usgs":false,"family":"Lamb","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":831570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831571,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210233,"text":"70210233 - 2019 - Relationships between soil macroinvertebrates and nonnative feral pigs (Sus scrofa) in Hawaiian tropical montane wet forests","interactions":[],"lastModifiedDate":"2025-12-29T15:05:28.007871","indexId":"70210233","displayToPublicDate":"2019-10-28T06:55:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between soil macroinvertebrates and nonnative feral pigs (Sus scrofa) in Hawaiian tropical montane wet forests","docAbstract":"Nonnative feral pigs (Sus scrofa) are recognized throughout the New World as a highly significant introduced species in terms of ecosystem alteration. Similarly, nonnative soil macroinvertebrates (e.g. earthworms, ground beetles) invade and alter the structure and function of native habitats globally. However, the relationship between feral pigs and soil macroinvertebrates remains largely unknown. This study analyzed relationships between these taxa using nine sites located inside and outside of feral pig management units representing a ~ 25 year chronosequence of removal in tropical montane wet forests in Hawai‘i. Soil macroinvertebrates were sampled from plots categorized as: actively trampled by feral pigs, actively rooted by feral pigs, feral pigs present with no signs of recent activity, or feral pigs removed over time. In total, we found 13 families of primarily nonnative soil macroinvertebrates. Plots with active trampling correlated with lower total macroinvertebrate abundance, biomass, and family richness. Plots with active rooting were correlated with higher abundance of nonnative earthworms (Lumbricidae and Megascolicidae) and ground beetles (Carabidae). The abundance, biomass, and biodiversity of macroinvertebrates did not vary with time since feral pig removal. Collectively, these results indicate: (1) trampling by feral pigs negatively influences soil macroinvertebrates; (2) feral pigs either modify habitats while rooting thereby facilitating earthworm and ground beetle habitat use or selectively seek out target prey species of soil macroinvertebrates; and (3) removal of feral pigs has minimal impacts on soil macroinvertebrates over time. These results are important globally due to the broadly overlapping ranges of S. scrofa and nonnative macroinvertebrates.","language":"English","publisher":"Springer","doi":"10.1007/s10530-019-02117-3","usgsCitation":"Wehr, N., Litton, C.M., Lincoln, N.K., and Hess, S.C., 2019, Relationships between soil macroinvertebrates and nonnative feral pigs (Sus scrofa) in Hawaiian tropical montane wet forests: Biological Invasions, v. 22, p. 577-586, https://doi.org/10.1007/s10530-019-02117-3.","productDescription":"10 p.","startPage":"577","endPage":"586","ipdsId":"IP-099387","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":375010,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"22","noUsgsAuthors":false,"publicationDate":"2019-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wehr, Nathaniel H. ","contributorId":205455,"corporation":false,"usgs":false,"family":"Wehr","given":"Nathaniel H. ","affiliations":[{"id":33542,"text":"Department of Natural Resources and Environmental Management, University of Hawai‘i at Mānoa, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":false,"id":789690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Litton, Creighton M 0000-0001-5521-1188","orcid":"https://orcid.org/0000-0001-5521-1188","contributorId":224834,"corporation":false,"usgs":false,"family":"Litton","given":"Creighton","middleInitial":"M","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":789691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lincoln, Noa K","contributorId":224835,"corporation":false,"usgs":false,"family":"Lincoln","given":"Noa","email":"","middleInitial":"K","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":789692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hess, Steve C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":150366,"corporation":false,"usgs":true,"family":"Hess","given":"Steve","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":789693,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249711,"text":"70249711 - 2019 - Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study","interactions":[],"lastModifiedDate":"2023-10-25T11:47:05.759399","indexId":"70249711","displayToPublicDate":"2019-10-25T06:43:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study","docAbstract":"Unregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R2 < 0.33), so multiple random forest classification models were also trained and tested. The final classification approach predicted <1 mg/L, 1–5 mg/L, and ≥5 mg/L using a random forest model with 58 variables and maximizing the Cohen's kappa statistic. The final model had an overall accuracy of 0.75 and high specificity for the higher two categories and high sensitivity for the lowest category. The results will be used for the categorical prediction of private well nitrate for AHS cohort participants that reside in North Carolina.
High-resolution, terrestrial laser scanning, also known as ground-based lidar (light detection and ranging), was used to quantify the volume of mercury-contaminated sediment eroded from an outcrop of historical placer-mining debris at the confluence of Humbug Creek and the South Yuba River in the Sierra Nevada foothills, about 17 kilometers northeast of Grass Valley, California, and delivered to a zone below an observed flood stage of the South Yuba River. Substantial quantities of mercury were used and lost to the environment from historical placer gold mining activities on the western slope of the Sierra Nevada, California, and recent studies have documented continued persistence of mercury and methylmercury concentrations in water, sediment, fish, and predatory invertebrates in the Yuba River drainage basin in relation to suspected mercury sources. To identify areas that have high levels of mercury contamination as possible remediation targets in the Yuba River drainage basin and other areas in the Sierra Nevada, the U.S. Geological Survey worked in cooperation with the Bureau of Land Management on this and other detailed studies. Malakoff Diggings, one of the largest hydraulic gold mines in the Sierra Nevada, is 3.5 kilometers north of the study site in the Humbug Creek subbasin.
Terrestrial laser scanning was used to produce centimeter-scale, three-dimensional maps of the complex outcrop surface, which was composed of an upper erosional area (cliff and over-steepened slope) and a lower depositional area (colluvial slope). The outcrop could not be mapped non-destructively or in sufficient detail by traditional surveying techniques. The study site, which was approximately 70 meters long, 30 meters wide and 20 meters high, was surveyed four times in 2 years (December 15, 2011; October 25, 2012; January 4, 2013; and November 22, 2013) to determine volumetric differences in the upper erosional and lower depositional areas between surveys. Measured changes in volume for the upper erosional area and lower depositional area were multiplied by the corresponding sediment density so that a mass-balance relationship, between the eroded and deposited sediment during each period, could be used to estimate the amount of mercury-contaminated sediment that was transported to below the base of the colluvial slope, where it could be mobilized by the South Yuba River during a flood having a 5-to-10-year recurrence interval. On December 2, 2012, a flood of this estimated magnitude reached the base of the colluvial slope.
Between the first and second surveys (December 15, 2011–October 25, 2012), an estimated mass of 18±9.2 kilograms of sediment was transported from steeper slopes to the gently sloping river bank below the base of the colluvial slope. Between the second and third surveys (October 25, 2012–January 4, 2013), an atmospheric river caused heavy precipitation at the study site during late November and early December 2012. This short-duration, high-intensity rain resulted in a large amount of erosion and deposition at the study site and also caused high streamflow (flood stage) in the South Yuba River. From October 2012 to January 2013, 51±31 kilograms of sediment was transported to below the base of the colluvial slope, that is, below the high-water mark of December 2, 2012. Between the third and fourth surveys (January 4, 2013–November 22, 2013), an additional 10±26 kilograms of sediment was transported to below the base of the colluvial slope. During the 24 months of the study, the total mass of sediment delivered below the base of the colluvial slope and the high-water mark of December 2, 2012, was 79±66 kilograms.
In any given year there is a 10–20-percent chance (5-to-10-year recurrence interval) of a flood equal to or greater than that of the December 2, 2012, flood, which could transport mercury-contaminated sediment at the study site into the South Yuba River. Hydraulically modeled estimates of the South Yuba River stage during floods having a 50- and 100-year recurrence interval (2- and 1-percent annual exceedance probability, respectively) indicated that resulting river stages could be 2.2–3.0 meters above the base of the colluvial slope, or 2.2–3.0 meters above the high-water mark of December 2, 2012. Such high river stages would be likely to inundate the lower half of the colluvial slope and mobilize a substantial volume of mercury-contaminated sediment to downstream areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195104","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Howle, J.F., Alpers, C.N., Kitchen, J., Bawden, G.W., and Bond, S., 2019, Quantifying the eroded and deposited mass of mercury-contaminated sediment by using terrestrial laser scanning at the confluence of Humbug Creek and the South Yuba River, Nevada County, California, 2011–13: U.S. Geological Survey Scientific Investigations Report 2019– 5104, 30 p., https://doi.org/10.3133/sir20195104.\n","productDescription":"Report: viii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-078829","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":368585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5104/sir20195104.pdf","text":"Report","size":"6.5 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California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Automated data-processing methods allow hydrologists to efficiently incorporate digital well-record datasets into the construction of hydrostratigraphic frameworks for groundwater-flow models. The method selected to construct the hydrostratigraphic framework can affect the extent of geologic heterogeneity that can be included in the model. The detail generated from a hydrostratigraphic framework can affect groundwater simulation results. The effects of detail on model accuracy, groundwater-flow simulations, and particle-tracking simulations are described in this study. This report compares differences in hydrostratigraphic frameworks and results of groundwater models using (1) a method that incorporates more hydrologic judgment at the expense of using limited lithologic data and (2) a method that is more automated and uses all available lithologic data. The study additionally evaluates the effect of model discretization and inclusion of more (or less) geologic detail on simulation results.
Two methods were used to create hydrostratigraphic frameworks of glacial deposits in the St. Joseph River Basin. One method, referred to as the subjective method, manually identifies stratigraphic boundaries using a sample of well logs from State databases and uses two-dimensional kriging to create three model layers of the study area. Indicator kriging is used to define aquifer extent in each layer. The second method, referred to as the objective method, uses three-dimensional kriging to automatically create a detailed heterogeneous model of the study area using all wells logs from the State database. The objective method increases detail in the vertical by greatly increasing the number of computer groundwater model layers from 3 to 30. In Elkhart County, Indiana, a previously published model represents the product of the subjective method, and a newly calibrated model of the same area represents the product of the objective method.
An automated calibration procedure was used with the objective model (derived from the objective method) for Elkhart County. The two most-sensitive parameters for the Elkhart County objective model are horizontal hydraulic conductivity of the sand and the combined sand and gravel/gravel deposits. Vertical hydraulic conductivity of the fine-grained and intermediate-sized deposits could not be estimated, possibly indicating major flow paths are along a continuously connected series of sand and gravel deposits and not through a confining layer.
The statistics measuring model calibration accuracy for the objective model were slightly better than statistics for the subjective model (model derived from the subjective method) of Elkhart County, but the hydraulic conductivities and flow rates for the two models were different. The mean absolute errors between simulated and measured groundwater levels are 2.04 and 2.16 feet for the objective and subjective models, respectively. Simulated seepage losses from and groundwater discharges to measured stream reaches in the objective model were evenly balanced in terms of over and under simulations of measured values; the subjective model tended to overpredict measured groundwater discharge to streams. The overprediction may be related to the 58 percent greater total inflow and outflow through the subjective model. The greater flow rate through the subjective model results from higher horizontal hydraulic conductivities in the subjective model than in the objective model. Horizontal hydraulic conductivity ranged from 23.9 to 111 feet per day in the objective model and generally ranged from 170 to 370 feet per day in the subjective model. The improvement in calibration statistics for the objective model relative to the subjective model may be from increased detail in how the objective model represents the distribution of fine- and coarse-grained deposits. The improvement also could be associated with the difference in methods used to represent the continuity of the confining unit.
The effect of differences in horizontal hydraulic conductivity distributions between the two models for Elkhart County is evident in the groundwater-flow paths simulated by the objective and subjective models. At a withdrawal well location, the flow lines produced by the objective model indicate a wider contributing area than that for the subjective model. The discontinuous confining unit represented in the objective model provided the opportunity for groundwater flow to split into an upper and lower path. The split in flow simulated by the objective model at one location was independently supported by bromide concentrations in groundwater; the subjective model did not duplicate the split in flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195088","collaboration":"U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Arihood, L.D., Lampe, D.C., Bayless, E.R., and Brown, S.E., 2019, Comparison of groundwater-model construction methods, representations of glacial geology, model designs, and groundwater-model flow simulations within Elkhart County, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5088, 44 p., https://doi.org/10.3133/sir20195088.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-065522 ","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5088/sir20195088.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5088"},{"id":368475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QN65RW","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"MODFLOW-2000 model used to illustrate the differences in flow paths and travel times when three-dimensional kriging is used to estimate the hydraulic conductivity distribution as compared to manual determinations of hydraulic conductivity distribution"},{"id":368473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5088/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Elkhart County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.7874,41.7615],[-85.7591,41.7613],[-85.6606,41.7608],[-85.6589,41.699],[-85.6575,41.6122],[-85.6554,41.5251],[-85.6542,41.4733],[-85.6552,41.4384],[-85.7704,41.4377],[-85.8874,41.4379],[-86.0008,41.4375],[-86.059,41.4367],[-86.0594,41.4644],[-86.0593,41.474],[-86.0593,41.479],[-86.0592,41.4935],[-86.0598,41.4999],[-86.0624,41.7619],[-85.932,41.7623],[-85.7874,41.7615]]]},\"properties\":{\"name\":\"Elkhart\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996
The Adirondack Park in New York State contains a unique and limited distribution of boreal ecosystem types, providing habitat for a number of birds at the southern edge of their range. Species are projected to shift poleward in a warming climate, and the limited boreal forest of the Adirondacks is expected to undergo significant change in response to rising temperatures and changing precipitation patterns. Here we expand upon a previous analysis to examine changes in occupancy patterns for eight species of boreal birds over a decade (2007–2016), and we assess the relative contribution of climate and non-climate drivers in determining colonization and extinction rates. Our analysis identifies patterns of declining occupancy for six of eight species, including some declines which appear to have become more pronounced since a prior analysis. Although non-climate drivers such as wetland area, connectivity, and human footprint continue to influence colonization and extinction rates, we find that for most species, occupancy patterns are best described by climate drivers. We modeled both average and annual temperature and precipitation characteristics and find stronger support for species’ responses to average climate conditions, rather than interannual climate variability. In general, boreal birds appear most likely to colonize sites that have lower levels of precipitation and a high degree of connectivity, and they tend to persist in sites that are warmer in the breeding season and have low and less variable precipitation in the winter. It is likely that these responses reflect interactions between broader habitat conditions and temperature and precipitation variables. Indirect climate influences as mediated through altered species interactions may also be important in this context. Given climate change predictions for both temperature and precipitation, it is likely that habitat structural changes over the long term may alter these relationships in the future.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0224308","usgsCitation":"Glennon, M., Langdon, S., Rubenstein, M.A., and Cross, M.S., 2019, Relative contribution of climate and non-climate drivers in determining dynamic rates ofboreal birds at the edge of their range: PLoS ONE, v. 14, no. 10, e0224308, 19 p., https://doi.org/10.1371/journal.pone.0224308.","productDescription":"e0224308, 19 p.","ipdsId":"IP-106475","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":459378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0224308","text":"Publisher Index Page"},{"id":379989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.223388671875,\n 42.924251753870685\n ],\n [\n -73.201904296875,\n 42.924251753870685\n ],\n [\n -73.201904296875,\n 44.941473354802504\n ],\n [\n -75.223388671875,\n 44.941473354802504\n ],\n [\n -75.223388671875,\n 42.924251753870685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"10","noUsgsAuthors":false,"publicationDate":"2019-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Glennon, Michale 0000-0002-7298-0728","orcid":"https://orcid.org/0000-0002-7298-0728","contributorId":218721,"corporation":false,"usgs":false,"family":"Glennon","given":"Michale","email":"","affiliations":[{"id":39895,"text":"Paul Smith's College","active":true,"usgs":false}],"preferred":false,"id":803567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langdon, Stephen 0000-0003-0490-021X","orcid":"https://orcid.org/0000-0003-0490-021X","contributorId":218722,"corporation":false,"usgs":false,"family":"Langdon","given":"Stephen","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":803568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubenstein, Madeleine A. 0000-0001-8569-781X mrubenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-8569-781X","contributorId":203206,"corporation":false,"usgs":true,"family":"Rubenstein","given":"Madeleine","email":"mrubenstein@usgs.gov","middleInitial":"A.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":803569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cross, Molly 0000-0002-4238-9208","orcid":"https://orcid.org/0000-0002-4238-9208","contributorId":149216,"corporation":false,"usgs":false,"family":"Cross","given":"Molly","affiliations":[{"id":17674,"text":"Wildlife Conservation Society, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":803570,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206287,"text":"70206287 - 2019 - Climatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs","interactions":[],"lastModifiedDate":"2019-12-03T09:58:55","indexId":"70206287","displayToPublicDate":"2019-10-24T13:14:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Climatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs","docAbstract":"Foundation plant species play a critical role in coastal wetlands, often modifying abiotic conditions that are too stressful for most organisms and providing the primary habitat features that support entire ecological communities. Here, we consider the influence of climatic drivers on the distribution of foundation plant species within coastal wetlands of the conterminous USA. Using region-level syntheses, we identified 24 dominant foundation plant species within 12 biogeographic regions, and we categorized species and biogeographic regions into four groups: graminoids, mangroves, succulents, and unvegetated. Literature searches were used to characterize the level of research directed at each of the 24 species. Most coastal wetlands research has been focused on a subset of foundation species, with about 45% of publications directed at just one grass species—Spartina alterniflora. An additional 14 and 8% have been directed, respectively, at two mangrove species—Rhizophora mangle and Avicennia germinans. At the national scale, winter temperature extremes govern the distribution of mangrove forests relative to salt marsh graminoids, and arid conditions can produce hypersaline conditions that increase the dominance of succulent plants, algal mats, and unvegetated tidal flats (i.e., salt flats, salt pans) relative to graminoid and mangrove plants. Collectively, our analyses illustrate the diversity of foundation plant species in the conterminous USA and begin to elucidate the influence of climatic drivers on their distribution. However, our results also highlight critical knowledge gaps and identify emerging research needs for assessing climate change impacts. Given the importance of plant-mediated processes in coastal wetland ecosystems, there is a pressing need in many biogeographic regions for additional species- and functional group-specific research that can be used to better anticipate coastal wetland responses to rising sea levels and changing temperature and precipitation regimes.","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00640-z","usgsCitation":"Osland, M., Grace, J., Guntenspergen, G., Thorne, K., Carr, J., and Feher, L., 2019, Climatic controls on the distribution of foundation plant species in coastal wetlands of the conterminous United States: Knowledge gaps and emerging research needs: Estuaries and Coasts, v. 42, no. 8, p. 1991-2003, https://doi.org/10.1007/s12237-019-00640-z.","productDescription":"13 p.","startPage":"1991","endPage":"2003","ipdsId":"IP-104790","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":368711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":220094,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":220095,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":220096,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":774085,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen","contributorId":220097,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":774086,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carr, Joel 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":220098,"corporation":false,"usgs":true,"family":"Carr","given":"Joel","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":774087,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":220099,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774088,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206528,"text":"70206528 - 2019 - Dissolved organic carbon turnover in permafrost-influenced watersheds of interior Alaska: Molecular insights and the priming effect","interactions":[],"lastModifiedDate":"2019-11-08T10:50:26","indexId":"70206528","displayToPublicDate":"2019-10-24T10:45:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved organic carbon turnover in permafrost-influenced watersheds of interior Alaska: Molecular insights and the priming effect","docAbstract":"Increased permafrost thaw due to climate change in northern high-latitudes has prompted concern over impacts on soil and stream biogeochemistry that affect the fate of dissolved organic carbon (DOC). Few studies to-date have examined the link between molecular composition and biolability of dissolved organic matter (DOM) mobilized from different soil horizons despite its importance in understanding carbon turnover in aquatic systems. Additionally, the effect of mixed DOM sources on microbial metabolism (e.g., priming) is not well understood. No studies to-date have addressed potential priming effects in northern high-latitude or permafrost-influenced aquatic ecosystems, yet these ecosystems may be hot spots of priming where biolabile, ancient permafrost DOC mixes with relatively stable, modern stream DOC. To assess biodegradability and priming of DOC in permafrost-influenced streams, we conducted 28 day bioincubation experiments utilizing a suite of stream samples and leachates of fresh vegetation and different soil horizons, including permafrost, from Interior Alaska. The molecular composition of unamended DOM samples at initial and final time points was determined by ultrahigh resolution mass spectrometry. Initial molecular composition was correlated to DOC biodegradability, particularly the contribution of energy-rich aliphatic compounds, and stream microbial communities utilized 50–56% of aliphatics in permafrost-derived DOM within 28 days. Biodegradability of DOC followed a continuum from relatively stable stream DOC to relatively biolabile DOC derived from permafrost, active layer organic soil, and vegetation leachates. Microbial utilization of DOC was ∼3–11% for stream bioincubations and ranged from 9% (active layer mineral soil-derived) to 66% (vegetation-derived) for leachate bioincubations. To investigate the presence or absence of a priming effect, bioincubation experiments included treatments amended with 1% relative carbon concentrations of simple, biolabile organic carbon substrates (i.e., primers). The amount of DOC consumed in primed treatments was not significantly different from the control in any of the bioincubation experiments after 28 days, making it apparent that the addition of biolabile permafrost-derived DOC to aquatic ecosystems will likely not enhance the biodegradation of relatively modern, stable DOC sources. Thus, future projections of carbon turnover in northern high-latitude region streams may not have to account for a priming effect.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00275","usgsCitation":"Textor, S.R., Wickland, K.P., Podgorski, D.C., Johnston, S.E., and Spencer, R., 2019, Dissolved organic carbon turnover in permafrost-influenced watersheds of interior Alaska: Molecular insights and the priming effect: Frontiers in Earth Science, v. 7, https://doi.org/10.3389/feart.2019.00275.","productDescription":"275, 17 p.","startPage":"17 pp","ipdsId":"IP-113156","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459381,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00275","text":"Publisher Index Page"},{"id":369090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.786865234375,\n 64.55316108653571\n ],\n [\n -148.798828125,\n 64.55316108653571\n ],\n [\n -148.798828125,\n 66.07600210896848\n ],\n [\n -152.786865234375,\n 66.07600210896848\n ],\n [\n -152.786865234375,\n 64.55316108653571\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Textor, Sadie R.","contributorId":220386,"corporation":false,"usgs":false,"family":"Textor","given":"Sadie","email":"","middleInitial":"R.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":774882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":774881,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":774883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":774884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, Robert G.M.","contributorId":173304,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G.M.","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":774885,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227944,"text":"70227944 - 2019 - Confluences function as ecological hotspots: Geomorphic and regional drivers can help identify patterns of fish distribution within a seascape","interactions":[],"lastModifiedDate":"2022-02-02T16:42:30.992918","indexId":"70227944","displayToPublicDate":"2019-10-24T10:36:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Confluences function as ecological hotspots: Geomorphic and regional drivers can help identify patterns of fish distribution within a seascape","docAbstract":"Quantifying heterogeneity in animal distributions through space and time is a precursor to addressing many important research and management issues. Obtaining these distributional data is especially difficult for mobile organisms that use broader geographic extents. Here, we asked if the merger between 2 research directions—(1) quantifying spatial linkages between fish and geomorphic features (e.g. confluences) and (2) analyzing larger-scale, multi-metric organismal patterns—can provide a broader geographic context for ecological issues that depend on understanding dynamic fish distribution. To address these objectives, we collected data from 59 tagged striped bass Morone saxatilis that were detected by a 26 acoustic receiver array deployed within Plum Island Estuary, MA, USA. We examined these telemetry data using generalized linear mixed models and chi-squared, cluster, and network analyses. Geomorphic site types informed the estuary-wide distribution of striped bass in that tagged fish spent the most time at confluence junctions; however, they did not spend the same amount of time at all junctions. Relative to integrating multiple metrics, number of tagged fish, residence time, and number of movements were not the same across all receivers. When all 3 metrics were considered together, 4 distinct clusters of distributional patterns emerged. Network analyses connected geomorphology and multi-metric seascape patterns. Confluence junctions in the Rowley and Middle regions were the most connected (high centrality) and most used sites (high residence time). Although confluence junctions function as ecological hotspots, researchers and managers will benefit from interpreting geomorphology within a larger geographic context.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13088","usgsCitation":"Taylor, R., Mather, M.E., Smith, J., and Gerber, K., 2019, Confluences function as ecological hotspots: Geomorphic and regional drivers can help identify patterns of fish distribution within a seascape: Marine Ecology Progress Series, v. 629, p. 133-148, https://doi.org/10.3354/meps13088.","productDescription":"16 p.","startPage":"133","endPage":"148","ipdsId":"IP-095357","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467315,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/65379","text":"External Repository"},{"id":395278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.87520599365234,\n 42.69934284303157\n ],\n [\n -70.77220916748047,\n 42.69934284303157\n ],\n [\n -70.77220916748047,\n 42.8\n ],\n [\n -70.87520599365234,\n 42.8\n ],\n [\n -70.87520599365234,\n 42.69934284303157\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"629","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryland","contributorId":273166,"corporation":false,"usgs":false,"family":"Taylor","given":"Ryland","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph","contributorId":273167,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":832650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerber, Kayla","contributorId":273168,"corporation":false,"usgs":false,"family":"Gerber","given":"Kayla","affiliations":[{"id":56437,"text":"KY wr","active":true,"usgs":false}],"preferred":false,"id":832651,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204737,"text":"sir20195073 - 2019 - Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management","interactions":[],"lastModifiedDate":"2019-10-24T11:23:12","indexId":"sir20195073","displayToPublicDate":"2019-10-24T10:15:00","publicationYear":"2019","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":"2019-5073","displayTitle":"Sediment Classification and the Characterization, Identification, and Mapping of Geologic Substrates for the Glaciated Gulf of Maine Seabed and Other Terrains, Providing a Physical Framework for Ecological Research and Seabed Management","title":"Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management","docAbstract":"A geologic substrate is a surface (or volume) of sediment or rock where physical, chemical, and biological processes occur, such as the movement and deposition of sediment, the formation of bedforms, and the attachment, burrowing, feeding, reproduction, and sheltering of organisms. Seabed mapping surveys in the Stellwagen Bank region off Boston, Massachusetts, from 1993 to 2004 have led to the development of a methodology for characterizing, identifying, and mapping geologic substrates. The resulting high-resolution interpretive maps (1:25,000) show the distribution of substrates in a glaciated terrain of banks and basins in water depths of 30 to 185 meters. Data sources used to characterize substrates are multibeam sonar bathymetric and backscatter imagery to document seabed topography and patterns of sediment and rock distribution, grain-size analyses of sediment samples to determine substrate composition, and video and photographic imagery of the seabed to aid in the interpretation of multibeam sonar imagery and to provide information on substrate layering and mobility, seabed structures, and sediments and nonsediment materials that cannot be physically sampled.
Sediment composition is a major property of many seabed substrates. Sediment grains belong to a continuum of grain-diameter sizes previously classified into grades (for example, fine sand, medium sand) and into aggregates (mud, sand, gravel). The definition of grade and aggregate boundaries in a classification is arbitrary, and a useful classification is limited to as few classes as are needed to effectively organize and apply information. For the purpose of mapping substrates, sediment grades and aggregates were simplified and re-classified into eight composite grades based on grain-size content, mode of transport, and ecological role. Five composite grades are identified using grain-size analysis and three are identified using video and photographic imagery of the seabed.
Naturally occurring sediments contain various amounts of the aggregates mud, sand, and gravel. The separation of naturally occurring sediments into sediment classes, based on grain-size analysis, requires that limits be set on the amount of mud, sand, and gravel each class contains. Fifteen previously identified basic sediment classes provided interpretive information on sediment transport by emphasizing gravel content (a low 0.01-weight-percent threshold) and on winnowing processes based on the sand-to-mud ratio. The present study recognizes 20 basic sediment classes that are combinations of aggregates in which the lower limits for recognition of mud and sand are 10 weight percent and of gravel, 25 weight percent. These sediment classes can be made more specific by listing their content of the composite grades fine-grained sand (3 and 4 phi), which is transported in suspension, and coarse-grained sand (0, 1, and 2 phi), which is transported as bedload. Additional sediment classes and nonsediment classes that cannot be sampled are recognized on the basis of visual analysis of seabed video and photographic imagery and include pebble, cobble, and boulder gravel, rock outcrops, and shell beds, among others.
Substrates are not classified because their properties are too varied for a classification to be concise and useful. Rather, substrates are characterized and identified by sediment grain-size composition (the sediment class); the distribution, in millimeters, of grain diameters in the sediment; the presence of nonsediments (for example, rock outcrops); substrate mobility based on the presence of sediment ripples; substrate layering (for example, a partial veneer of sand on gravel); and seabed structures. These properties have interpretive value by providing information about sedimentary processes acting on a substrate and about its ecological function. A geologic substrate, when it is associated with one or more species, is an important element of a habitat.
This methodology was developed to map a glaciated terrain characterized by geologic substrates that typify a wide range of erosional and depositional sedimentary environments, and it likely will be useful for mapping substrates in other terrains. Substrate maps provide the physical framework required for identifying sediment transport processes, validating sediment transport models, studying the ecology of species and communities, and managing marine resources and seabed usage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195073","usgsCitation":"Valentine, P.C., 2019, Sediment classification and the characterization, identification, and mapping of geologic substrates for the glaciated Gulf of Maine seabed and other terrains, providing a physical framework for ecological research and seabed management: U.S. Geological Survey Scientific Investigations Report 2019–5073, 37 p., https://doi.org/10.3133/sir20195073.","productDescription":"vii, 37 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102650","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":368354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5073/coverthb.jpg"},{"id":368358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5073/sir20195073.pdf","text":"Report","size":"2.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5073"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Atlantic Ocean, Stellwagen Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.2301025390625,\n 42.809506838324204\n ],\n [\n -70.5157470703125,\n 42.65214190481525\n ],\n [\n -70.61737060546875,\n 42.56117285531808\n ],\n [\n -70.4718017578125,\n 42.114523952464246\n ],\n [\n -70.015869140625,\n 42.05133213230167\n ],\n [\n -70.2301025390625,\n 42.809506838324204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
(508) 548–8700 or (508) 457–2200
Large‐scale river restoration programs have emerged recently as a tool for improving spawning habitat for native salmonids in highly altered river ecosystems. Few studies have quantified the extent to which restored habitat is utilized by salmonids, which habitat features influence redd site selection, or the persistence of restored habitat over time. We investigated fall‐run Chinook salmon spawning site utilization and measured and modeled corresponding habitat characteristics in two restored reaches: a reach of channel and floodplain enhancement completed in 2013 and a reconfigured channel and floodplain constructed in 2002. Redd surveys demonstrated that both restoration projects supported a high density of salmon redds, 3 and 14 years following restoration. Salmon redds were constructed in coarse gravel substrates located in areas of high sediment mobility, as determined by measurements of gravel friction angles and a grain entrainment model. Salmon redds were located near transitions between pool‐riffle bedforms in regions of high predicted hyporheic flows. Habitat quality (quantified as a function of stream hydraulics) and hyporheic flow were both strong predictors of redd occurrence, though the relative roles of these variables differed between sites. Our findings indicate that physical controls on redd site selection in restored channels were similar to those reported for natural channels elsewhere. Our results further highlight that in addition to traditional habitat criteria (e.g., water depth, velocity, and substrate size), quantifying sediment texture and mobility, as well as intragravel flow, provides a more complete understanding of the ecological benefits provided by river restoration projects.
Investigation of ground failure triggered by the 2018
Track tubes are used to identify small animals by their tracks. Animals that are small enough to fit into the tubes walk over ink pads and onto cardstock paper to obtain bait within the tube, leaving their footprints. The tracking tubes described in this document are designed to be set on the ground with free access and exit at either end with additional design components for stability, durability, and efficiency. They are also designed to prevent dirt from getting onto the ink pads and to decrease the ability of birds and other mammals to pull out track cards or bait.
We describe detailed methods for constructing, setting and checking track tubes, as well as measuring and identifying small mammal prints for a small mammal study. The protocols described are for monitoring the Pacific pocket mouse (PPM); however, this method can be applied to many small mammal species that have uniquely identifiable tracks in relation to co-occurring species.
We have deployed track tubes for over 5 years on Marine Corps Base Camp Pendleton for PPM discovery efforts and to monitor the three extant PPM populations on Base. We have shown that nightly detection probability is similar to that of live-trapping, but the track tubes can be checked weekly or bi-monthly. We use this passive and economical method to assess timing of annual emergence and torpor, seasonal activity, and localized colonization and extinction events. Using this method, we can model occupancy dynamics in relation to habitat and disturbance covariates that directly inform management and support a monitoring and management feedback loop for this species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A15","collaboration":"Prepared in cooperation with the U.S. Marine Corps, Marine Corps Base Camp Pendleton","usgsCitation":"Brehme, C.S., Matsuda, T.A., Adsit-Morris, D.T., Clark, D.R., Burlaza, M.A.T., Sebes, J.B., and Fisher, R.N., 2019, Track tube construction and field protocol for small mammal surveys with emphasis on the endangered Pacific pocket mouse (Perognathus longimembris pacificus): U.S. Geological Survey Techniques and Methods, book 2, chap. A15, 18 p., plus appendix, https://doi.org/10.3133/tm2A15.","productDescription":"v, 30 p.","onlineOnly":"Y","ipdsId":"IP-095381","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":368471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a15/coverthb.jpg"},{"id":368472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a15/tm2a15.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 2A15"}],"country":"United States","state":"California","otherGeospatial":"Camp Pendleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.6580810546875,\n 33.19962596829635\n ],\n [\n -117.12799072265625,\n 33.19962596829635\n ],\n [\n -117.12799072265625,\n 33.43373345341701\n ],\n [\n -117.6580810546875,\n 33.43373345341701\n ],\n [\n -117.6580810546875,\n 33.19962596829635\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
Periodicity of fire disturbance is a known driver of ecosystem function and is reported as important in both promoting and maintaining viable breeding habitat for the endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis; CSSS). In south Florida, the CSSS serves as a fine-scale indicator of the marl and mixed-marl prairie communities of the Florida Everglades. The CSSS distribution is affected by numerous well-documented physical drivers, including water depth and fire regime. Here, we fit zero-inflated negative binomial generalized linear mixed models and used model selection to determine the relationship between CSSS bird count observations from 1992 to 2014 and the spatially-specific fire return interval on the landscape. CSSS bird count was highest at a 5–8-year fire return interval and increased linearly with the percent of cell burned (400 × 400 m cells). The results of this study can inform management plans designed to maintain existing, and promote new, marl prairie habitat for conservation of the CSSS.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.130","usgsCitation":"Benscoter, A., Beerens, J., Pearlstine, L.G., and Romanach, S., 2019, Fire disturbance influences endangered Cape Sable Seaside Sparrow (Ammopiza maritima mirabilis) relative bird count: Conservation Science and Practice, v. 1, no. 12, e130, 7 p., https://doi.org/10.1111/csp2.130.","productDescription":"e130, 7 p.","ipdsId":"IP-108301","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.130","text":"Publisher Index Page"},{"id":368712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.6943359375,\n 25.105497373014686\n ],\n [\n -80.37597656249999,\n 25.105497373014686\n ],\n [\n -80.37597656249999,\n 26.254009699865737\n ],\n [\n -81.6943359375,\n 26.254009699865737\n ],\n [\n -81.6943359375,\n 25.105497373014686\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"12","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Benscoter, Allison 0000-0003-4205-3808 abenscoter@usgs.gov","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":178750,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","email":"abenscoter@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beerens, James 0000-0001-8143-916X","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":220092,"corporation":false,"usgs":true,"family":"Beerens","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":774081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220093,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":774082,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205028,"text":"pp1854 - 2019 - Groundwater availability in the Ozark Plateaus aquifer system","interactions":[],"lastModifiedDate":"2019-10-23T07:17:38","indexId":"pp1854","displayToPublicDate":"2019-10-22T12:31:42","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1854","displayTitle":"Groundwater Availability in the Ozark Plateaus Aquifer System","title":"Groundwater availability in the Ozark Plateaus aquifer system","docAbstract":"The study described in this report, initiated by the U.S. Geological Survey in 2014, was designed to evaluate fresh groundwater resources within the Ozark Plateaus, central United States, as an area within a broader national assessment of groundwater availability. The goals of the Ozark study were to evaluate historical effects of human activities on water levels and groundwater availability, quantify groundwater resources now and under probable future pumping and climate conditions, and evaluate existing monitoring networks for their value in making better predictions of future groundwater resources. Previous studies include simulation of local-scale groundwater flow under varying temporal scales, or simulation of the regional system under steady-state conditions. While these studies are useful, particularly for the problem for which they were designed, there is a need to look at the larger regional system under transient conditions to fully evaluate the water resource over time. This study focused on multiple spatial and temporal scales to examine changes in groundwater pumping, storage, and water-level declines. The regional scale provides a broad view of the sources and demands on the system with time.
The study area covers approximately 68,000 square miles in the central United States in parts of Missouri, Arkansas, Kansas, and Oklahoma and encompasses the Ozark Plateaus Physiographic Province (Ozark Plateaus), including the Salem Plateau, Springfield Plateau, and Boston Mountains. Groundwater is withdrawn from the Ozark Plateaus aquifer system (Ozark system) for public supply and for domestic, agriculture (including irrigation and aquaculture), livestock, and non-agricultural use (including industrial, thermoelectric power generation, mining, and commercial). The Ozark system provides an important drinking-water supply for people living in the Ozark Plateaus because public supply and domestic use combined constitute the largest groundwater use. Precipitation is the ultimate source of freshwater to the Ozark system; most rainfall occurs during April, May, and June, and precipitation increases generally from north to south across the study area.
Groundwater use currently accounts for only 10 percent of the total water use in the areas overlying the Ozark system, but provides a critical drinking-water resource because public supply and domestic groundwater withdrawals are largely from groundwater resources. The 380 million gallons per day of groundwater withdrawn from the Ozark system in 2010 accounts for approximately 2 percent of recharge. Although groundwater use represents a small component of the hydrologic budget, because of low storage in aquifer units, cones of depression with steep water-level gradients can develop quickly around pumping centers.
The amount of water entering and leaving the aquifer system from 1900 to about 1965 was relatively constant at a rate of about 13 billion gallons per day (Bgal/d). Much of this inflow of water is discharged through streams in the system to balance the hydrologic budget. Changes in storage over time (from outflows to inflows) reflect the large variability in recharge: if recharge decreases, water levels will decrease, resulting in less groundwater discharge to streams and more water released from aquifer storage. Conversely, when recharge increases, water levels increase, more groundwater discharges to streams, and aquifer storage is replenished. Although pumping generally increased from 1900 to 2016, it does not appear to correlate with the change in storage over the same time period. Regionally, simulated change in groundwater storage corresponds with changes in recharge, more so than with increases in pumping.
Average recharge was 11.6 Bgal/d for the period 1900 to 2016. Recharge was generally above average from predevelopment to 1965, followed by a period of below-average recharge from 1965 to about 1980. Recharge remained consistently above average from 1980 to about 1988, after which there was a period of average or below-average recharge, reflected by a decline through the mid-2000s.
The implications and potential effects of increased pumping and long-term climate change on the Ozark Plateaus hydrologic system and groundwater availability are a concern for communities and resource managers in the area. Pumping varies from year to year, but is generally expected to moderately increase with population, industrial, and agricultural needs. Most climate models predict warmer minimum and maximum air temperatures by midcentury in the Ozark Plateaus area, especially from midspring through early fall. Three scenarios were developed to simulate possible future conditions from 2016 to 2060 and assess the potential effects on the hydrologic system and availability of water resources. For each scenario, changes in water levels and hydrologic budget components were evaluated from predevelopment (1900) to present (2016) and 45 years into the future (2060). The baseline scenario represents an extension of the average (1996 to 2016) seasonal pumping and recharge values. The pumping scenario is an extension of the average (1996 to 2016) seasonal recharge values with increases in pumping following the historical trend for the period 2016–2060 of up to 120 percent of the 1996 to 2016 average seasonal pumping values. The general circulation model (GCM) scenario is an extension of the average (1996 to 2016) seasonal pumping values and variable recharge based on seasonal averages of soil water storage from a water-balance model using temperature and precipitation from multiple GCMs.
The general patterns of water-level decline are similar for each scenario. The areas of water-level decline in southwest Missouri and northeast Oklahoma are only marginally different by 2060 from those of 2009. In one area south of Springfield, Mo., water-level declines are less in the baseline and GCM scenarios than in 2009. This may be the result of a transition from groundwater use to surface-water supplies for a larger percentage of the demand in the area.
For all three scenarios, forecasted pumping, recharge, and aquifer properties play an important role in determining the uncertainty of water-level forecasts at 94 real-time observation wells. Simulated aquifer properties in the productive middle and lower Ozark aquifers and the St. Francois confining unit of the Ozark system contribute most to predictive uncertainty in water levels at approximately 35 percent of the real-time observation wells. Out of the 94 real-time observation wells, 82 are developed in the lower Ozark aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1854","collaboration":"Water Availability and Use Science Program","usgsCitation":"Clark, B.R., Duncan, L.L., and Knierim, K.J., 2019, Groundwater availability in the Ozark Plateaus aquifer system: U.S. Geological Survey Professional Paper 1854, 82 p., https://doi.org/10.3133/pp1854.","productDescription":"Report: x, 82 p.; Data Release","numberOfPages":"95","onlineOnly":"Y","ipdsId":"IP-097847","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":368455,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1854/pp1854.pdf","text":"Report","size":"18.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1854"},{"id":368456,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H0PQ93","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model of groundwater flow in the Ozark Plateaus aquifer system"},{"id":368454,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1854/coverthb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
640 Grassmere Park, Suite 100
Nashville, Tennessee, 37211