We model the kinematic rupture process of the 2019
A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared.
For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft.
The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells.
Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed.
In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205036","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2020, Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts: U.S. Geological Survey Scientific Investigations Report 2020–5036, 45 p., https://doi.org/10.3133/sir20205036.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103689","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375551,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NM2PHP","text":"USGS data release","linkHelpText":"Data on well characteristics and well-pair characteristics for estimating high groundwater levels in selected areas of Massachusetts"},{"id":375553,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5036/sir20205036.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5036"},{"id":375554,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5036/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.8343505859375,\n 42.90011265525328\n ],\n [\n -73.2952880859375,\n 42.9524020856897\n ],\n [\n -73.27880859375,\n 42.65820178455667\n ],\n [\n -73.5150146484375,\n 42.12267315117256\n ],\n [\n -73.5479736328125,\n 41.393294288784865\n ],\n [\n -73.54248046875,\n 41.29431726315258\n ],\n [\n -73.487548828125,\n 41.20345619205131\n ],\n [\n -73.7347412109375,\n 41.10005163093046\n ],\n [\n -73.65234375,\n 41.000629848685385\n ],\n [\n -72.9547119140625,\n 41.14143302653628\n ],\n [\n -72.0538330078125,\n 41.17451935556443\n ],\n [\n -71.43310546875,\n 41.29431726315258\n ],\n [\n -70.6475830078125,\n 41.21585377825921\n ],\n [\n -69.7686767578125,\n 41.16211393939692\n ],\n [\n -69.8785400390625,\n 41.87774145109676\n ],\n [\n -70.1806640625,\n 42.17968819665961\n ],\n [\n -70.57617187499999,\n 42.718768102606326\n ],\n [\n -70.8343505859375,\n 42.90011265525328\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
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Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.
","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teich, M. 0000-0002-8850-9279","orcid":"https://orcid.org/0000-0002-8850-9279","contributorId":225393,"corporation":false,"usgs":false,"family":"Teich","given":"M.","email":"","affiliations":[{"id":41094,"text":"Austrian Research Centre for Forests (BFW), Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":791022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vincent, L.","contributorId":225394,"corporation":false,"usgs":false,"family":"Vincent","given":"L.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lejeune, Y.","contributorId":225395,"corporation":false,"usgs":false,"family":"Lejeune","given":"Y.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sicart, J.-E.","contributorId":225396,"corporation":false,"usgs":false,"family":"Sicart","given":"J.-E.","email":"","affiliations":[{"id":41096,"text":"Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) - UMR 5001,","active":true,"usgs":false}],"preferred":false,"id":791025,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monnet, J.-M.","contributorId":225397,"corporation":false,"usgs":false,"family":"Monnet","given":"J.-M.","email":"","affiliations":[{"id":41097,"text":"Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210859,"text":"70210859 - 2020 - Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","interactions":[],"lastModifiedDate":"2020-06-30T13:29:12.764279","indexId":"70210859","displayToPublicDate":"2020-06-15T08:24:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","docAbstract":"The National Park Service (NPS) manages hundreds of parks in the United States, and many contain important aquatic ecosystems and/or threatened and endangered aquatic species vulnerable to hydro-climatic change. Effective management of park resources under future hydro-climatic uncertainty requires information on both baseline conditions and the range of projected future conditions. A monthly water balance model was used to assess baseline (1981-1999) conditions and a range of projected future hydro-climatic conditions in 374 NPS parks. General circulation model outputs representing 214 future climate simulations were used to drive the model. Projected future changes in temperature (T), precipitation (P), and runoff (R) are expressed as departures from historical baselines. Climate simulations indicate increasing T in 2030 for all parks with 50th percentile simulations projecting increases of 1.67 oC or more in 50% of parks. Departures in 2030 P indicate a mix of mostly increases and some decreases, with 50th percentile simulations projecting increases in P in more than 70% of parks. Departures in R for 2030 are mostly decreases , with the 50th percentile simulations projecting decreases in R in more than 50% of parks in all seasons except winter. Hence in many parks, R is projected to decrease even when P is projected to increase because of increasing T in all NPS parks. Projected changes in future hydro-climatic conditions can also be assessed for individual parks, and Rocky Mountain National Park and Congaree National Park are used as examples.","language":"English","publisher":"MDPI","doi":"10.3390/w12061704","usgsCitation":"Battaglin, W., Hay, L., Lawrence, D.J., McCabe, G.J., and Norton, P.A., 2020, Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States: Water, v. 6, no. 12, 1704, 24 p., https://doi.org/10.3390/w12061704.","productDescription":"1704, 24 p.","ipdsId":"IP-117255","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":456399,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061704","text":"Publisher Index Page"},{"id":376013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n 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48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"6","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglin, William A. 0000-0001-7287-7096","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":204638,"corporation":false,"usgs":true,"family":"Battaglin","given":"William A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren","contributorId":209524,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[],"preferred":true,"id":791751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, David J.","contributorId":34374,"corporation":false,"usgs":true,"family":"Lawrence","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":791752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":791753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791754,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211015,"text":"70211015 - 2020 - Conceptualizing alternate regimes in a large floodplain-river ecosystem","interactions":[],"lastModifiedDate":"2020-07-10T13:20:01.000249","indexId":"70211015","displayToPublicDate":"2020-06-15T08:16:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Conceptualizing alternate regimes in a large floodplain-river ecosystem","docAbstract":"Regime shifts –persistent changes in the structure and function of an ecosystem - are well-documented in many ecosystems but remain poorly understood in floodplain-river ecosystems. We apply a resilience perspective to large floodplain-river ecosystems by presenting three examples of plausible sets of alternate regimes that are relevant to natural resource management interests within the Upper Mississippi River and Illinois River. These alternate regimes include: 1) a clear water and abundant vegetation regime vs. a turbid water and sparse vegetation regime in lentic, off-channel areas, 2) a diverse native fish community regime vs. an invasive-dominated fish community regime, and 3) a regime characterized by a diverse and dynamic mosaic of floodplain vegetation types vs. one characterized as a persistent invasive wet meadow monoculture. For each set of potential alternate regimes, we synthesize known or hypothesized feedback mechanisms that reinforce regimes, controlling variables that drive regime transitions, and restoration pathways. The conceptual models presented here provide a framework for synthesizing our understanding of the dynamics of this ecosystem and are relevant to other large floodplain-river ecosystems that face similar human pressures across the world. The models are currently being used to prioritize future research, test hypotheses, and inform restoration and management on the Upper Mississippi River and Illinois River. Through sharing our approach, we provide a case study in which we document an important step in operationalizing resilience concepts for the management of natural resources.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2020.110516","usgsCitation":"Bouska, K.L., Houser, J.N., De Jager, N.R., Drake, D.C., Collins, S.F., Gibson-Reniemer, C.K., and Thomsen, M.A., 2020, Conceptualizing alternate regimes in a large floodplain-river ecosystem: Journal of Environmental Management, v. 264, 110516, 15 p., https://doi.org/10.1016/j.jenvman.2020.110516.","productDescription":"110516, 15 p.","ipdsId":"IP-108847","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":376247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Iowa, Illinois, Missouri","otherGeospatial":"Upper Mississippi River, Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.58154296875,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"264","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott F.","contributorId":172292,"corporation":false,"usgs":false,"family":"Collins","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":792434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson-Reniemer, Caniel K.","contributorId":228874,"corporation":false,"usgs":false,"family":"Gibson-Reniemer","given":"Caniel","email":"","middleInitial":"K.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":792435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thomsen, Meredith A.","contributorId":228875,"corporation":false,"usgs":false,"family":"Thomsen","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":792436,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225148,"text":"70225148 - 2020 - Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","interactions":[],"lastModifiedDate":"2021-10-14T12:44:11.60355","indexId":"70225148","displayToPublicDate":"2020-06-15T07:41:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","docAbstract":"The U.S. Department of Interior National Park Service is charged with both monitoring avian communities and evaluating the influence of visitors to National Parks on sensitive species; however, this task is challenging considering that sampling programs often involve multiple species, each with differing behavior, habitat requirements, and detectability. Our objectives were to build a model to describe the status of waterbirds in Glacier Bay National Park, Alaska, USA, and assess effects of area closures on these species. We used a Bayesian multistate occupancy model to describe the status of multiple species and make the best possible use of existing survey data. We modeled up to 5 states per species and evaluated predictors of occupancy, nesting, and abundance, as well as survey-related predictors of state-dependent detection probability. We found that occupancy probability varied across species and habitats (islands vs. glacial outwashes). For most species, occupancy probability was substantially greater at sites occupied in the year previous (site persistence). We found weak evidence that area closures affected the occurrence of species in the study, but this was largely because most sites were closed for the entirety of the study period. The probability of detecting occurrence, nesting, and abundance varied across species and survey methods (ground vs. vessel). Detection parameters provided valuable information for enhancing the efficiency of future surveys, by identifying preferred survey methods and sampling periods for specific waterbird species. © 2020 The Wildlife Society.
Dynamically triggered offshore aftershocks, caused by passing seismic waves from main shocks located on land, are currently not considered in tsunami warnings. The M7.0 2010 Haiti earthquake epicenter was located on land 27 km north of the Caribbean Sea and its focal mechanism was oblique strike-slip. Nevertheless, a tsunami recorded on a Caribbean Deep-Ocean Assessment and Reporting of Tsunami (DART) buoy and a tide gauge produced runup heights of 1–3 m along Haiti southeast coast. Earthquake finite-fault model inversions of the DART waveform suggest that a reverse fault doublet with magnitudes of M6.8 and M6.5 located 85 km southwest of the epicenter may have excited the tsunami. This doublet collocates with dynamically triggered aftershocks, derived from back-projection analysis, that occurred 20-60 s after the main shock of the Haiti earthquake. The aftershocks are within a region of maximum dynamic strain predicted by the main shock, on a possibly tectonically active submarine ridge southwest of Haiti's Southern Peninsula. The agreement between the tsunami finite-fault source models and the seismic and tectonic evidence suggests that earthquakes on land, even strike-slip faults, can generate tsunamis by dynamically triggering offshore aftershocks.
Environmental research has been shifting toward a new normal in which a primary focus is to capture change that may be accelerating. In this study, we collected particulate samples in the northern San Francisco Bay Estuary (SFBE) in the fall of 2011 through the spring of 2012 in order to assess vascular plant contributions across both time and space and to compare our findings with a similar set of samples from 1990 to 1992. Across the ∼20-year span, we detected (1) decreasing C:Na ratios (averages ± SD of 12.5 ± 2.5 vs. 8.8 ± 1.4, significant t-test with p < 0.0001); (2) distinct shifts in chlorophyll vs. salinity, with higher chlorophyll concentrations shifting toward freshwater; and (3) greater relative proportions of vascular plant carbon that also appears less degraded (as indicated by lignin measurements) shifting from freshwater toward higher salinities. Lignin compositional data (syringyl:vanillyl and cinnamyl:vanillyl) suggest that increased lignin content in the more saline samples could be derived from wetland materials, while a two-endmember mixing model indicates that a significant portion of the particulate organic carbon (POC) in the western sites (50–60% as an upper bound, 13–15% as a lower bound) could be wetland-derived. This has potential implications for the lower food web, given recent work that demonstrates selective feeding by copepods on wetland detrital material in the northern SFBE. The latter has ramifications for proposed wetland restoration within the SFBE and Sacramento River/San Joaquin River Delta system, namely, that restored wetlands could confer important benefits toward the food web. Equally important is to prioritize continued monitoring of particulate organic matter cycling in the SFBE system to make sure that changing conditions are accounted for in any management decision.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.00185","usgsCitation":"Hernes, P.J., Dyda, R.Y., and Bergamaschi, B.A., 2020, Reassessing particulate organic carbon dynamics in the highly disturbed San Francisco Bay Estuary: Frontiers Earth Science - Biogeoscience, v. 8, 185, 13 p., https://doi.org/10.3389/feart.2020.00185.","productDescription":"185, 13 p.","ipdsId":"IP-117634","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00185","text":"Publisher Index Page"},{"id":502352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin River Delta, San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2443083713161,\n 38.39872273873195\n ],\n [\n -122.2443083713161,\n 37.81288212710655\n ],\n [\n -121.45817059994386,\n 37.81288212710655\n ],\n [\n -121.45817059994386,\n 38.39872273873195\n ],\n [\n -122.2443083713161,\n 38.39872273873195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hernes, Peter J.","contributorId":139730,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":959148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dyda, Rachael Y. 0000-0002-4616-7231","orcid":"https://orcid.org/0000-0002-4616-7231","contributorId":369567,"corporation":false,"usgs":false,"family":"Dyda","given":"Rachael","middleInitial":"Y.","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":959149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217553,"text":"70217553 - 2020 - Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series","interactions":[],"lastModifiedDate":"2021-01-21T21:00:37.352527","indexId":"70217553","displayToPublicDate":"2020-06-13T14:57:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series","docAbstract":"To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly (
In the Northern Gulf of Mexico, salt marshes are threatened by sea level rise, erosion, and loss of protective barrier islands. These barrier islands provide critical habitat for wildlife, including globally significant populations of marsh and shorebirds. We investigated salt marsh restoration on two Louisiana barrier islands using presence of 8 marsh bird species as an index to evaluate restoration success. Land loss was extensive for both islands prior to restoration, with submerged marsh restored by backfilling sediment into the marsh platform. Restoration methods were similar between the two islands, although Raccoon Island was built to a higher elevation (1.1 m) than Whiskey Island (0.8m). Avian presence was estimated via passive acoustic monitoring and point counts. To evaluate restoration success, we modeled influence of habitat covariates on index species presence in restored and reference (intact) sites over three breeding seasons and modeled occupancy for 6 species. On Whiskey Island, index richness was higher in restored sites. Marsh specialists Seaside Sparrows (Ammospiza maritima ) and Least Bitterns (Ixobrychus exilis ) had higher occupancy in restored areas on Whiskey, while generalist species showed no response to site. These results are likely due to a strong association between habitat and vegetation type, with restored sites dominated by Spartina alterniflora and reference sites by Avicennia germinans . On Raccoon Island, species richness was low across all sites. Our results suggest that restoration efforts were successful in creating salt marsh habitat on Whiskey but not Raccoon as of the time of our study.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13222","usgsCitation":"Byerly, P.A., Waddle, H., Premeaux, A.R., and Leberg, P.L., 2020, Effects of barrier island salt marsh restoration on marsh bird occurrence in the Northern Gulf of Mexico: Restoration Ecology, v. 28, no. 6, p. 1610-1620, https://doi.org/10.1111/rec.13222.","productDescription":"11 p.","startPage":"1610","endPage":"1620","ipdsId":"IP-118357","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Isles Derniers, Raccoon Island, Whiskey Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.96954345703125,\n 29.014745722129636\n ],\n [\n -90.65711975097656,\n 29.014745722129636\n ],\n [\n -90.65711975097656,\n 29.09517707913941\n ],\n [\n -90.96954345703125,\n 29.09517707913941\n ],\n [\n -90.96954345703125,\n 29.014745722129636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Byerly, Paige A.","contributorId":237930,"corporation":false,"usgs":false,"family":"Byerly","given":"Paige","email":"","middleInitial":"A.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":222187,"corporation":false,"usgs":true,"family":"Waddle","given":"J. Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Premeaux, Alexis R.","contributorId":237932,"corporation":false,"usgs":false,"family":"Premeaux","given":"Alexis","email":"","middleInitial":"R.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leberg, Paul L.","contributorId":237934,"corporation":false,"usgs":false,"family":"Leberg","given":"Paul","email":"","middleInitial":"L.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795566,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210797,"text":"70210797 - 2020 - Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain","interactions":[],"lastModifiedDate":"2020-06-25T15:19:50.029027","indexId":"70210797","displayToPublicDate":"2020-06-13T09:25:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain","docAbstract":"Conservation status and management priorities are often informed by population trends. Trend estimates can be derived from population surveys or models, but both methods are associated with sources of uncertainty. Many Arctic-breeding shorebirds are thought to be declining based on migration and/or overwintering population surveys, but data are lacking to estimate the trends of some shorebird species. In addition, for most species, little is known about the stage(s) at which population bottlenecks occur, such as breeding vs. nonbreeding periods. We used previously published and unpublished estimates of vital rates to develop the first large-scale population models for 6 species of Arctic-breeding shorebirds in North America, including separate estimates for 3 subspecies of Dunlin. We used the models to estimate population trends and identify life stages at which population growth may be limited. Our model for the arcticola subspecies of Dunlin agreed with previously published information that the subspecies is severely declining. Our results also linked the decline to the subspecies’ low annual survival rate, thus potentially implicating factors during the nonbreeding period in the East Asian-Australasian Flyway. However, our trend estimates for all species showed high uncertainty, highlighting the need for more accurate and precise estimates of vital rates. Of the vital rates, annual survival had the strongest influence on population trend in all taxa. Improving the accuracy, precision, and spatial and temporal coverage of estimates of vital rates, especially annual survival, would improve demographic model-based estimates of population trends and help direct management to regions or seasons where birds are subject to higher mortality.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa026","usgsCitation":"Weiser, E.L., Lanctot, R., Brown, S.C., Gates, H., Bety, J., Boldenow, M.L., Brook, R.W., Brown, G.S., English, W.B., Flemming, S.A., Franks, S., Gilchrist, H.G., Giroux, M., Johnson, A.C., Kendall, S., Kennedy, L.V., Koloski, L., Kwon, E., Lamarre, J., Lank, D.B., Latty, C.J., Lecomte, N., Liebezeit, J.R., McGuire, R., McKinnon, L., Nol, E., Payer, D.C., Perz, J., Rausch, J., Robards, M.D., Saalfeld, S.T., Senner, N.R., Smith, P., Soloviev, M., Solovyeva, D.V., Ward, D.H., Wood, P., and Sandercock, B., 2020, Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain: The Condor, v. 1222, duaa026, 14 p., https://doi.org/10.1093/condor/duaa026.","productDescription":"duaa026, 14 p.","ipdsId":"IP-114598","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":456413,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa026","text":"Publisher Index Page"},{"id":436931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DZZ1OB","text":"USGS data release","linkHelpText":"Arctic Shorebird Population Model"},{"id":375919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1222","noUsgsAuthors":false,"publicationDate":"2020-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":791464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lanctot, Richard B.","contributorId":77879,"corporation":false,"usgs":false,"family":"Lanctot","given":"Richard B.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":791470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Stephen C.","contributorId":38457,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":791471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gates, H. 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habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul-out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population-level inference concerning central place use. Harbor seal haul-out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul-out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul-out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul-out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model-based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3123","usgsCitation":"Brost, B.M., Hooten, M., and Small, R., 2020, Model-based clustering reveals patterns in central place use of a marine top predator: Ecosphere, e03123, 15 p., https://doi.org/10.1002/ecs2.3123.","productDescription":"e03123, 15 p.","ipdsId":"IP-079248","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3123","text":"Publisher Index Page"},{"id":394975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.171142578125,\n 56.32262930069559\n ],\n [\n -151.885986328125,\n 57.76865857271793\n ],\n [\n -152.479248046875,\n 58.019737000187305\n ],\n [\n -153.74267578125,\n 58.04300405858762\n ],\n [\n -155.115966796875,\n 57.320589769167135\n ],\n [\n -154.171142578125,\n 56.32262930069559\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Brost, Brian M.","contributorId":272252,"corporation":false,"usgs":false,"family":"Brost","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":831864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":831863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Small, Robert J.","contributorId":272253,"corporation":false,"usgs":false,"family":"Small","given":"Robert J.","affiliations":[{"id":56329,"text":"akfg","active":true,"usgs":false}],"preferred":false,"id":831865,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210511,"text":"sir20205030 - 2020 - Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features","interactions":[],"lastModifiedDate":"2020-06-12T16:06:26.425579","indexId":"sir20205030","displayToPublicDate":"2020-06-12T09:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5030","displayTitle":"Statewide Assessment of Karst Aquifers in New York With an Inventory of Closed-Depression and Focused-Recharge Features","title":"Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features","docAbstract":"Karst is a landscape formed from the dissolution of soluble rock or rock containing minerals that are easily dissolved from within the rock. The landscape is characterized by sinkholes, caves, losing streams, springs, and underground drainage systems, which rapidly move water through the karst. The two forms of karst in New York State include carbonate karst, which forms in carbonate rock (limestone, marble, and dolostone), and evaporite karst, which forms in rock that contains the evaporite minerals gypsum and halite.
Past and recent studies of karst across the State have shown that areas of focused recharge in karstic carbonate rock allow contaminants to enter aquifer systems with little attenuation. Focused areas of recharge need to be identified to help prevent such contamination from sources on or adjacent to the karst. The New York State Departments of Environmental Conservation and Health are collaborating with the agricultural community to make farmers and farm-planning advisors more aware of karst and how to manage daily farming activities to reduce their impact on surface water and groundwater resources, especially in karst areas. There is also a need to make regulators, planners, and the general public aware of New York’s karst resources and to properly protect and manage these resources to protect the quality of groundwater and surface water that can flow into, through, and from karst bedrock.
Using publicly available geospatial data, karst bedrock and closed depressions over or near karst rock were identified across New York. Carbonate, evaporite, and marble geologic units were selected from a statewide 1:250,000-scale bedrock geology dataset. The selected geologic units were intersected with 7.5-minute quadrangle maps to define the study area.
The U.S. Geological Survey has compiled an inventory of closed depressions from statewide digital contour data, scanned 7.5-minute topographic maps known as a digital raster graphics, and light detection and ranging (lidar) digital elevation models. Analysis of the data resulted in the identification of 5,023 closed depressions statewide. The inventory was conducted to eliminate duplication of results from analysis of the three data sources. A series of overlay analyses was conducted using the closed depressions and thematic data known to be key factors in determining the probability of a closed depression contributing to focused groundwater recharge; the thematic data include bedrock geology, soil type, soil infiltration rate, and land cover.
Though the extent of karst development is important in understanding the interaction between surface water and groundwater in karst terrains, some of the worst cases of groundwater contamination in karst can occur where only minor karst features might be present. The presence of karst—be it a short section of a solutioned fracture or an extensive cave system—requires careful consideration, forward-looking environmental planning, and consistent water-quality protection to preserve New York State’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205030","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Kappel, W.M., Reddy, J.E., and Root, J.C., 2020, Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features: U.S. Geological Survey Scientific Investigations Report 2020–5030, 74 p., https://doi.org/10.3133/sir20205030.","productDescription":"Report: viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090019","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":375401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5030/coverthb.jpg"},{"id":375404,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGN5IJ","text":"USGS data release","linkHelpText":"Data for statewide assessment of New York’s karst aquifers with an inventory of closed-depression and focused-recharge features"},{"id":375534,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5030"},{"id":375482,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030_table1.pdf","text":"Table 1","size":"140 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Stratigraphic column of New York State bedrock indicating those units in which karst features might be present"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4926","usgsCitation":"Leonard, C., Legleiter, C.J., Lea, D.M., and Schmidt, J.C., 2020, Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty: Earth Surface Processes and Landforms, v. 45, no. 11, p. 2727-2744, https://doi.org/10.1002/esp.4926.","productDescription":"18 p.","startPage":"2727","endPage":"2744","ipdsId":"IP-113525","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436932,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SEBJ3X","text":"USGS data release","linkHelpText":"Aerial photographs from the Yampa and Little Snake Rivers in northwest Colorado used to characterize channel changes occurring between 1954 and 1961"},{"id":378500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonard, Christina","contributorId":195596,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","email":"","affiliations":[],"preferred":true,"id":799076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lea, Devin M.","contributorId":240907,"corporation":false,"usgs":false,"family":"Lea","given":"Devin","email":"","middleInitial":"M.","affiliations":[{"id":48159,"text":"Department of Geography, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":799078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":799079,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210999,"text":"70210999 - 2020 - Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA)","interactions":[],"lastModifiedDate":"2020-08-26T19:19:54.479689","indexId":"70210999","displayToPublicDate":"2020-06-12T08:27:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA)","docAbstract":"This research examined the influences of outflow characteristics affecting riverbank stability. The 130 km stretch of the lower Osage River downstream from Bagnell Dam (Missouri, USA) provided an excellent case study for this purpose. The integrated BSTEM model with the HEC-RAS model was accurately calibrated and validated with data from the U.S. Geological Survey (USGS). Then, the outflow characteristics (peak flow duration, flow drawdown rate, and low flow duration) were investigated individually. The results of this study showed that: 1) Riverbank stability is little affected by the duration time of the peak flow, especially on the reaches far from the dam. 2) Sudden flow drawdown significantly reduces riverbank stability. However, the impact of the drawdown rate decreases with distance from the dam. 3) The duration of the low flow after peak flow influences the riverbank stability value proportional to the distance from the dam. The time of low flow before failure increases as the distance from the dam increases.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02626667.2020.1772974","usgsCitation":"Mohammed-Ali, W., Mendoza, C., and Holmes, R.R., 2020, Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA): Hydrological Sciences Journal, v. 65, no. 10, p. 1784-1793, https://doi.org/10.1080/02626667.2020.1772974.","productDescription":"10 p.","startPage":"1784","endPage":"1793","ipdsId":"IP-110034","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Lower Osage River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.7081298828125,\n 38.13887716726548\n ],\n [\n -92.1697998046875,\n 38.13887716726548\n ],\n [\n -92.1697998046875,\n 38.302869955150044\n ],\n [\n -92.7081298828125,\n 38.302869955150044\n ],\n [\n -92.7081298828125,\n 38.13887716726548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mohammed-Ali, Wesam","contributorId":225556,"corporation":false,"usgs":false,"family":"Mohammed-Ali","given":"Wesam","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":792383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mendoza, Cesar","contributorId":225557,"corporation":false,"usgs":false,"family":"Mendoza","given":"Cesar","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":792384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":1624,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":793358,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224298,"text":"70224298 - 2020 - Assessment of fire fuel load dynamics in shrubland ecosystems in the western United States using MODIS products","interactions":[],"lastModifiedDate":"2021-09-21T13:14:01.451306","indexId":"70224298","displayToPublicDate":"2020-06-12T08:11:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of fire fuel load dynamics in shrubland ecosystems in the western United States using MODIS products","docAbstract":"Kodiak Island in southern Alaska is one of the premier examples globally for the study of forearc magmatism. This location contains two Paleocene intrusive belts that formed due to the subduction of a migrating spreading ridge and slab-window: the Kodiak batholith and the trenchward magmatic belt. These magmatic rocks are part of the Sanak-Baranof belt, which extends for greater than 2,100 km along the southern Alaskan margin and vary in age from 61 to 50 Ma west to east.
Trenchward-belt rocks, with an 40Ar/39Ar age of 60.2±0.9 Ma, intrude into the Paleocene Ghost Rocks Formation and are composed of granitoids, basaltic dikes, and small gabbroic plutons that lie along or southward of the Kalsin Bay Fault. Such intrusions were emplaced at shallow levels and have abundant evidence of incomplete intermingling of basaltic and granitic magmas. These textures indicate trenchward-belt intrusions that froze before complete assimilation, leaving behind features such as abundant locally stoped blocks, gabbroic pods within granitic intrusions, and microstructural evidence such as strongly embayed olivine and pyroxene phenocrysts in granitoid bodies.
The Kodiak batholith and satellite intrusions extend for over 110 km along the axis of Kodiak Island and vary in width from 2 to 6 km. These intrude into the Late Cretaceous Kodiak Formation. U-Pb ages on zircon from the intrusions range from 59.2±0.2 Ma in the southwest to 58.4±0.2 Ma near its northwest tip. We interpret these ages as tracking the location of a migrating triple junction and associated slab-window. The batholith is composed of granite and granodiorite, with lesser amounts of tonalite and diorite. The center of the Kodiak batholith contains high-inclusion zones with abundant residual host rock fragments that were carried up from 5–10 km below current exposure levels. These high-inclusion zones contain biotite aggregates, pure quartz clots, and large xenocrysts of sillimanite, kyanite, andalusite, and garnet. This is a higher-pressure mineral assemblage than exists in the batholith metamorphic aureole. Gravity observations and modeling are consistent with the high-inclusion zones extending downward for 5–10 km. The Kodiak batholith results from a migrating triple junction and slab-window that led to high degrees of partial melting within the Kodiak accretionary prism.
Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Invasive predatory lake trout Salvelinus namaycush were discovered in Yellowstone Lake in 1994 and caused a precipitous decrease in abundance of native Yellowstone cutthroat trout Oncorhynchus clarkii bouvieri. Suppression efforts (primarily gillnetting) initiated in 1995 did not curtail lake trout population growth or lakewide expansion. An adaptive management strategy was developed in 2010 that specified desired conditions indicative of ecosystem recovery. Population modeling was used to estimate effects of suppression efforts on the lake trout and establish effort benchmarks to achieve negative population growth (λ < 1). Partnerships enhanced funding support, and a scientific review panel provided guidance to increase suppression gillnetting effort to >46,800 100-m net nights; this effort level was achieved in 2012 and led to a reduction in lake trout biomass. Total lake trout biomass declined from 432,017 kg in 2012 to 196,675 kg in 2019, primarily because of a 79% reduction in adults. Total abundance declined from 925,208 in 2012 to 673,983 in 2019 but was highly variable because of recruitment of age-2 fish. Overall, 3.35 million lake trout were killed by suppression efforts from 1995 to 2019. Cutthroat trout abundance remained below target levels, but relative condition increased, large individuals (> 400 mm) became more abundant, and individual weights doubled, probably because of reduced density. Continued actions to suppress lake trout will facilitate further recovery of the cutthroat trout population and integrity of the Yellowstone Lake ecosystem.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes5020018","usgsCitation":"Koel, T., Arnold, J.L., Bigelow, P., Brenden, T.O., Davis, J.D., Detjens, C.R., Doepke, P., Ertel, B.D., Glassic, H., Gresswell, R.E., Guy, C., MacDonald, D.J., Ruhl, M.E., Stuth, T.J., Sweet, D.P., Syslo, J.M., Thomas, N.A., Tronstad, L., White, P.J., and Zale, A.V., 2020, Yellowstone Lake ecosystem restoration: A case study for invasive fish management: Fishes, v. 5, no. 2, 18, 63 p., https://doi.org/10.3390/fishes5020018.","productDescription":"18, 63 p.","ipdsId":"IP-118729","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":456433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes5020018","text":"Publisher Index Page"},{"id":396739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.9619140625,\n 44.01652134387754\n ],\n [\n -109.6875,\n 44.01652134387754\n ],\n [\n -109.6875,\n 44.95702412512118\n ],\n [\n -110.9619140625,\n 44.95702412512118\n ],\n [\n -110.9619140625,\n 44.01652134387754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Koel, Todd M.","contributorId":272054,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Jeffrey L.","contributorId":278609,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bigelow, Patricia E.","contributorId":276048,"corporation":false,"usgs":false,"family":"Bigelow","given":"Patricia E.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":837116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Jeffery D.","contributorId":287835,"corporation":false,"usgs":false,"family":"Davis","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Detjens, Colleen R.","contributorId":270712,"corporation":false,"usgs":false,"family":"Detjens","given":"Colleen","email":"","middleInitial":"R.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doepke, Philip D.","contributorId":278610,"corporation":false,"usgs":false,"family":"Doepke","given":"Philip D.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837121,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ertel, Brian D.","contributorId":181863,"corporation":false,"usgs":false,"family":"Ertel","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Glassic, Hayley C.","contributorId":278613,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":837184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Guy, Christopher S","contributorId":120991,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher S","affiliations":[],"preferred":false,"id":837185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"MacDonald, Drew J.","contributorId":270660,"corporation":false,"usgs":false,"family":"MacDonald","given":"Drew","email":"","middleInitial":"J.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ruhl, Michael E.","contributorId":287915,"corporation":false,"usgs":false,"family":"Ruhl","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837187,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stuth, Todd J.","contributorId":287916,"corporation":false,"usgs":false,"family":"Stuth","given":"Todd","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":837188,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Sweet, David P.","contributorId":287917,"corporation":false,"usgs":false,"family":"Sweet","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":837189,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Syslo, John M.","contributorId":276045,"corporation":false,"usgs":false,"family":"Syslo","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837190,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Thomas, Nathan A.","contributorId":270658,"corporation":false,"usgs":false,"family":"Thomas","given":"Nathan","email":"","middleInitial":"A.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837191,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Tronstad, Lusha M.","contributorId":224819,"corporation":false,"usgs":false,"family":"Tronstad","given":"Lusha M.","affiliations":[{"id":40947,"text":"Wyoming Natural Diversity Database, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":837192,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"White, Patrick J.","contributorId":169530,"corporation":false,"usgs":false,"family":"White","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":837193,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Zale, Alexander V. 0000-0003-1703-885X zale@usgs.gov","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":3010,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"zale@usgs.gov","middleInitial":"V.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837194,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70262599,"text":"70262599 - 2020 - The ocean's impact on slow slip events","interactions":[],"lastModifiedDate":"2025-01-21T17:37:28.332157","indexId":"70262599","displayToPublicDate":"2020-06-11T11:34:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The ocean's impact on slow slip events","docAbstract":"We test the hypothesis that ocean seafloor pressures impart stresses that alter the initiation or termination of transient slow slip events (SSEs) on shallow submarine and near-coastal faults, using simulated seafloor pressures and a new catalog of SSEs in the Hikurangi subduction zone. We show that seafloor pressures may be represented by an average time history over the ~100-km dimensions of the study area. We account for SSE uncertainties and the multiplicity of processes that affect SSEs statistically by estimating the probabilities of rejecting the null hypothesis that SSE initiation or termination pressures are those to be expected by chance sampling of known (modeled) seafloor pressures, with low probabilities indicating some causal connection. No impact of ocean pressure changes on SSE initiation is detectable, but a correlation with their terminations is suggested. SSE slip that weakens the fault and makes it more sensitive to small stress changes may explain results.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087273","usgsCitation":"Gomberg, J.S., Baxter, P.J., Smith, E.G., Ariyoshi, K., and Chiswell, S., 2020, The ocean's impact on slow slip events: Geophysical Research Letters, v. 47, no. 14, e2020GL087273, 15 p., https://doi.org/10.1029/2020GL087273.","productDescription":"e2020GL087273, 15 p.","ipdsId":"IP-115199","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499853,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/ffe5bbfe0ddb4ed785934362b7777064","text":"External Repository"},{"id":480845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 177.28639581617597,\n -38.17149200548941\n ],\n [\n 177.28639581617597,\n -40.523586910552204\n ],\n [\n 180.53561304652231,\n -40.523586910552204\n ],\n [\n 180.53561304652231,\n -38.17149200548941\n ],\n [\n 177.28639581617597,\n -38.17149200548941\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baxter, Peter J.","contributorId":201839,"corporation":false,"usgs":false,"family":"Baxter","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":924648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Euan G. C.","contributorId":194943,"corporation":false,"usgs":false,"family":"Smith","given":"Euan","email":"","middleInitial":"G. C.","affiliations":[],"preferred":false,"id":924649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chiswell, Steve","contributorId":242932,"corporation":false,"usgs":false,"family":"Chiswell","given":"Steve","email":"","affiliations":[{"id":48587,"text":"National Institute of Water & Atmospheric Research Ltd","active":true,"usgs":false}],"preferred":false,"id":924651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216750,"text":"70216750 - 2020 - Asymmetric benefits of a heterospecific breeding association vary with habitat, conspecific abundance and breeding stage","interactions":[],"lastModifiedDate":"2021-10-26T16:06:27.581353","indexId":"70216750","displayToPublicDate":"2020-06-11T09:27:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Asymmetric benefits of a heterospecific breeding association vary with habitat, conspecific abundance and breeding stage","docAbstract":"Heterospecific breeding associations may benefit individuals by mitigating predation risk but may also create costs if they increase competition for resources or are more easily detectable by predators. Our understanding of the interactions among hetero‐ and conspecifics is often lacking in mixed species colonies. Here, we test how the presence of hetero‐ and conspecifics influence nest and chick survival for two listed (under the U.S. Endangered Species Act) migratory species breeding on the Missouri River, USA. We monitored 2507 piping plover Charadrius melodus nests and 3245 chicks as well as 1060 least tern Sternula antillarum nests and 1374 chicks on Lake Sakakawea, the Garrison River Reach and the Gavins Point Reach for varying years between 2007 and 2016. Piping plover nest and chick survival improved with the presence and abundance of least terns, but least terns only benefited from piping plover presence for certain study areas and breeding stages. Piping plover nest survival was also improved by the presence and abundance of conspecifics on the Garrison River Reach and was negatively influenced by conspecific presence on Lake Sakakawea. Least tern chick survival improved with the presence of other least terns only on the Gavins Point Reach. Ultimately, the heterospecific breeding association between plovers and terns is mutualistic but asymmetric and is moderated by habitat, abundance of conspecifics and breeding stage. Our results highlight that spatiotemporal variation in the interactions among individuals breeding in groups precludes simple generalizations and suggests that management focused on one species may restrict benefits to that focal species if nest site requirements for heterospecifics are not also included.
The consequences of environmental disturbance and management are difficult to quantify for spatially structured populations because changes in one location carry through to other areas as a result of species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different from other contribution metrics, as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences the ordering of node contribution. We find that habitats within which all individuals reside during one season are the most important to short-term network growth under complete removal scenarios, whereas the central breeding region contributes most to population growth over all but the strongest disturbances. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.
use changes in one location carry through to other areas due to species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different than other contribution metrics as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences ordering of node contribution. We find that habitats through which all migrants flow are the most important to short-term network growth under complete-removal scenarios. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/709009","usgsCitation":"Sample, C., Bieri, J., Allen, B.L., Dementieva, Y., Carson, A., Higgins, C., Piatt, S., Qiu, S., Stafford, S., Mattsson, B., Semmens, D., Diffendorfer, J., and Thogmartin, W.E., 2020, Quantifying the contribution of habitats and pathways to a spatially structured population facing environmental change: American Naturalist, v. 196, no. 2, p. 157-168, https://doi.org/10.1086/709009.","productDescription":"12 p.","startPage":"157","endPage":"168","ipdsId":"IP-110963","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":456440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/709009","text":"Publisher Index 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L.","contributorId":193210,"corporation":false,"usgs":false,"family":"Allen","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":795678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dementieva, Yulia","contributorId":219841,"corporation":false,"usgs":false,"family":"Dementieva","given":"Yulia","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carson, Alyssa","contributorId":219842,"corporation":false,"usgs":false,"family":"Carson","given":"Alyssa","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Higgins, Connor","contributorId":237967,"corporation":false,"usgs":false,"family":"Higgins","given":"Connor","email":"","affiliations":[{"id":35881,"text":"Emmanuel 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Redlands","active":true,"usgs":false}],"preferred":false,"id":795684,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mattsson, Brady J.","contributorId":171612,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady J.","affiliations":[{"id":26928,"text":"Univ. of Vienna","active":true,"usgs":false}],"preferred":false,"id":795685,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":795686,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":795687,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":795688,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70210940,"text":"70210940 - 2020 - Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc","interactions":[],"lastModifiedDate":"2020-07-08T15:58:01.581242","indexId":"70210940","displayToPublicDate":"2020-06-11T08:52:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc","docAbstract":"The growth of southern Laurentia has been attributed to the accretion of juvenile arc terranes during the successive 1.74-1.68 Ga Yavapai and 1.65-1.60 Ga Mazatzal orogenies. However, in light of the increasing importance of the ca. 1.49-1.40 Ga Mesoproterozoic Picuris orogeny, the tectonic setting in which the Mazatzal crustal province and its distinctive quartzite-rhyolite successions were generated needs additional examination. The Sandia-Manzano-Los Pinos uplift in central New Mexico is an ideal place to characterize the tectonic history of the Mazatzal crustal province. A comprehensive geochronologic and Hf-isotopic dataset for Proterozoic rocks of the Sandia-Manzano-Los Pinos uplift is presented. Plutonic and metavolcanic rocks in the Sandia-Manzano-Los Pinos uplift were emplaced in three pulses at 1668-1655 Ma, 1587 Ma, and 1459-1453 Ma. Hf-isotope data from the Paleoproterozoic plutonic rocks are juvenile, with both leucogranite and arc-related granodiorite yielding εHf(t) values ranging from +6 to +12, compared to the coeval depleted mantle value of +10 at ca. 1.65 Ga. Inherited zircon in Paleoproterozoic rocks suggest that crust older than 1.7 Ga was involved in their genesis. Hf-isotope data from Mesoproterozoic plutonic rocks in the Sandia-Manzano-Los Pinos uplift are consistent with derivation from 1.7-1.6 Ga lithosphere. Detrital zircon indicate that metasedimentary rocks of the lower Manzano Group were derived primarily from local sources that have U-Pb-Hf isotope compositions similar to the plutonic rocks which intrude and volcanic rocks that underlie the Manzano Group. The detrital zircon provenance of the Manzano Group broadens up-section from unimodal populations with age peaks at ca. 1.65 Ga to include 1.7-3.0 Ga detrital zircon derived from older Laurentian sources like the Yavapai and Mojave provinces. We offer a new model for the formation of the Mazatzal crustal province of New Mexico as a continental margin arc built on top of the previously assembled Yavapai province. The Manzano Group quartzite-rhyolite succession was formed by lithospheric extension above a north-dipping, southward retreating subduction zone. The Manzano Group was then subjected to ca. 1.65 Ga syn-magmatic tectonism and later intracratonic contractional tectonism, likely during the 1.46-1.40 Ga Picuris orogeny.","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105820","usgsCitation":"Holland, M.E., Grambling, T.A., Karlstrom, K.E., Jones, J.V., Nagotko, K.N., and Daniel, C.G., 2020, Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc: Precambrian Research, v. 347, 105820, 19 p., https://doi.org/10.1016/j.precamres.2020.105820.","productDescription":"105820, 19 p.","ipdsId":"IP-119090","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":436934,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U0P2ZY","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Zircon from the Manzano Mountains, New Mexico"},{"id":376146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 32.52828936482526\n ],\n [\n -103.46923828124999,\n 32.52828936482526\n ],\n [\n -103.46923828124999,\n 36.38591277287651\n ],\n [\n -108.45703125,\n 36.38591277287651\n ],\n [\n -108.45703125,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"347","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holland, Mark E.","contributorId":228842,"corporation":false,"usgs":false,"family":"Holland","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grambling, Tyler A.","contributorId":228843,"corporation":false,"usgs":false,"family":"Grambling","given":"Tyler","email":"","middleInitial":"A.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karlstrom, Karl E.","contributorId":228844,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, James V. 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