The 2016–2017 shallow submarine eruption of Bogoslof volcano produced numerous infrasound signals over 9 months that were recorded on six Alaska Volcano Observatory (AVO) arrays at ranges of 59 to over 800 km from the volcano. The lack of geophysical monitoring near Bogoslof and the repeated production of volcanic clouds to flight levels made monitoring by remote infrasound critical during the eruption; for the first time, AVO relied extensively on automated infrasound detections from regional arrays to dispatch timely notifications of the ongoing activity. Most of the 70 eruptive events were detected on at least one array, but no array detected all of the events mainly because atmospheric conditions were highly variable during the eruption. Acoustic propagation modeling helps explain some of the variation in array detections but also highlights limitations in regional propagation models. To our knowledge, this is the first example of well-recorded infrasound from an explosive eruption occurring in shallow seawater, providing extensive insights into eruption dynamics in this unique environment. The dominance of low-frequency infrasound (0.1–1 Hz) is attributed to eruptions occurring beneath tens of meters of seawater. Higher-frequency infrasound signals were mostly limited to eruptions where the vent was isolated from major interaction with seawater or in several cases where a lava dome grew above sea level.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1355-0","usgsCitation":"Lyons, J.J., Iezzi, A., Fee, D., Schwaiger, H., Wech, A., and Haney, M.M., 2020, Infrasound generated by the 2016-2017 shallow submarine eruption of Bogoslof volcano, Alaska: Bulletin of Volcanology, v. 82, 19, 14 p., https://doi.org/10.1007/s00445-019-1355-0.","productDescription":"19, 14 p.","ipdsId":"IP-112505","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":383363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -178.63769531249997,\n 48.86471476180277\n ],\n [\n -155.0390625,\n 48.86471476180277\n ],\n [\n -155.0390625,\n 61.39671887310411\n ],\n [\n -178.63769531249997,\n 61.39671887310411\n ],\n [\n -178.63769531249997,\n 48.86471476180277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":810601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":199660,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[],"preferred":false,"id":810602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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 Center","active":true,"usgs":true}],"preferred":true,"id":810603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810605,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226997,"text":"70226997 - 2020 - Ecosystem-specific growth responses to climate pattern by a temperate freshwater fish","interactions":[],"lastModifiedDate":"2021-12-27T14:45:19.675432","indexId":"70226997","displayToPublicDate":"2020-01-31T08:42:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Ecosystem-specific growth responses to climate pattern by a temperate freshwater fish","docAbstract":"Somatic growth patterns among animal populations are maintained through complex processes that vary among ecosystems. Changes in growth patterns may be concomitant with changes in climate; however, understanding how growth will manifest among ecosystems is limited. Information embedded within fish hard-parts (i.e., otoliths, spines, vertebrae) can account for variation in growth patterns resulting from changing climate conditions. Channel catfish Ictalurus punctatus is a freshwater fish species widely distributed across North America with limited information regarding climate influences on growth and differences in climate-growth relations among ecological systems. We assessed growth (total length) response to changing climate conditions for channel catfish among three waterbody types—pit lakes, irrigation and power-generation reservoirs, and flood-control reservoirs in Nebraska, USA. We used linear mixed-effect models and an information theoretic approach to assess the relative strengths among competing hypotheses. The most supported linear mixed-effect model of channel catfish growth was a function of fish age and an interaction between waterbody type and growing-degree-day (GDD). A positive trend existed in GDD from 1990 through 2008 whereby the predicted increase in GDD among waterbody types ranged from 182 GDD to 189 GDD. The predicted change in channel catfish growth resulting from increased GDD ranged from 1% to 39% among waterbody types. Channel catfish population rate functions, thus, may not respond similarly to climate conditions across ecosystem types. Changes in climate variables may contribute to system-specific responses in population dynamics for channel catfish as well as other similar freshwater species. The establishment of relations between climate and growth variables for a freshwater generalist with a plastic diet and broad temperature tolerance serves as an indication of the breadth of responses possible for freshwater fishes under global changes in climate conditions.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models.
Estimating the number of breeding pairs in a mixed-species waterbird colony is difficult because colonial waterbirds are vulnerable to human intrusion and their colonies are often in remote areas with limited access. We investigated methods to estimate the number of nests of waterbirds at a large, mixed-species colony at Chase Lake National Wildlife Refuge in south-central North Dakota. The primary goals of this study were to evaluate survey methods for shrub- and ground-nesting colonial waterbirds at Chase Lake National Wildlife Refuge and to develop protocols for estimating abundance of the different species. The specific objectives were (1) to assess visible-nest counts for ciconiiform species from the perimeter of nesting areas (hereafter, perimeter counts) and observational surveys from fixed points outside the colony to count flights of adult ciconiiforms in and out of the colony (hereafter, flightline surveys) as alternatives to within-colony counts of ciconiiform nests, and (2) to assess semiautomated, pixel-based image-analysis techniques to estimate abundance of American White Pelicans (Pelecanus erythrorhynchos) as an alternative to traditional manual counts from aerial photographs.
For shrub-nesting ciconiiform species, observers counted 2,259 and 1,759 active ciconiiform nests in 2012 and 2013, respectively, during within-colony counts of ciconiiform nests. Results from within-colony counts of ciconiiform nests indicated a positive relation between the number of nests and the area of the shrub subcolony for the three most common ciconiiform species and all ciconiiform species combined. The perimeter nest counts of ciconiiform nests at Chase Lake represented only 18.8 percent of the total active ciconiiform nests counted in 11 subcolonies in 2012, which was well below the recommended target of 50 percent. Although we found a positive relationship between the number of nests counted during perimeter counts and the number of nests counted during within-colony counts for the three most common ciconiiform species and all ciconiiform species combined, perimeter counts at Chase Lake were hampered by disturbance to nesting birds. Thus, we discontinued the perimeter counts before they were completed. We did not develop predictive models from these perimeter counts in 2012 because these models could be misleading due to inconsistent application of the survey methods, which likely would have provided inaccurate perimeter counts. The extent of this issue is unknown. Flightline surveys at Chase Lake documented patterns of ciconiiform activity that were unknown for this region. For the common ciconiiform species, the number of flights to and from the South Island at Chase Lake were greatest in the morning (7:00−12:00 central daylight time [CDT]) and least in the afternoon (12:00−17:00), and least early in the breeding season (May 29–June 20, 2013) and greatest later in the breeding season (June 24–August 1, 2013). Flightline surveys are an index but lacked comparability with within-colony nest counts because the two methods provide measures of different things (that is, adult activity away from the colony as compared to the number of nests within the colony). The overall proportions of flights generally reflected the proportions of the within-colony nest counts for the four most common species: Black-crowned Night-Heron (Nycticorax nycticorax), Cattle Egret (Bubulcus ibis), Great Egret (Ardea alba), and Snowy Egret (Egretta thula). Flightline surveys at Chase Lake indicated apparent variation related to the time of day and season, as well as a variation in detection of inbound and outbound adult ciconiiforms. For ciconiiforms at Chase Lake, the most appropriate combination of survey approaches will depend on the need for annual estimates of nest abundance of ciconiiform species, balanced with the financial, personnel, and logistical constraints associated with the survey methods.
For ground-nesting American White Pelicans, the results from this study indicated that digital-image processing using remote-sensing software provides an accurate estimate of the number of American White Pelican nests. Estimates of the number of pelican nests from digital-image processing, using two commercially available remote-sensing software packages, produced nest estimates that were comparable to those of traditional manual counts from aerial photographs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201008","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Igl, L.D., Bartos, A.J., Woodward, R.O., Scherr, P., and Sovada, M.A, 2020, Evaluation of survey methods for colonial waterbirds at Chase Lake National Wildlife Refuge, North Dakota: U.S. Geological Survey Open-File Report 2020–1008, 44 p., https://doi.org/10.3133/ofr20201008. ","productDescription":"Report: viii, 44 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-112516","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":371775,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1008/ofr20201008.pdf","text":"Report","size":"7.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1008"},{"id":371774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1008/coverthb.jpg"},{"id":371776,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90NK31K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evaluation of Survey Methods for Colonial Waterbirds at Chase Lake National Wildlife Refuge, North Dakota, data release"}],"country":"United States","state":"North Dakota ","otherGeospatial":"Chase Lake National Wildlife Refuge","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.481105,46.983794 ], [ -99.481105,47.030693 ], [ -99.417191,47.030693 ], [ -99.417191,46.983794 ], [ -99.481105,46.983794 ] ] ] } } ] }","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
Since the 1990s, increased chloride concentrations of water pumped from wells (much of which is used for drinking water) and the effects of withdrawals on groundwater-dependent ecosystems have led to concerns over groundwater availability on the island of Molokaʻi, Hawaiʻi. An improved understanding of the hydrologic effects of proposed groundwater withdrawals is needed to ensure effective management of the groundwater resources of Molokaʻi, plan for possible growth, and accommodate cultural, social, and economic concerns. To address the information needs of managers and community stakeholders on Molokaʻi, the U.S. Geological Survey developed a numerical groundwater model capable of simulating salinity change and reduction in groundwater discharge in coastal areas of central and southern Molokaʻi. Estimates of groundwater recharge needed as input to the numerical groundwater model were made using a daily water budget for each decade during 1940−2012 (the period 2000−12 spanned 13 years) and the most current available data, including the distributions of monthly rainfall and potential evapotranspiration. Total island recharge during the decadal periods ranged from a low of about 189 Mgal/d during the 1970s to a high of 278 Mgal/d during the 1960s. These recharge estimates were used to develop an island-wide numerical groundwater model with simplifying assumptions (sharp interface between freshwater and saltwater; two-dimensional flow). The island-wide model provided estimates of groundwater inflows to the main area of interest simulated with a three-dimensional numerical groundwater model. Simulated withdrawal scenarios were selected in consultation with water managers and stakeholders and consisted of: (1) a baseline scenario using average recharge (1978−2007 rainfall and 2010 land cover) and average 2016−17 withdrawals; (2) a scenario using average recharge and withdrawals from existing wells at pending (as of January 2019) water-use permit rates; (3) six scenarios using average recharge and selected withdrawals from existing and proposed wells; and (4) a scenario using reduced recharge and selected withdrawals from existing and proposed wells. Results of the simulated withdrawal scenarios indicate that wells may be capable of producing groundwater with chloride concentrations below 250 mg/L at withdrawal rates exceeding average 2016−17 rates. However, the quality of water withdrawn from production wells is dependent on the rate and distribution of the withdrawals. For all nonbaseline scenarios, simulated groundwater discharge to the nearshore environment is reduced relative to the baseline scenario. Areas of discharge reduction may correspond to areas used for cultural or subsistence purposes. The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the hydrologic effects of additional groundwater withdrawals in central Molokaʻi. The model has several limitations, including its nonuniqueness and inability to account for local-scale heterogeneities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195150","collaboration":"Prepared in cooperation with the State of Hawai‘i Department of Hawaiian Home Lands, State of Hawai‘i Office of Hawaiian Affairs, and County of Maui Department of Water Supply","usgsCitation":"Oki, D.S., Engott, J.A., and Rotzoll, K., 2020, Numerical simulation of groundwater availability in central Moloka‘i, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5150, 95 p., https://doi.org/10.3133/sir20195150.","productDescription":"Report: ix, 95 p.; Data Release","numberOfPages":"95","onlineOnly":"Y","ipdsId":"IP-032683","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":399622,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109628.htm"},{"id":371721,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRQASS","linkHelpText":"Central Molokaʻi, Hawaiʻi, SUTRA model"},{"id":371719,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5150/coverthb.jpg"},{"id":371720,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5150/sir20195150.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5150"}],"country":"United States","state":"Hawaii","otherGeospatial":"Moloka‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.77352905273438,\n 21.179289725795993\n ],\n [\n -156.8572998046875,\n 21.163922551671376\n ],\n [\n -156.92184448242188,\n 21.167764494849468\n ],\n [\n -156.97265625,\n 21.211299542246586\n ],\n [\n -156.99737548828125,\n 21.189533621502626\n ],\n [\n -157.1429443359375,\n 21.199776807250093\n ],\n [\n -157.21298217773435,\n 21.220261047755002\n ],\n [\n -157.25830078125,\n 21.218980865996457\n ],\n [\n -157.25555419921875,\n 21.17672864097083\n ],\n [\n -157.2967529296875,\n 21.14599216495789\n ],\n [\n -157.30499267578125,\n 21.097313035028538\n ],\n [\n -157.18826293945312,\n 21.090906697412837\n ],\n [\n -157.08801269531247,\n 21.103719096296263\n ],\n [\n -157.03582763671875,\n 21.090906697412837\n ],\n [\n -156.90811157226562,\n 21.051181240269393\n ],\n [\n -156.84906005859375,\n 21.047336278183312\n ],\n [\n -156.77215576171875,\n 21.08450008351735\n ],\n [\n -156.70074462890625,\n 21.15879980561845\n ],\n [\n -156.77352905273438,\n 21.179289725795993\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Controlled and natural source seismic data are used to build a 3-D P wave model for southern North Island, New Zealand, where the Pacific Plate subducts beneath the Australian Plate at a rate of ~41 mm/year. Our analysis reveals an abrupt along-strike transition in overthrusting plate structure within Cook Strait. Contrasts in properties (Vp, Vp/Vs, and Qs) likely reflects the degree of deformation in the Australian Plate, where the Alpine-Wairau and Awatere Faults mark the northern boundary of a terrane that has undergone >50° of clockwise vertical-axis rotation since the early Miocene. Heterogeneity of the crustal transition is likely associated with changes in frictional and elastic properties that may impact elastic stress accumulation and inhibit southward propagation of megathrust earthquakes. Low connectivity of faults in Cook Strait is consistent with the heterogeneity we observe and may promote complex earthquake triggering by lateral stress loading during earthquakes or slow slip events.
Effects of riparian vegetation on fluvial sediment dynamics depend on morphological traits of the constituent species. Determining the effects of different morphological guilds on sedimentation rates, as influenced by multiple aspects of dam operations, can help identify viable strategies for streamflow and vegetation management to achieve riparian resource goals. Plants of increasing size and branching density or complexity have been found to have greater effects on sedimentation in free‐flowing systems; however, this relationship could differ in regulated rivers. We tested the hypothesis that plant guilds of increasing height and branching complexity would be positively associated with sedimentation rates on 23 sandbars deposited in zones of recirculating flow (eddies) along the Colorado River in Grand Canyon. We used an image‐based vegetation classification and digital elevation models from annual topographic surveys to track associations between six plant morphological guilds and topographic change over 5 years. Vegetation had significant associations with deposition after accounting for geomorphic setting, but the ordinal guild scale was not positively correlated with deposition magnitude. Instead, low‐statured rhizomatous and herbaceous guilds were particularly effective at capturing sediment in the separation zone of sandbars, whereas tall herbs and large shrubs were most effective at capturing sediment in reattachment zones. These nuanced interactions between geomorphic position and morphological guild may be a direct consequence of flow regulation through modifications to physical deposition and erosion processes. Flow regulation may also select for a narrow subset of morphological guilds, reducing the diversity of vegetation feedbacks on sedimentation and emphasizing geomorphic drivers.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3589","usgsCitation":"Butterfield, B.J., Grams, P.E., Durning, L., Hazel, J., Palmquist, E.C., Ralston, B., and Sankey, J.B., 2020, Associations between riparian plant morphological guilds and fluvial sediment dynamics along the regulated Colorado River in Grand Canyon: River Research and Applications, v. 36, no. 3, p. 410-421, https://doi.org/10.1002/rra.3589.","productDescription":"12 p.","startPage":"410","endPage":"421","ipdsId":"IP-110031","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437137,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93F8JJK","text":"USGS data release","linkHelpText":"Long-term sandbar monitoring data along the Colorado River in Marble and Grand Canyons, Arizona"},{"id":377385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.005126953125,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":795829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Durning, Laura E. 0000-0003-3282-2458","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":177023,"corporation":false,"usgs":false,"family":"Durning","given":"Laura E.","affiliations":[],"preferred":false,"id":795831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hazel, Joseph 0000-0003-0903-3397","orcid":"https://orcid.org/0000-0003-0903-3397","contributorId":189310,"corporation":false,"usgs":false,"family":"Hazel","given":"Joseph","affiliations":[],"preferred":false,"id":795832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ralston, Barbara 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":195797,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":795834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795835,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211256,"text":"70211256 - 2020 - Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems","interactions":[],"lastModifiedDate":"2020-07-22T15:45:46.58433","indexId":"70211256","displayToPublicDate":"2020-01-29T10:42:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems","docAbstract":"Seagrass provides a wide range of economically and ecologically valuable ecosystem services, with shoreline erosion control often listed as a key service, but can also alter the sediment dynamics and waves within back-barrier bays. Here we incorporate seagrass dynamics into an existing barrier-marsh exploratory model, GEOMBEST++, to examine the coupled interactions of the back-barrier bay with both adjacent (marsh) and non-adjacent (barrier island) subsystems. While seagrass reduces marsh edge erosion rates and increases progradation rates in many of our 288 model simulations, seagrass surprisingly increases marsh edge erosion rates when sediment export from the back-barrier basin is negligible because the ability of seagrass to reduce the volume of marsh sediment eroded matters little for back-barrier basins in which all sediment is conserved. Our model simulations also suggest that adding seagrass to the bay subsystem leads to increased deposition in the bay, reduced sediment available to the marsh, and enhanced marsh edge erosion until the bay reaches a new, shallower equilibrium depth. In contrast, removing seagrass liberates previously-sequestered sediment that is then delivered to the marsh, leading to enhanced marsh progradation. Lastly, we find that seagrass reduces barrier island migration rates in the absence of back-barrier marsh by filling accommodation space in the bay. These model observations suggest that seagrass meadows operate as dynamic sources and sinks of sediment that can influence the evolution of coupled marsh and barrier island landforms in unanticipated ways.","language":"English","publisher":"Wiley","doi":"10.1029/2019JG005416","usgsCitation":"Reeves, I., Moore, L., Goldstein, E., Murray, B., Carr, J., and Kirwan, M.L., 2020, Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems: Journal of Geophysical Research: Biogeosciences, v. 125, no. 2, e2019JG005416, 19 p., https://doi.org/10.1029/2019JG005416.","productDescription":"e2019JG005416, 19 p.","ipdsId":"IP-102822","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457972,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jg005416","text":"Publisher Index Page"},{"id":376641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.15997314453125,\n 37.03983207971425\n ],\n [\n -75.74523925781249,\n 37.01571219880126\n ],\n [\n -75.41015624999999,\n 37.87268533717655\n ],\n [\n -75.87432861328125,\n 37.87268533717655\n ],\n [\n -76.15997314453125,\n 37.03983207971425\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Ian","contributorId":229522,"corporation":false,"usgs":false,"family":"Reeves","given":"Ian","affiliations":[{"id":16637,"text":"University of North Carolina, Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":793437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Laura","contributorId":19090,"corporation":false,"usgs":false,"family":"Moore","given":"Laura","affiliations":[{"id":24532,"text":"Department of Geological Sciences, University of North Carolina, Chapel Hill, NC 27599, USA","active":true,"usgs":false}],"preferred":false,"id":793438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldstein, Evan ","contributorId":221556,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan ","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":793439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, Brad","contributorId":229523,"corporation":false,"usgs":false,"family":"Murray","given":"Brad","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":793440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":793441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":793442,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208019,"text":"ofr20201009 - 2020 - Potential duration of aftershocks of the 2020 southwestern Puerto Rico earthquake","interactions":[],"lastModifiedDate":"2022-04-21T20:38:12.80295","indexId":"ofr20201009","displayToPublicDate":"2020-01-29T08:27:35","publicationYear":"2020","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":"2020-1009","displayTitle":"Potential Duration of Aftershocks of the 2020 Southwestern Puerto Rico Earthquake","title":"Potential duration of aftershocks of the 2020 southwestern Puerto Rico earthquake","docAbstract":"Aftershocks (earthquakes clustered spatially and chronologically near the occurrence of a causative earthquake) are ongoing in southwestern Puerto Rico after a series of earthquakes, which include a magnitude 6.4 earthquake that occurred near Barrio Indios, Guayanilla, on January 7, 2020, and affected the surrounding area. This report estimates the expected duration of these aftershocks by incorporating observations of aftershocks as of January 17, 2020, into a well-established statistical model of how earthquake sequences behave. Aftershocks will persist for years to decades, although with decreasing frequency, and earthquakes will likely be felt on a daily basis for up to several months. These estimates have significant uncertainty owing to different scenarios of how the earthquake sequence may evolve over time and could also change if a new large aftershock occurs. This report also estimates the amount of time remaining until the annual probability of magnitude 5, 6, and 7 or greater aftershocks—which could cause additional damage—decreases to 50, 25, 10, 5, and 1 percent. As of this writing, the chance of having a magnitude 6 or greater earthquake within a given year, going forward, will not fall below 25 percent for another 3 months to 3 years. The chance of having a magnitude 5 or greater earthquake will not fall below 25 percent for a decade or more. The aftershocks discussed in this report would be located in the same general area as the aftershocks that have already occurred. Our results do not imply a change in the risk of earthquakes in other parts of Puerto Rico.
Las réplicas (terremotos agrupados espacial y cronológicamente cerca de la ocurrencia de un terremoto causante) están en curso en el suroeste de Puerto Rico después de una serie de terremotos, que incluyen un terremoto de magnitud 6.4, ocurrido cerca del Barrio Indios, Guayanilla, el 7 de enero de 2020 y que afectaron las áreas circundantes. Este informe estima la duración esperada de las réplicas incorporando observaciones de réplicas a partir del 17 de enero de 2020 en un modelo estadístico bien establecido de cómo se comportan las secuencias de terremotos. Las réplicas persistirán durante años o décadas, aunque con una frecuencia decreciente, y los terremotos probablemente se sentirán a diario durante varios meses. Estos estimados tienen una incertidumbre significativa debido a diferentes escenarios de cómo la secuencia del terremoto puede evolucionar con el tiempo y también podrían cambiar si ocurre una nueva réplica grande. Este informe también estima la cantidad de tiempo restante hasta que la probabilidad anual de réplicas de magnitud 5, 6 y 7 o más - que podría causar daños adicionales- disminuya a un 50, 25, 10, 5 y 1 por ciento. Al momento de escribir este artículo, la posibilidad de tener un terremoto de magnitud 6 en un año determinado, en el futuro, no caerá por debajo del 25 por ciento durante otros 3 meses a 3 años. La probabilidad de tener un terremoto de magnitud 5 o mayor no será inferior al 25 por ciento durante una década o más. Las réplicas discutidas en este informe se ubicarían en la misma área general que las réplicas que ya han ocurrido. Nuestros resultados no implican un cambio en el riesgo de terremotos en otras partes de Puerto Rico.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201009","usgsCitation":"van der Elst, N.J., Hardebeck, J.L., and Michael, A.J., 2020, Potential duration of aftershocks of the 2020 southwestern Puerto Rico earthquake: U.S. Geological Survey Open-File Report 2020–1009, 5 p., https://doi.org/10.3133/ofr20201009.","productDescription":"v, 5 p.","onlineOnly":"Y","ipdsId":"IP-115560","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":399452,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109627.htm"},{"id":371666,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1009/coverthb.jpg"},{"id":371665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1009/ofr20201009_spanish.pdf","text":"Report (Español)","linkFileType":{"id":1,"text":"pdf"}},{"id":371664,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1009/ofr20201009.pdf","text":"Report (English)","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.21,\n 17.7683\n ],\n [\n -66.5942,\n 17.7683\n ],\n [\n -66.5942,\n 18.0558\n ],\n [\n -67.21,\n 18.0558\n ],\n [\n -67.21,\n 17.7683\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
Polar bears (Ursus maritimus) from the southern Beaufort Sea (SB) subpopulation have traditionally fed predominantly upon ice‐seals; however, as the proportion of the subpopulation using onshore habitat has recently increased, foraging on land‐based resources, including remains of subsistence‐harvested bowhead whales (Balaena mysticetus) and colonial nesting seabirds has been observed. Adipose tissue samples were collected from this subpopulation during the springs of 2013–2016 and analyzed for fatty acid signatures. Diet estimates were generated for the proportional consumption of ringed seal (Pusa hispida), bearded seal (Erignathus barbatus), and beluga whale (Delphinapterus leucas), relative to onshore foods, including bowhead whale remains and seabird, as represented by black guillemot (Cepphus grylle mandtii) nestlings and eggs. Quantitative fatty acid signature analysis (QFASA) estimated that the ice‐obligate prey, ringed seal, remained the predominant prey species of SB polar bears (46.4 ± 1.8%), with much lower consumption of bearded seal (19.6 ± 2.0%), seabird (17.0 ± 1.2%), bowhead whale (15.0 ± 1.4%), and hardly any beluga whale (2.0 ± 0.5%). Adult and subadult females appeared to depend more on the traditional ringed seal prey than adult and subadult males. Diet estimates of SB polar bears showed significant interannual variability for all prey (F12, 456 = 3.17, p < .001). Longer‐term estimates suggested that both types of onshore prey, bowhead whale remains and seabird, have represented a moderate proportion of the food resources used by SB polar bears since at least the start of the 21st Century.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6043","usgsCitation":"Bourque, J., Atwood, T.C., Divoky, G.J., Stewart, C., and McKinney, M.A., 2020, Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources: Ecology and Evolution, v. 10, no. 4, p. 2093-2103, https://doi.org/10.1002/ece3.6043.","productDescription":"11 p.","startPage":"2093","endPage":"2103","ipdsId":"IP-108540","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":457977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6043","text":"Publisher Index Page"},{"id":437138,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JXZSUS","text":"USGS data release","linkHelpText":"Diet Estimates of Southern Beaufort Sea Polar Bears, 2004-2016"},{"id":371900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska ","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.68554687499997,\n 69.62651016802958\n ],\n [\n -140.9765625,\n 69.62651016802958\n ],\n [\n -140.9765625,\n 73.57816726137321\n ],\n [\n -162.68554687499997,\n 73.57816726137321\n ],\n [\n -162.68554687499997,\n 69.62651016802958\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Bourque, Jennifer","contributorId":222102,"corporation":false,"usgs":false,"family":"Bourque","given":"Jennifer","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":781205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":781204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Divoky, George J.","contributorId":100912,"corporation":false,"usgs":false,"family":"Divoky","given":"George","email":"","middleInitial":"J.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":781206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Connie","contributorId":222103,"corporation":false,"usgs":false,"family":"Stewart","given":"Connie","email":"","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":781207,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKinney, Melissa A.","contributorId":11496,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":6619,"text":"University of Connecticutt","active":true,"usgs":false}],"preferred":false,"id":781208,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208196,"text":"70208196 - 2020 - A brief introduction to seismic instrumentation: Where does my data come from?","interactions":[],"lastModifiedDate":"2020-03-11T15:05:45","indexId":"70208196","displayToPublicDate":"2020-01-29T06:56:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"A brief introduction to seismic instrumentation: Where does my data come from?","docAbstract":"Modern seismology has been able to take advantage of several technological advances. These include feedback loops in the seismometer, specialized digitizers with absolute timing, and compression formats for storing data. While all of these advances have helped to improve the field, they can also leave newcomers a bit confused. Our goal here is to give a brief overview of how recordings of seismic ground motion originate. We discuss the chain of events that are required to obtain digital data plus how these steps can be reversed to recover units of ground motion such as acceleration, velocity, or displacement. Finally, we show a few examples of data that has become compromised because of various non-ground motion signals. We hope to give a quick practical introduction to allow the reader to become familiar with the various jargon used in observational seismology.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190214","usgsCitation":"Ringler, A.T., and Bastien, P., 2020, A brief introduction to seismic instrumentation: Where does my data come from?: Seismological Research Letters, v. 91, no. 2A, p. 1074-1083, https://doi.org/10.1785/0220190214.","productDescription":"10 p.","startPage":"1074","endPage":"1083","ipdsId":"IP-112527","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":371784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"2A","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":780907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bastien, Patrick 0000-0001-7222-3906","orcid":"https://orcid.org/0000-0001-7222-3906","contributorId":222001,"corporation":false,"usgs":true,"family":"Bastien","given":"Patrick","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":780908,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227803,"text":"70227803 - 2020 - Age distribution of red tree voles in northern spotted owl pellets estimated from molar tooth development","interactions":[],"lastModifiedDate":"2022-02-01T20:45:09.725338","indexId":"70227803","displayToPublicDate":"2020-01-28T15:44:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2900,"text":"Northwest Science","onlineIssn":"2161-9859","printIssn":"0029-344X","active":true,"publicationSubtype":{"id":10}},"title":"Age distribution of red tree voles in northern spotted owl pellets estimated from molar tooth development","docAbstract":"We used molar measurements from 136 known-age red tree voles (Arborimus longicaudus) to develop regression models that could estimate tree vole age from skeletonized remains. The best regression included a quadratic structure of the ratio between two measurements, crown height and anterior height, and natural log-transformed age in days. The regression predicted that molar roots begin to develop at 40 days of age and that molar crowns are worn completely away at 1,177 days of age. We used the regression to estimate the age distribution of 1,703 red tree voles found in northern spotted owl (Strix occidentalis caurina) pellets collected in western Oregon during 1970–2009. The age distribution of red tree voles in pellets was dominated by young individuals, with 81% younger than one year and only 0.5% older than two years. The proportion of individuals 61–120 days old was particularly high relative to other age classes. The proportion of subadult (52–120 days old) individuals exhibited regional variation between the Oregon Cascades and the Coast Range. Localized annual variation in age distribution was low, exhibited no evidence of cyclic variation, and was positively associated with local precipitation rates during the spotted owl nesting season (March–June). We hypothesize that the age distribution of tree voles in owl pellets may be similar to the age structure of tree vole populations in the wild, but acknowledge that this is virtually impossible to test because tree voles cannot be adequately sampled using conventional small mammal capture methods.
","language":"English","publisher":"BioOne","doi":"10.3955/046.093.0304","usgsCitation":"Marks-Fife, C.A., Forsman, E.D., and Dugger, K., 2020, Age distribution of red tree voles in northern spotted owl pellets estimated from molar tooth development: Northwest Science, v. 93, no. 3-4, p. 193-208, https://doi.org/10.3955/046.093.0304.","productDescription":"16 p.","startPage":"193","endPage":"208","ipdsId":"IP-092934","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"3-4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marks-Fife, Chad A.","contributorId":272849,"corporation":false,"usgs":false,"family":"Marks-Fife","given":"Chad","email":"","middleInitial":"A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":832335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":832336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832334,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210620,"text":"70210620 - 2020 - Does the virus cross the road? Viral phylogeographic patterns among bobcat populations reflect a history of urban development","interactions":[],"lastModifiedDate":"2020-09-10T19:53:51.951009","indexId":"70210620","displayToPublicDate":"2020-01-28T11:42:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1601,"text":"Evolutionary Applications","active":true,"publicationSubtype":{"id":10}},"title":"Does the virus cross the road? Viral phylogeographic patterns among bobcat populations reflect a history of urban development","docAbstract":"Urban development has major impacts on connectivity among wildlife populations and is thus likely an important factor shaping pathogen transmission in wildlife. However, most investigations of wildlife diseases in urban areas focus on prevalence and infection risk rather than potential effects of urbanization on transmission itself. Feline immunodeficiency virus (FIV) is a directly transmitted retrovirus that infects many felid species and can be used as a model for studying pathogen transmission at landscape scales. We investigated phylogenetic relationships among FIV isolates sampled from five bobcat (Lynx rufus ) populations in coastal southern California that appear isolated due to major highways and dense urban development. Divergence dates among FIV phylogenetic lineages in several cases reflected historical urban growth and construction of major highways. We found strong FIV phylogeographic structure among three host populations north‐west of Los Angeles, largely coincident with host genetic structure. In contrast, relatively little FIV phylogeographic structure existed among two genetically distinct host populations south‐east of Los Angeles. Rates of FIV transfer among host populations did not vary significantly, with the lack of phylogenetic structure south‐east of Los Angeles unlikely to reflect frequent contemporary transmission among populations. Our results indicate that major barriers to host gene flow can also act as barriers to pathogen spread, suggesting potentially reduced susceptibility of fragmented populations to novel directly transmitted pathogens. Infrequent exchange of FIV among host populations suggests that populations would best be managed as distinct units in the event of a severe disease outbreak. Phylogeographic inference of pathogen transmission is useful for estimating the ability of geographic barriers to constrain disease spread and can provide insights into contemporary and historical drivers of host population connectivity.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.12927","usgsCitation":"Kozakiewicz, C.P., Burridge, C.P., Funk, W., Craft, M.E., Crooks, K.R., Fisher, R.N., Fountain-Jones, N.M., Jennings, M.K., Kraberger, S.J., Lee, J.S., Lyren, L.M., Riley, S.P., Serieys, L.E., VandeWoude, S., and Carver, S., 2020, Does the virus cross the road? Viral phylogeographic patterns among bobcat populations reflect a history of urban development: Evolutionary Applications, v. 13, no. 3, p. 1806-1817, https://doi.org/10.1111/eva.12927.","productDescription":"12 p.","startPage":"1806","endPage":"1817","ipdsId":"IP-115355","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457988,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12927","text":"Publisher Index Page"},{"id":375558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles, San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.38818359375,\n 34.07086232376631\n ],\n [\n -117.65258789062499,\n 34.07086232376631\n ],\n [\n -117.65258789062499,\n 35.25907654252574\n ],\n [\n -120.38818359375,\n 35.25907654252574\n ],\n [\n -120.38818359375,\n 34.07086232376631\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.68554687499999,\n 32.55144352864431\n ],\n [\n -114.949951171875,\n 32.55144352864431\n ],\n [\n -114.949951171875,\n 34.25721644329402\n ],\n [\n -117.68554687499999,\n 34.25721644329402\n ],\n [\n -117.68554687499999,\n 32.55144352864431\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kozakiewicz, Christopher P.","contributorId":212126,"corporation":false,"usgs":false,"family":"Kozakiewicz","given":"Christopher","email":"","middleInitial":"P.","affiliations":[{"id":38423,"text":"School of Biological Sciences, University of Tasmania, Hobart, Tasmania, Australia","active":true,"usgs":false}],"preferred":false,"id":790866,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burridge, Christopher P.","contributorId":221854,"corporation":false,"usgs":false,"family":"Burridge","given":"Christopher","email":"","middleInitial":"P.","affiliations":[{"id":16141,"text":"University of Tasmania","active":true,"usgs":false}],"preferred":false,"id":790867,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funk, W. 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2020 - Climate and human water use diminish wetland networks supporting continental waterbird migration","interactions":[],"lastModifiedDate":"2022-02-14T17:22:16.822394","indexId":"70228598","displayToPublicDate":"2020-01-28T10:42:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate and human water use diminish wetland networks supporting continental waterbird migration","docAbstract":"Migrating waterbirds moving between upper and lower latitudinal breeding and wintering grounds rely on a limited network of endorheic lakes and wetlands when crossing arid continental interiors. Recent drying of global endorheic water stores raises concerns over deteriorating migratory pathways, yet few studies have considered these effects at the scale of continental flyways. Here, we investigate the resiliency of waterbird migration networks across western North America by reconstructing long-term patterns (1984–2018) of terminal lake and wetland surface water area in 26 endorheic watersheds. Findings were partitioned regionally by snowmelt- and monsoon-driven hydrologies and combined with climate and human water-use data to determine their importance in predicting surface water trends. Nonlinear patterns of lake and wetland drying were apparent along latitudinal flyway gradients. Pervasive surface water declines were prevalent in northern snowmelt watersheds (lakes −27%, wetlands −47%) while largely stable in monsoonal watersheds to the south (lakes −13%, wetlands +8%). Monsoonal watersheds represented a smaller proportion of total lake and wetland area, but their distribution and frequency of change within highly arid regions of the continental flyway increased their value to migratory waterbirds. Irrigated agriculture and increasing evaporative demands were the most important drivers of surface water declines. Underlying agricultural and wetland relationships however were more complex. Approximately 7% of irrigated lands linked to flood irrigation and water storage practices supported 61% of all wetland inundation in snowmelt watersheds. In monsoonal watersheds, small earthen dams, meant to capture surface runoff for livestock watering, were a major component of wetland resources (67%) that supported networks of isolated wetlands surrounding endorheic lakes. Ecological trends and human impacts identified herein underscore the importance of assessing flyway-scale change as our model depictions likely reflect new and emerging bottlenecks to continental migration.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15010","usgsCitation":"Donnelly, J., King, S.L., Silverman, N., Collins, D., Carrera-Gonzalez, E., Lafon-Terrazas, A., and Moore, J., 2020, Climate and human water use diminish wetland networks supporting continental waterbird migration: Global Change Biology, v. 26, no. 4, p. 2042-2059, https://doi.org/10.1111/gcb.15010.","productDescription":"18 p.","startPage":"2042","endPage":"2059","ipdsId":"IP-112789","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.15010","text":"Publisher Index Page"},{"id":395897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","volume":"26","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, J.P.","contributorId":276300,"corporation":false,"usgs":false,"family":"Donnelly","given":"J.P.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silverman, N.L.","contributorId":276301,"corporation":false,"usgs":false,"family":"Silverman","given":"N.L.","email":"","affiliations":[{"id":56951,"text":"Adaptive Hydrology, LLC","active":true,"usgs":false}],"preferred":false,"id":834726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, D. 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The beach width was also computed as the cross-shore distance between the forecasted 2028 shoreline and dune-base positions. While it does not evaluate the suitability of a nesting beach or identify optimal nesting habitat, the beach width can be used as a proxy for habitat availability. An analysis was conducted along the Florida Atlantic coast with an initial goal of demonstrating a method that combines available data for shoreline and dune positions with a Kalman Filter algorithm developed to predict decadal-scale shoreline evolution and then uses these features to define future beach width. This section of the southeastern United States hosts the largest assemblage of nesting loggerhead sea turtles (Caretta caretta) in the world, in addition to other species, and critical habitat is designated as part of the species’ listing package under the Endangered Species Act of 1973 (16 U.S.C. ch. 35 § 1531 et seq) for most of the nesting beaches within the study area. This work introduces an approach to inform ecosystem services assessments using data typically derived for shoreline change and storm vulnerability models.
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600 4th Street South
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