Outbreaks of defoliating insects in low-diversity tropical forests occur infrequently but provide valuable insights about outbreak ecology in temperate environments and in general. We investigated an extensive outbreak of the endemic koa moth (Scotorythra paludicola), which defoliated endemic koa trees (Acacia koa) over a third of their range on Hawai‘i Island during 2013 and 2014. At Hakalau Forest National Wildlife Refuge, we observed the dynamics of the outbreak and its effects on host trees, nutrient cycling, and insectivorous consumers in reforestation stands of densely planted koa and in natural forest stands of mixed koa and ‘ōhi‘a (Metrosideros polymorpha). Contrary to predictions of the resource concentration hypothesis, caterpillar biomass and defoliation severity were greater in the natural forest sites, where koa density was relatively low. Caterpillars preferentially consumed the most palatable koa foliage type (phyllodes), and koa initially refoliated with the least palatable foliage type (true leaves). Lightly defoliated small trees refoliated more quickly than did heavily defoliated ones but the opposite was true for large trees, which also produced a greater proportion of phyllodes. Mortality was greatest for heavily defoliated small koa. Caterpillar frass caused larger increases in soil nitrogen (N) than phosphorus (P) availability, with the greatest N increases in fine-textured soils. Foliar N increased in alien grasses under koa canopies compared to grasses away from koa and to native woody understory species. Bird activity was influenced by ‘ōhi‘a flower abundance and the severity of koa defoliation; birds switched to outbreaking caterpillar prey, and they gained weight during the outbreak. Bat foraging times decreased during the outbreak, apparently because they became satiated quickly each night. Parasitoid wasps increased with caterpillar abundance but had little influence on outbreak dynamics. Reducing alien grass cover and increasing tree diversity would likely reduce the impacts of insect outbreaks and similar perturbations to native forests.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3926","usgsCitation":"Banko, P.C., Peck, R.W., Yelenik, S.G., Paxton, E.H., Bonaccorso, F., Montoya-Aiona, K., Hughes, R.F., and Perakis, S.S., 2022, Hypotheses and lessons from a native moth outbreak in a low-diversity, tropical rainforest: Ecosphere, v. 13, no. 2, p. 1-41, https://doi.org/10.1002/ecs2.3926.","productDescription":"e3926, 41 p.","startPage":"1","endPage":"41","ipdsId":"IP-080107","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":448722,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3926","text":"Publisher Index Page"},{"id":435958,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HE9WKK","text":"USGS data release","linkHelpText":"Hawaii Island insect response to koa moth (Scotorythra paludicola) outbreak, 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G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":835502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":835503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonaccorso, Frank 0000-0002-5490-3083 fbonaccorso@usgs.gov","orcid":"https://orcid.org/0000-0002-5490-3083","contributorId":143709,"corporation":false,"usgs":true,"family":"Bonaccorso","given":"Frank","email":"fbonaccorso@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":835504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montoya-Aiona, Kristina 0000-0002-1776-5443 kmontoya-aiona@usgs.gov","orcid":"https://orcid.org/0000-0002-1776-5443","contributorId":5899,"corporation":false,"usgs":true,"family":"Montoya-Aiona","given":"Kristina","email":"kmontoya-aiona@usgs.gov","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":835505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hughes, R. Flint","contributorId":140151,"corporation":false,"usgs":false,"family":"Hughes","given":"R.","email":"","middleInitial":"Flint","affiliations":[{"id":13397,"text":"USDA Forest Service, fhughes@fs.fed.us","active":true,"usgs":false}],"preferred":false,"id":835506,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":835507,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229156,"text":"70229156 - 2022 - DSWEmod - The production of high-frequency surface water map composites from daily MODIS images","interactions":[],"lastModifiedDate":"2022-04-12T13:36:29.136585","indexId":"70229156","displayToPublicDate":"2022-02-21T06:51:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"DSWEmod - The production of high-frequency surface water map composites from daily MODIS images","docAbstract":"Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high-frequency surface water mapping, we compared map results derived from Landsat (30-m) and MODIS (250-m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat-based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15-day time series, as compared to 32% of the monthly Landsat DSWE time series. The five-day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud-filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter-interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.
Spring floods trigger spawning in many native fishes of the desert Southwest (USA), but less is known about fish community response when native fishes are rare. Here, we document change to native and nonnative fish captures and instream habitat features following a decade-high flooding event (2019) in the Verde River (AZ) where native fish captures were rare in the years pre-flood. Using prepositioned areal electrofishing devices (PAEDs), we sampled the fish community at 90 sampling units pre-flood (2017) and resampled those same units post-flood (2019) to compare and identify changes to catch and habitat features. Relative abundance of native fishes increased from 0.6% pre-flood (0.01 fish/PAED) to 53.0% post-flood (1.66 fish/PAED) and was largely attributable to the presence of juvenile Roundtail Chub Gila robusta (≤ 70 mm total length (TL)) and juvenile Sonora Sucker Catostomus insignis (≤ 100 mm TL). Juvenile Desert Sucker Catostomus clarkii experienced a lesser increase. One adult native fish was captured in 2017 and adult native fishes were absent from 2019 sampling. The catch of adult/subadult Common Carp Cyprinus carpio (> 100 mm TL) declined; however, this could be related to reservoir management and not the flood. The abundance of all size-classes of Black Bass Micropterus spp., Red Shiner Cyprinella lutrensis and other nonnative fishes did not change. The majority (97%) of juvenile native fishes were captured at the uppermost sampling reach. A 54% reduction to canopy cover across all sampling reaches and an increase of fine sediments at the most downstream reach demonstrates how floods can restructure the river environment. This case-study adds evidence that protection of spring floods is vital to the persistence and recolonization of fishes native to the desert Southwest, especially where they are rare. The continued presence of nonnative species may preclude juvenile native fishes from recruiting to adults.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2021.2002734","usgsCitation":"Jenney, C.J., Nemec, Z.C., Lee, L.N., and Bonar, S.A., 2022, Increased juvenile native fish abundance following a major flood in an Arizona river: Journal of Freshwater Ecology, v. 37, no. 1, p. 1-14, https://doi.org/10.1080/02705060.2021.2002734.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-135120","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":448726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2021.2002734","text":"Publisher Index Page"},{"id":429646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.47741088388918,\n 33.945270025606774\n ],\n [\n -111.47741088388918,\n 34.92271952728409\n ],\n [\n -112.26906155680956,\n 34.92271952728409\n ],\n [\n -112.26906155680956,\n 33.945270025606774\n ],\n [\n -111.47741088388918,\n 33.945270025606774\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenney, Christopher J.","contributorId":288206,"corporation":false,"usgs":false,"family":"Jenney","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":902326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nemec, Zach C.","contributorId":288222,"corporation":false,"usgs":false,"family":"Nemec","given":"Zach","email":"","middleInitial":"C.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":902327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Larissa N.","contributorId":288223,"corporation":false,"usgs":false,"family":"Lee","given":"Larissa","email":"","middleInitial":"N.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":902328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902325,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240968,"text":"70240968 - 2022 - Occurrence and sources of lead in private wells, Sturbridge, Massachusetts","interactions":[],"lastModifiedDate":"2023-03-03T12:39:51.735588","indexId":"70240968","displayToPublicDate":"2022-02-20T06:36:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Occurrence and sources of lead in private wells, Sturbridge, Massachusetts","docAbstract":"Lead (Pb) occurrence and sources and aqueous geochemistry were assessed in private wellhead and tap water at a targeted area of concern for possible exceedances and at a control area in the same geologic formation, and in wells at a nearby landfill in south-central Massachusetts (MA). Total Pb concentrations were below the U.S. Environmental Protection Agency (USEPA) Action Level of 15 μg/L in all samples, and about 6% of unfiltered samples contained Pb concentrations that exceeded 1.0 μg/L. Pb concentrations were higher under conditions that are acidic and oxic (pH ≤ 6.5 and dissolved oxygen [DO] ≥ 2 mg/L), in which minerals that could sequester lead or manganese typically are undersaturated, and adsorption by hydrous ferric oxide is limited. Under more neutral to alkaline conditions, the precipitation of Pb in solid solution series minerals such as (Ca,Pb)CO3 and (Ba,Pb)SO4−2, and adsorption by amorphous ferric hydroxides, could limit Pb solubility in the bedrock aquifer or in the plumbing. The low Pb concentrations and the absence of distinctive Pb and strontium (Sr) isotope ratio patterns in samples indicate that a nearby landfill is not likely a significant Pb source. Dissolved concentrations of Pb, copper (Cu), and zinc (Zn) in tap samples were significantly greater than those in wellhead samples, indicating that some Pb is derived from plumbing. Wellhead or tap samples with the highest Pb concentrations also had the greatest corrosivity potential based on the calcite saturation index and the PPGC (Potential to Promote Galvanic Corrosion) and supports the premise that Pb concentrations in tap samples were derived partly from corrosion of plumbing. Concentrations of other constituents, including arsenic (As), uranium (U), Sr, boron (B), and lithium (Li) were not statistically different between the tap and wellhead samples but, apart from Sr, all were statistically higher in the control area than in the target area. This variation in constituent concentrations suggests geochemical variation within the host Paxton Formation, possibly related to faulting and contact with the Ayer granite east of the control area.
Behavioral interactions between conspecific animals can be influenced by relatedness and familiarity. Compared to other vertebrate taxa, considering such aspects of social behavior when housing captive reptiles has received less attention, despite the implications this could have for informing husbandry practices, enhancing welfare, and influencing outcomes of conservation translocations. In this study, to test how kinship and familiarity influenced social behavior in a reptile, we reared 16 captive-born Eastern Box Turtles (Terrapene carolina) under semi-natural conditions in four equally sized groups, where each group comprised pairs of siblings and non-siblings. Using separation distance between pairs of turtles in rearing enclosures as a measure of gregariousness, we found no evidence suggesting siblings more frequently interacted with one another compared to non-relatives over the first five months of life (β = -0.016, 95% CI: -0.117 to 0.084). Average pair separation distance decreased during this time (β = -0.146, 95% CI: -0.228 to -0.063) but may have been due to turtles aggregating around concentrated resources like heat and moist retreat areas as cold winter temperatures approached. When subject were eight months old, we measured repeated separation distances between unique pair combinations in an experimental environment and similarly found no support for gregariousness (associations) being influenced by kinship or familiarity (β = -1.554, 95% CI: -9.956 to 6.848). Additionally, differences in body size between pairs of turtles (β = -22.289, 95% CI: -68.448 to 23.870) nor the five-minute time interval during the 90-minute trial (P ≥ 0.18) had any apparent effect on associations. Agonistic interactions between individuals were never observed. Encouragingly, based on our results, group housing and rearing of juvenile box turtles did not appear to negatively impact their behavioral and physiological well-being. Unlike findings for other taxa, including some reptiles, our results suggest strategically housing groups of juvenile T. carolina to maintain social stability may not be an important husbandry consideration, or even a requirement, when planning releases of captive-reared individuals for conservation purposes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.applanim.2022.105586","usgsCitation":"Tetzlaff, S.J., Sperry, J.H., and DeGregorio, B.A., 2022, You can go your own way: No evidence for social behavior based on kinship or familiarity in captive juvenile box turtles: Applied Animal Behaviour Science, v. 248, 105586, 5 p., https://doi.org/10.1016/j.applanim.2022.105586.","productDescription":"105586, 5 p.","ipdsId":"IP-136145","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":448731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.applanim.2022.105586","text":"Publisher Index Page"},{"id":432047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tetzlaff, Sasha J.","contributorId":341593,"corporation":false,"usgs":false,"family":"Tetzlaff","given":"Sasha","email":"","middleInitial":"J.","affiliations":[{"id":81758,"text":"US Army ERDC-CERL","active":true,"usgs":false}],"preferred":false,"id":908665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sperry, Jinelle H.","contributorId":341594,"corporation":false,"usgs":false,"family":"Sperry","given":"Jinelle","email":"","middleInitial":"H.","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":908666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908667,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209990,"text":"70209990 - 2022 - Climate change and fishes in estuaries","interactions":[],"lastModifiedDate":"2022-10-05T16:09:41.785456","indexId":"70209990","displayToPublicDate":"2022-02-18T10:50:55","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7","title":"Climate change and fishes in estuaries","docAbstract":"This chapter provides an overview of the main drivers of change in estuarine systems, their expected causes and impacts on estuarine fish and fisheries. An analysis of global, regional and local patterns of estuarine fish and how climate-induced change may impact estuarine systems and their fish communities is provided. We also examine the main environmental, climatic and biological stressors likely to impact estuarine fish and associated fisheries. A set of case studies is used to illustrate the differences in potential impacts associated with various global regions and types of estuaries. An understanding of climate change in estuaries will support estuarine ecosystem resilience, inform management and facilitate adaptation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Fish and fisheries in estuaries: A global perspective","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781119705345.ch7","usgsCitation":"Gillanders, B.M., McMillan, M.N., Reis-Santos, P., Baumgartner, L.J., Brown, L.R., Conallin, J., Feyrer, F.V., Henriques, S., James, N.C., Jaureguizar, A.J., Pessanha, A.L., Vasconcelos, R.P., Vu, A., Walther, B., and Wibowo, A., 2022, Climate change and fishes in estuaries, chap. 7 of Fish and fisheries in estuaries: A global perspective, p. 380-457, https://doi.org/10.1002/9781119705345.ch7.","productDescription":"78 p.","startPage":"380","endPage":"457","ipdsId":"IP-116180","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":407966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gillanders, Bronwyn M","contributorId":291280,"corporation":false,"usgs":false,"family":"Gillanders","given":"Bronwyn","email":"","middleInitial":"M","affiliations":[{"id":62654,"text":"School of Biological Sciences, and Environment Institute, University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":853893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMillan, Matthew N.","contributorId":297357,"corporation":false,"usgs":false,"family":"McMillan","given":"Matthew","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":853894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reis-Santos, P.","contributorId":93283,"corporation":false,"usgs":true,"family":"Reis-Santos","given":"P.","email":"","affiliations":[],"preferred":false,"id":853895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baumgartner, Lee J.","contributorId":203990,"corporation":false,"usgs":false,"family":"Baumgartner","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36787,"text":"Charles Sturt University, Institute for Land, Water, and Society","active":true,"usgs":false}],"preferred":false,"id":853896,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788727,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conallin, John","contributorId":220478,"corporation":false,"usgs":false,"family":"Conallin","given":"John","email":"","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":853897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788726,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henriques, Sofia","contributorId":297358,"corporation":false,"usgs":false,"family":"Henriques","given":"Sofia","email":"","affiliations":[],"preferred":false,"id":853898,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"James, Nicola C.","contributorId":297359,"corporation":false,"usgs":false,"family":"James","given":"Nicola","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":853899,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jaureguizar, Andres J","contributorId":297360,"corporation":false,"usgs":false,"family":"Jaureguizar","given":"Andres","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":853900,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pessanha, Andre L. 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South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname.
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Carolina\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: February 18, 2022; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Reliable estimates of the magnitude of peak flows in streams are required for the economical and safe design of transportation and water conveyance structures. In addition, reliable estimates of the magnitude of low flows at defined frequencies and durations are needed for meeting regulatory requirements, quantifying base flows in streams and rivers, and evaluating time of travel and dilution of toxic spills. This report, in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey, presents methods for estimating the magnitude of peak flows and low flows at defined frequencies and durations on nontidal streams in Delaware, at locations both monitored by streamflow-gage sites and ungaged. Methods are presented for estimating (1) the magnitude of peak flows for return periods ranging from 2 to 500 years (50-percent to 0.2-percent annual-exceedance probability), and (2) the magnitude of low flows as applied to 7-, 14-, and 30-consecutive day low-flow periods with recurrence intervals of 2, 10, and 20 years (50-, 10-, and 5-percent annual non-exceedance probabilities). These methods are applicable to watersheds that exhibit a full range of development conditions in Delaware. The report also describes StreamStats, a web application that allows users to easily obtain peak-flow and low-flow magnitude estimates for user-selected locations in Delaware.
Peak-flow and low-flow magnitude estimates for ungaged sites are obtained using statistical regression analysis through a process known as regionalization, where information from a group of streamflow-gage sites within a region forms the basis for estimates for ungaged sites within the same region. Ninety-four streamflow-gage sites in and near Delaware with at least 10 years of nonregulated annual peak-flow data were used for the peak-flow regression analysis, a subset of the 121 sites for which peak-flow estimates were computed. These sites included both continuous-record streamflow-gage sites as well as partial record sites. Forty-five streamflow-gage sites with at least 10 years of nonregulated low-flow data available were used for the low-flow regression analyses, a subset of the 68 sites for which low-flow estimates were computed. Estimates for gaged sites are obtained by combining (1) the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates using the recent Bulletin 17C guidelines that incorporate the Expected Moments Algorithm with (2) regional estimates of peak-flow magnitude derived from regional regression equations and regional skew derived from sites with records greater than or equal to 35 years. Example peak-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Estimates for low-flow gaged sites are obtained by combining (1) the station low-flow statistics (mean, standard deviation, and skew) and low-flow estimates with (2) regional estimates of low-flow magnitude derived from regional regression equations. Example low-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. A total of 54 sites in the Coastal Plain region were used to develop peak-flow regressions for the region and 40 sites were used for the Piedmont region. Similarly, 24 sites were used for low-flow regression equation development in the Coastal Plain, with 21 in the Piedmont. Peak and low-flow site inclusion in the Coastal Plain tended to be more restricted with tidal influence and ranges of basin characteristics, including drainage area, limiting regression equation development and application.
Regional regression equations for peak flows and low flows, as applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces in Delaware, are presented. Peak-flow regression equations used variables that quantified drainage area, basin slope, percent area with well-drained soils, percent area with poorly drained soils, impervious area, and percent area of surface water storage in estimating peak-flow estimates, whereas low-flow regression equations used only drainage area and percent poorly drained soils in the estimation of low flows. Average standard errors for peak-flow regressions tended to be lower than those for low- flow regressions, with lower errors in the Piedmont region for both peak- and low-flow regressions. For peak-flow estimates, a sensitivity analysis of Piedmont regression equation estimates to changes in impervious area is also presented.
Additional topics associated with the analyses performed during the study are discussed, including (1) the availability and description of 32 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites and inclusion in or exclusion from the regional analysis; (3) regional skew analysis and determination of regression regions; (4) sample adjustments and removal of sites owing to regulation and redundancy; and (5) a brief comparison of peak- and low-flow estimates at gages used in previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225005","collaboration":"Prepared in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey","usgsCitation":"Hammond, J.C., Doheny, E.J., Dillow, J.J.A., Nardi, M.R., Steeves, P.A., and Warner, D.L., 2022, Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware: U.S. Geological Survey Scientific Investigations Report 2022–5005, 46 p., https://doi.org/10.3133/sir20225005.","productDescription":"Report: vi, 46 p.; 4 Data Releases","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127314","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":502293,"rank":10,"type":{"id":36,"text":"NGMDB Index 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Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
The San Gorgonio Pass region of southern California is a locus of extensive Quaternary deformation within a multi-strand section of the San Andreas fault zone. The geomorphology of the San Gorgonio Pass region reflects the complicated history of geologic events in the formation of this structurally complex region. We define fault-bounded blocks in San Gorgonio Pass and focus on two that are characterized by extensive crystalline bedrock outcrops with similar bedrock lithologies. These two blocks are separated by the San Bernardino strand of the San Andreas fault. Morphometric variables, including local relief, slope, slope distribution, and surface roughness, consistently demonstrate distinctions between the bedrock upland regions of the two blocks. Geologic observations of the region highlight differences in Quaternary units within the two blocks, reflective of the differing surficial processes active in each block. Within the Kitching Peak block, the morphology highlights a lineament that we informally name the Lion Canyon lineament. This boundary more clearly differentiates the two regions, as compared to the mapped San Bernardino strand, and may represent the previously active strand or bounding structure in this section. The distinction in morphology and surficial processes leads to our interpretation that the Kitching Peak and Pisgah Peak blocks have experienced different uplift histories. This further leads to the conclusion that the San Bernardino strand, broadly defined, has been integrated, at some point in the past, with the Banning strand, allowing for through-going rupture along the fault system. This connectivity may have occurred along the Burro Flats section of the San Bernardino strand or the Lion Canyon lineament. The fault connection along the mapped trace of the San Bernardino strand is not currently evident at the surface, however, suggesting that the integration has been disrupted. We propose this is due to intervals of N-S compression in the region, manifest as slip along the San Gorgonio Pass fault zone and other regional faults. We present evidence for lateral displacement along the San Bernardino and Banning strands of the San Andreas fault, discuss the implications of these displacements, and propose a sequence of fault activity, including multiple phases of activity along the San Bernardino and Banning strand pathway to account for the structural complexity and lack of surficial fault continuity.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02424.1","usgsCitation":"Kendrick, K., Matti, J., and Barth, N., 2022, Geologic and geomorphic evidence for multi-phase history of strands of the San Andreas fault through the San Gorgonio Pass structural knot, southern California: Geosphere, v. 18, no. 2, p. 424-457, https://doi.org/10.1130/GES02424.1.","productDescription":"34 p.","startPage":"424","endPage":"457","ipdsId":"IP-126613","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02424.1","text":"Publisher Index Page"},{"id":482564,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Gorgonio Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.4974257755139,\n 34.476943268181415\n ],\n [\n -117.4974257755139,\n 33.51535234501618\n ],\n [\n -115.60686222272504,\n 33.51535234501618\n ],\n [\n -115.60686222272504,\n 34.476943268181415\n ],\n [\n -117.4974257755139,\n 34.476943268181415\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Kendrick, Katherine J.","contributorId":351566,"corporation":false,"usgs":true,"family":"Kendrick","given":"Katherine J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matti, Jonathan C.","contributorId":351567,"corporation":false,"usgs":true,"family":"Matti","given":"Jonathan C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":928934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barth, Nicholas C","contributorId":351568,"corporation":false,"usgs":false,"family":"Barth","given":"Nicholas C","affiliations":[{"id":13325,"text":"University of California Riverside","active":true,"usgs":false}],"preferred":false,"id":928935,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228913,"text":"70228913 - 2022 - Partitioning ground motion uncertainty when conditioned on station data","interactions":[],"lastModifiedDate":"2022-03-28T16:55:10.476242","indexId":"70228913","displayToPublicDate":"2022-02-17T17:59:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Partitioning ground motion uncertainty when conditioned on station data","docAbstract":"Rapid estimation of earthquake ground shaking and proper accounting of associated uncertainties in such estimates when conditioned on strong‐motion station data or macroseismic intensity observations are crucial for downstream applications such as ground failure and loss estimation. The U.S. Geological Survey ShakeMap system is called upon to fulfill this objective in light of increased near‐real‐time access to strong‐motion records from around the world. Although the station data provide a direct constraint on shaking estimates at specific locations, these data also heavily influence the uncertainty quantification at other locations. This investigation demonstrates methods to partition the within‐ (phi) and between‐event (tau) uncertainty estimates under the observational constraints, especially when between‐event uncertainties are heteroscedastic. The procedure allows the end users of ShakeMap to create separate between‐ and within‐event realizations of ground‐motion fields for downstream loss modeling applications in a manner that preserves the structure of the underlying random spatial processes.
","language":"English","publisher":"Seismological Society of America.","doi":"10.1785/0120210177","usgsCitation":"Engler, D.T., Worden, C., Thompson, E.M., and Jaiswal, K.S., 2022, Partitioning ground motion uncertainty when conditioned on station data: Bulletin of the Seismological Society of America, v. 112, no. 2, p. 1060-1079, https://doi.org/10.1785/0120210177.","productDescription":"20 p.","startPage":"1060","endPage":"1079","ipdsId":"IP-133182","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":396463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":835872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Worden, Charles 0000-0003-1181-685X cbworden@usgs.gov","orcid":"https://orcid.org/0000-0003-1181-685X","contributorId":152042,"corporation":false,"usgs":true,"family":"Worden","given":"Charles","email":"cbworden@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":835873,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":835874,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":835875,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228692,"text":"70228692 - 2022 - Demographic implications of lead poisoning for eagles across North America","interactions":[],"lastModifiedDate":"2022-02-22T16:29:21.509329","indexId":"70228692","displayToPublicDate":"2022-02-17T14:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Demographic implications of lead poisoning for eagles across North America","docAbstract":"Lead poisoning occurs worldwide in populations of predatory birds, but exposure rates and population impacts are known only from regional studies. We evaluated the lead exposure of 1210 bald and golden eagles from 38 US states across North America, including 620 live eagles. We detected unexpectedly high frequencies of lead poisoning of eagles, both chronic (46 to 47% of bald and golden eagles, as measured in bone) and acute (27 to 33% of bald eagles and 7 to 35% of golden eagles, as measured in liver, blood, and feathers). Frequency of lead poisoning was influenced by age and, for bald eagles, by region and season. Continent-wide demographic modeling suggests that poisoning at this level suppresses population growth rates for bald eagles by 3.8% (95% confidence interval: 2.5%, 5.4%) and for golden eagles by 0.8% (0.7%, 0.9%). Lead poisoning is an underappreciated but important constraint on continent-wide populations of these iconic protected species.
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Previous geologic map compilations predate recent geologic mapping efforts that have contributed new insights into the stratigraphy and structure of this quadrangle. This new map, compiled in collaboration with the Arizona Geological Survey, incorporates these recent mapping efforts to provide an updated depiction of the quadrangle’s geologic framework. The scope of this map is limited to bedrock units of Miocene and older age because younger deposits have not been mapped in enough detail across the quadrangle to support a systematic compilation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211097","collaboration":"Prepared in Cooperation with the Arizona Geological Survey","usgsCitation":"Stone, P., Spencer, J.E., and Beard, L.S., comps., 2022, Preliminary bedrock geologic map of the Blythe 30' x 60' quadrangle, California and Arizona: U.S. Geological Survey Open-File Report 2021–1097, 1 sheet, scale 1:100,000, 10-p. pamphlet, https://doi.org/10.3133/ofr20211097.","productDescription":"Report: iv, 10 p., 1 Sheet; 50.42 x 33.06: Data Release","numberOfPages":"10","additionalOnlineFiles":"Y","ipdsId":"IP-100416","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":396064,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YUU64Z","description":"Stone, P., Spencer, J.E., and Beard, L.S., 2022, Digital data for the preliminary bedrock geologic map of the Blythe 30’ x 60’ quadrangle, California and Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9YUU64Z.","linkHelpText":"Digital data for the preliminary bedrock geologic map of the Blythe 30’ x 60’ quadrangle, California and Arizona"},{"id":501529,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112445.htm","linkFileType":{"id":5,"text":"html"}},{"id":396063,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2021/1097/ofr20211097_sheet.pdf","size":"20 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1097/ofr20211097_pamphlet.pdf","text":"Pamphlet","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1097/covrthb.jpg"}],"country":"United States","state":"Arizona, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.00,\n 33.30\n ],\n [\n -114.00,\n 33.30\n ],\n [\n -114.00,\n 34.00\n ],\n [\n -115.00,\n 34.00\n ],\n [\n -115.00,\n 33.30\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Surveys and monitoring for the endangered Least Bell’s Vireo (Vireo bellii pusillus; vireo) were done at the San Luis Rey Flood Risk Management Project Area (Project Area) in the city of Oceanside, San Diego County, California, between April 4 and August 4, 2021. We completed four protocol surveys during the breeding season, supplemented by weekly territory monitoring visits. We identified a total of 122 territorial male vireos; 111 were confirmed as paired and 8 were confirmed as single males. For the remaining three territories, we were unable to confirm pair status. Five transient vireos were detected in 2021. The vireo population in the Project Area decreased by 24 percent from 2020 to 2021. Vireo populations decreased across San Diego County, with a 14-percent decrease documented at Marine Corps Base Camp Pendleton (MCBCP); a 5-percent decrease on the Otay River; a 6-percent decrease on the middle San Luis Rey River; and a 44-percent decrease at Marine Corps Air Station (although this decrease was likely exaggerated by large-scale vegetation clearing that occurred prior to the 2021 breeding season).
We used an index of treatment (Treatment Index) to evaluate the impact of on-going vegetation clearing on the Project Area vireo population. The Treatment Index measures the cumulative effect of vegetation treatment within a territory (since 2005) by using the percent area treated weighted by the number of years since treatment. We found that the Treatment Index for unoccupied habitat was more than two times that of occupied habitat, indicating that vireos selected less treated habitat in which to settle.
We monitored vireo nests at three general site types: (1) within the flood channel where exotic and native vegetation removal has occurred regularly (Channel), (2) three sites next to the flood channel where limited exotic and native vegetation removal has occurred (Off-channel), and (3) three sites that have been actively restored by planting native vegetation (Restoration). Nesting activity was monitored in 85 territories, 8 of which were occupied by single males. Of the completed nests, 39 percent were successful, and nest success did not differ among the three sites. Clutch size was greater in the Channel than the Off-channel sites, and the proportion of hatchlings that fledged was greater in Off-channel sites than Channel and Restoration sites. There were no other nest-level differences detected among site types, nor were there any differences in territory-level measures of productivity (young fledged per pair, double-brooding) among the sites. Overall, breeding success and productivity were slightly lower in 2021 than in 2020, with 66 percent of pairs fledgling at least one young and pairs fledging an average of 1.9±1.7 young.
To investigate if the cumulative years of treatment had an impact on vireo reproductive effort, we looked at the effects of the Treatment Index on reproductive parameters. Results from generalized linear models indicated that treatment did not have an effect on vireo nesting effort or the number of vireo fledglings per pair produced in 2021. Similarly, our analysis of nest survival for 2021 revealed no effect of Treatment Index on daily survival rate.
Analysis of vegetation data collected at vireo nests from 2006 to 2021 did not indicate an effect of vegetation at the nest on daily survival rate. We also found no differences in nest-placement characteristics among site types or successful/unsuccessful nests.
Red/arroyo willow (Salix laevigata or Salix lasiolepis) was the species most commonly selected for nesting by vireos in all three site types. Black willow (Salix gooddingii) and mule fat (Baccharis salicifolia) also were commonly used. Vireos used a wider variety of species for nesting in Channel and Off-channel sites (seven and eight species, respectively) compared with Restoration sites (three species).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221012","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Los Angeles District","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Houston, Alexandra , Allen, L.D., Pottinger, R.E., and Kus, B.E., 2022, Least Bell's Vireos and Southwestern Willow Flycatchers at the San Luis Rey flood risk management project area in San Diego County, California: Breeding activities and habitat use—2021 Annual report: U.S. Geological Survey Open-File Report 2022–1012, 79 p., https://doi.org/10.3133/ofr20221012.","productDescription":"viii, 79 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-135579","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":396128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1012/covrthb.jpg"},{"id":396129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1012/ofr20221012.pdf","text":"Report","size":"7 Mb"},{"id":396130,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1012/ofr20221012.xml"},{"id":396131,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1012/images"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.41500854492188,\n 33.19301824551205\n ],\n [\n -117.17056274414064,\n 33.19301824551205\n ],\n [\n -117.17056274414064,\n 33.288350918671775\n ],\n [\n -117.41500854492188,\n 33.288350918671775\n ],\n [\n -117.41500854492188,\n 33.19301824551205\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.
","language":"English","publisher":"BMC","doi":"10.1186/s13750-022-00258-y","usgsCitation":"Balbi, S., Bagstad, K.J., Magrach, A., Sanz, M.J., Aguilar-Amuchastegui, N., Guipponi, C., and Villa, F., 2022, The global environmental agenda urgently needs a semantic web of knowledge: Environmental Evidence, v. 11, 5, 6 p., https://doi.org/10.1186/s13750-022-00258-y.","productDescription":"5, 6 p.","ipdsId":"IP-126413","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":448740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-022-00258-y","text":"Publisher Index Page"},{"id":396173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Balbi, Stefano 0000-0001-8190-5968","orcid":"https://orcid.org/0000-0001-8190-5968","contributorId":208481,"corporation":false,"usgs":false,"family":"Balbi","given":"Stefano","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":835326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":835327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magrach, Ainhoa 0000-0003-2155-7556","orcid":"https://orcid.org/0000-0003-2155-7556","contributorId":208482,"corporation":false,"usgs":false,"family":"Magrach","given":"Ainhoa","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":835328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanz, Maria Jose 0000-0003-0471-3094","orcid":"https://orcid.org/0000-0003-0471-3094","contributorId":279661,"corporation":false,"usgs":false,"family":"Sanz","given":"Maria","email":"","middleInitial":"Jose","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":835329,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aguilar-Amuchastegui, Naikoa 0000-0002-5072-0079","orcid":"https://orcid.org/0000-0002-5072-0079","contributorId":279662,"corporation":false,"usgs":false,"family":"Aguilar-Amuchastegui","given":"Naikoa","email":"","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":835330,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guipponi, Carlo","contributorId":279664,"corporation":false,"usgs":false,"family":"Guipponi","given":"Carlo","email":"","affiliations":[{"id":47673,"text":"Ca’ Foscari University of Venice","active":true,"usgs":false}],"preferred":false,"id":835331,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Villa, Ferdinando 0000-0002-5114-3007","orcid":"https://orcid.org/0000-0002-5114-3007","contributorId":208486,"corporation":false,"usgs":false,"family":"Villa","given":"Ferdinando","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":835332,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231644,"text":"70231644 - 2022 - Pollutant co-attenuation via in-stream interactions between mine drainage and municipal wastewater","interactions":[],"lastModifiedDate":"2022-05-18T14:00:27.633554","indexId":"70231644","displayToPublicDate":"2022-02-17T08:57:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Pollutant co-attenuation via in-stream interactions between mine drainage and municipal wastewater","docAbstract":"Municipal wastewater (MWW) and mine drainage (MD) are common co-occurring sources of freshwater pollution in mining regions. The physicochemical interactions that occur after mixing MWW and MD in a waterway may improve downstream water quality of an impaired reach by reducing downstream concentrations of nutrients and metals (i.e., “co-attenuation”). A first-order stream (Bradley Run in central Pennsylvania), with coal MD and secondarily treated MWW entering the stream in the same location, was systematically monitored to determine in-stream water-quality dynamics. Monitored constituents included pH, nutrients (i.e., phosphorus and nitrogen), and metals (e.g., iron, aluminum, manganese). Mixing of the MWW, MD, and upstream water decreased concentrations of phosphate, aluminum, and iron by 94%, 91%, and 98%, respectively, relative to conservative mixtures at the 1400-m-downstream site. The pollutant co-attenuation resulted in water quality equivalent to that upstream of the pollutant sources and improved the phosphorus-based trophic status of the stream. Geochemical models indicate the primary mechanisms for P attenuation in the studied stream were precipitation as variscite (AlPO4:2H2O) or amorphous AlPO4 plus adsorption to hydrous ferric oxide, despite a much greater abundance of hydrous aluminum oxide. The results presented in this study suggest that in-stream mixing of MD with untreated or secondarily treated MWW may be an important, overlooked factor affecting downstream transport of common pollutants in mining regions. Decreased metals loading and increased pH resulting from natural attenuation and remediation of MD could affect the potential for retention of phosphate by stream sediment and could lead to the release of nutrients from legacy accumulations, highlighting the potential need to address high-nutrient discharges (e.g., improved MWW treatment) in concert with MD remediation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2022.118173","usgsCitation":"Spellman, C.J., Smyntek, P.M., Cravotta, C., Tasker, T.L., and Strosnider, W.H., 2022, Pollutant co-attenuation via in-stream interactions between mine drainage and municipal wastewater: Water Research, v. 214, 118173, 10 p., https://doi.org/10.1016/j.watres.2022.118173.","productDescription":"118173, 10 p.","ipdsId":"IP-134190","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":400756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"214","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Spellman, Charles J.","contributorId":291844,"corporation":false,"usgs":false,"family":"Spellman","given":"Charles","email":"","middleInitial":"J.","affiliations":[{"id":62771,"text":"Department of Civil and Environmental Engineering, University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":843213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smyntek, Peter M.","contributorId":291642,"corporation":false,"usgs":false,"family":"Smyntek","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":843214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tasker, Travis L.","contributorId":211456,"corporation":false,"usgs":false,"family":"Tasker","given":"Travis","email":"","middleInitial":"L.","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":843216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strosnider, William H. J.","contributorId":291845,"corporation":false,"usgs":false,"family":"Strosnider","given":"William","email":"","middleInitial":"H. J.","affiliations":[{"id":62772,"text":"Baruch Institute for Marine and Coastal Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":843217,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228754,"text":"70228754 - 2022 - Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics","interactions":[],"lastModifiedDate":"2022-02-18T14:08:59.596133","indexId":"70228754","displayToPublicDate":"2022-02-17T08:03:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10120,"text":"npj Biofilms and Microbiomes","active":true,"publicationSubtype":{"id":10}},"title":"Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics","docAbstract":"Environmentally relevant metagenomes and BONCAT-FACS derived translationally active metagenomes from Powder River Basin coal seams were investigated to elucidate potential genes and functional groups involved in hydrocarbon degradation to methane in coal seams with high- and low-sulfate levels. An advanced subsurface environmental sampler allowed the establishment of coal-associated microbial communities under in situ conditions for metagenomic analyses from environmental and translationally active populations. Metagenomic sequencing demonstrated that biosurfactants, aerobic dioxygenases, and anaerobic phenol degradation pathways were present in active populations across the sampled coal seams. In particular, results suggested the importance of anaerobic degradation pathways under high-sulfate conditions with an emphasis on fumarate addition. Under low-sulfate conditions, a mixture of both aerobic and anaerobic pathways was observed but with a predominance of aerobic dioxygenases. The putative low-molecular-weight biosurfactant, lichysein, appeared to play a more important role compared to rhamnolipids. The methods used in this study—subsurface environmental samplers in combination with metagenomic sequencing of both total and translationally active metagenomes—offer a deeper and environmentally relevant perspective on community genetic potential from coal seams poised at different redox conditions broadening the understanding of degradation strategies for subsurface carbon.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41522-022-00267-2","usgsCitation":"Schweitzer, H.S., Smith, H.J., Barnhart, E.P., McKay, L.J., Gerlach, R., Cunningham, A.B., Malmstrom, R.R., Goudeau, D., and Fields, M.W., 2022, Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics: npj Biofilms and Microbiomes, v. 8, 7, 10 p., https://doi.org/10.1038/s41522-022-00267-2.","productDescription":"7, 10 p.","ipdsId":"IP-126104","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":448742,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41522-022-00267-2","text":"Publisher Index Page"},{"id":396170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKay, Luke J.","contributorId":268349,"corporation":false,"usgs":false,"family":"McKay","given":"Luke","email":"","middleInitial":"J.","affiliations":[{"id":55631,"text":"Center for Biofilm Engineering, Montana State University, Bozeman","active":true,"usgs":false}],"preferred":false,"id":835321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cunningham, Alfred B.","contributorId":172389,"corporation":false,"usgs":false,"family":"Cunningham","given":"Alfred","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":835323,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Malmstrom, Rex R.","contributorId":268350,"corporation":false,"usgs":false,"family":"Malmstrom","given":"Rex","email":"","middleInitial":"R.","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":835324,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goudeau, Danielle","contributorId":268351,"corporation":false,"usgs":false,"family":"Goudeau","given":"Danielle","email":"","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":835405,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":835325,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237304,"text":"70237304 - 2022 - Effects of weather variation on waterfowl migration: Lessons from a continental-scale generalizable avian movement and energetics model","interactions":[],"lastModifiedDate":"2022-10-07T12:24:33.871983","indexId":"70237304","displayToPublicDate":"2022-02-17T07:19:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Effects of weather variation on waterfowl migration: Lessons from a continental-scale generalizable avian movement and energetics model","docAbstract":"We developed a continental energetics-based model of daily mallard (Anas platyrhynchos) movement during the non-breeding period (September to May) to predict year-specific migration and overwinter occurrence. The model approximates movements and stopovers as functions of metabolism and weather, in terms of temperature and frozen precipitation (i.e., snow). The model is a Markov process operating at the population level and is parameterized through a review of literature. We applied the model to 62 years of daily weather data for the non-breeding period. The average proportion of available habitat decreased as weather severity increased, with mortality decreasing as the proportion of available habitat increased. The most commonly used locations during the course of the non-breeding period were generally consistent across years, with the most inter-annual variation present in the overwintering area. Our model revealed that the distribution of mallards on the landscape changed more dramatically when the variation in daily available habitat was greater. The main routes for avian migration in North America were predicted by our simulations: the Atlantic, Mississippi, Central, and Pacific flyways. Our model predicted an average of 77.4% survivorship for the non-breeding period across all years (range = 76.4%–78.4%), with lowest survivorship during autumn (90.5 ± 1.4%), intermediate survivorship in winter (91.8 ± 0.7%), and greatest survivorship in spring (93.6 ± 1.1%). We provide the parameters necessary for exploration within and among other taxa to leverage the generalizability of this migration model to a broader expanse of bird species, and across a range of climate change and land use/land cover change scenarios.
Metals and organic contaminants in aquatic systems affect the coupling of aquatic and terrestrial ecosystems through two pathways: contaminant-induced effects on insect emergence and emergence-induced contaminant transfer. Consequently, the impact of aquatic contaminants on terrestrial ecosystems can be driven by modifications in the quantity and quality of adult aquatic insects serving as prey or contaminants entering terrestrial food webs as part of the diet of terrestrial predators. Here, we provide an overview of recent advances in the field, separating metals from organic contaminants due to their differential propensity to bioaccumulate and thus their potential contribution to either of the two pathways. Finally, this review highlights the knowledge gap in the relative impact of these pathways on terrestrial insectivores.
Although forb-rich plantings for pollinator habitat are becoming more common, natural resource managers lack insight on the cost-effectiveness of forbs seeded in private land conservation programs. Additional information on the possible benefits of including more forb species in a mix may help guide the design of future pollinator habitat. We examined the detection of blooming forbs from seed mixes, colonization of non-seeded blooming forbs, and bee utilization of forbs on private lands enrolled in United States Department of Agriculture (USDA) conservation programs. By obtaining the original seed mixes used on conservation program lands and sampling forbs and bees, we provide a first-hand evaluation of the cost-effectiveness of USDA conservation plantings in a field setting. We identified seeded forbs with the highest blooming detection rates and the most common colonizing forbs across 27 sites, the majority of which were considered young conservation plantings. We additionally designated 16 forbs as the most cost-effective due to their higher-than-average blooming detection rate and lower-than-average seed cost. We found a positive association between seed mix richness and number of blooming, seeded forbs detected and found a negative association with blooming, non-native colonizing forbs, which highlights potential benefits of increasing forb richness in seed mixes. However, we did not observe an association between seed mix richness and wild bee or honey bee counts. Examining the cost-effectiveness of seeded forbs on USDA conservation enrollments and subsequent bee utilization can inform land managers in designing seed mixes for pollinator habitat.
Deep oceanic overturning circulation in the Atlantic (Atlantic Meridional Overturning Circulation (AMOC)) is projected to decrease in the future in response to anthropogenic warming. Caesar et al.1 argue that an AMOC slowdown started in the nineteenth century and intensified during the mid-twentieth century. Although the argument and selected evidence proposed have some merits, we find that their conclusions might be different if a more complete array of data available in the North Atlantic region is considered. We argue that the strength of AMOC over recent centuries is still poorly constrained and the expected slowdown may not have started yet.
Recently, Moffa-Sánchez et al.2 compiled a comprehensive set of palaeoclimate proxy data from the North Atlantic and Arctic regions using objective criteria to identify high-quality datasets of ocean conditions that span the past two millennia (Fig. 1). Although no direct (singular) proxy for AMOC exists, the palaeoceanographic proxy data compiled by Moffa-Sánchez et al.2 highlight the spatial and temporal complexities of the ocean state in modern times and the recent past. When all the available proxy records potentially related to AMOC variability and twentieth century observational datasets are considered, the time history of the AMOC system becomes less certain. In contrast, selecting only a subset of proxy records that share similar trends, as performed by Caesar et al.1, provides an incomplete perspective on AMOC changes through time.
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport. We used historical ignition data (1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
Reservoirs are examples of novel ecosystems that have developed into popular sport fisheries; these systems are also becoming seasonal refuges for migratory birds, including the piscivorous American white pelican (Pelecanus erythrorhynchos). We (1) investigated the proportion of cutthroat trout (Oncorhynchus clarkii utah) and other species consumed by pelicans, (2) compared whether pelicans have an adverse effect on cutthroat trout spawning movement, and (3) determined the importance of pelican predation relative to other sources of fish mortality in Strawberry Reservoir, Utah, USA. Pelican diet samples consisted of >85% Utah sucker (Catostomus ardens), 6% Utah chub (Gila atraria), and 3% cutthroat trout. Based on bioenergetics, we estimated that pelicans consumed 384–3020 individual cutthroat trout (0.17%–1.3% of population), in contrast to 14 166–27 196 nongame sucker and chub. Bird-related mortality rate ranged from 1% to 6% for stocked, PIT (passive integrated transponder)-tagged adult cutthroat trout and from 4% to 16% for subadult cutthroat trout. Based on these results, any management action taken to control pelican predation would need to be weighed against the benefits of nongame fish removal and the protected status of pelicans.
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At the Sheep Creek site on the southernmost Warm Springs section, two earthquakes occurred at 9.8−14.0 ka (95% confidence) and 6.5−7.1 ka; each had ∼1.9 m of vertical displacement. About 4 km to the southeast, across the Willow Creek Hills, two ruptures at the Arentson Gulch site on the northernmost Thousand Springs section occurred at 9.0−14.7 ka and 6.1−7.5 ka with ∼1.9 m of vertical displacement each. We synthesize these and previous paleoseismic results into a model of five postglacial (<15 ka) ruptures along a ∼65 km reach of the northern Lost River fault zone. Our results show that the Borah Peak earthquake (34 km; 0.9 m mean displacement) was unique compared to previous ruptures that had both longer and shorter rupture lengths (∼25−38 km), more displacement (mean of ∼1.3−1.4 m), and equal or greater magnitude (Mw 6.9−7.1) than that in the 1983 earthquake. These ruptures support a hypothesis of variable rupture length and displacement on the northern Lost River fault zone and show that predecessors to the 1983 rupture have passed unimpeded through the Willow Creek Hills. Our work demonstrates that normal faults are capable of producing variable spatial-temporal patterns of rupture that, together with comparisons of fault geometry and historical rupture length, improve our understanding of fault segmentation and help inform models of earthquake rupture probability.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36144.1","usgsCitation":"DuRoss, C., Briggs, R.W., Gold, R.D., Hatem, A.E., Elliott, A.J., Delano, J., Medina-Cascales, I., Gray, H., Mahan, S.A., Nicovich, S., Lifton, Z., Kleber, E.J., McDonald, G.N., Hiscock, A., Bunds, M., and Reitman, N.G., 2022, How similar was the 1983 Mw 6.9 Borah Peak earthquake rupture to its surface-faulting predecessors along the northern Lost River fault zone (Idaho, USA)?: Geological Society of America Bulletin, v. 134, no. 11-12, p. 2767-2789, https://doi.org/10.1130/B36144.1.","productDescription":"23 p.","startPage":"2767","endPage":"2789","ipdsId":"IP-132673","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":448759,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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