Quantifying seepage losses from unlined irrigation canals is necessary to improve water use and conservation. The use of heat as a tracer is widely used in quantifying seepage rates across the sediment–water interface. In this study, field observations and two-dimensional numerical models were used to simulate seepage losses during the 2018 and 2019 irrigation season in the Truckee Canal system. Nineteen transects were instrumented with temperature probes and stage recording devices for inverse modeling to derive seepage flux and volumetric losses over the 39 km length of canal. The numerical models for each transect were calibrated and validated using the two-year dataset. Soil zones and observation data were used in each numerical model to help guide calibration of vertical and lateral heat and fluid fluxes. Model simulations were used to derive multivariable regression equations that consider stage, temperature, and hydraulic gradient. The results demonstrate the value of long-term datasets that illustrate the seasonality of groundwater levels, siltation, stage, and temperature on seepage rates. Seepage rates estimated by the numerical models range from 0.16 to 4.6 m3/d m−1. Total annual volumetric losses estimated for 2018 and 2019 were 1.6 × 10-2 to 1.2 × 10-2 km3, respectively. The seepage losses estimated by this study account for 32 % to 41 % of the inflow volumes. Regression models were able to reproduce seepage time-series simulated by the numerical models reasonably well. In arid environments, water diverted into irrigation canals may be influenced by seasonal variations in temperature sufficient to influence the water accounting of conveyed surface flows.
Here we determine how traditional morphometrics (TM) compares with geometric morphometrics (GM) in discriminating among morphologies of four forms of ciscoes of the Coregonus artedi complex collected from Lake Huron. One of the forms comprised two groups of the same deepwater cisco separated by capture depth, whereas the other three forms were shallow-water ciscoes. Our three groups of shallow-water ciscoes were better separated (3% versus 19% overlap) in Principle Component Analysis (PCA) with TM data than with GM data incorporating semilandmarks (evenly spaced nonhomologous landmarks used to bridge between widely separated homologous landmarks). Our two deepwater cisco groups, comprising a putatively single form collected from different depths, separated more in PCAs with GM data (33% overlap) than in PCAs with TM data (66% overlap), an anomaly caused by greater decompression of the swimbladder and deformation of the body wall in the group captured at greater depths. Separation of the two deepwater cisco groups captured at different depths was not affected by the removal of semilandmarks. Assignment of forms using canonical variate analysis (CVA) accurately assigned 86% of individuals using TM data, 98% of individuals using GM data incorporating semilandmarks, and 100% of individuals using GM data without semilandmarks. However, we considered assignments from the same form of deepwater cisco into separate groups as misassignments resulting from different capture depths, which reduced the accuracy of assignments with GM data to 66% with semilandmarks. Our study implies that TM will continue to have an important role in morphological discrimination within Coregonus and other fishes similarly shaped.
Conservation planning for large ecosystems has multiple benefits but is often challenging to implement because of the multiple jurisdictions, species, and habitats involved. In addition, decision making at large spatial scales can be hampered because many approaches do not explicitly incorporate potentially competing values and concerns of stakeholders. After the Deepwater Horizon oil spill, establishing baselines was challenging because of (1) variation in study designs, (2) inconsistent use of explicit objectives and hypotheses, (3) inconsistent use of standardized monitoring protocols, and (4) variation in spatial and temporal scope associated with avian monitoring projects before the spill. Herein, we show how the Gulf of Mexico Avian Monitoring Network members used structured decision making to identify bird monitoring priorities. We used multiple tools and techniques to clearly define the problem and stakeholder objectives and to identify bird monitoring priorities at the scale of the entire northern Gulf of Mexico region. Although our example is specific to the northern Gulf of Mexico, this approach provides an example of how stakeholder values can be incorporated into the coordination process of broad-scale monitoring programs to address management, restoration, and scientific questions in other ecosystems and for other taxa.
","language":"English","publisher":"InForms","doi":"10.1287/inte.2022.1154","usgsCitation":"Fournier, A., Wilson, R., Gleason, J.S., Adams, E.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M., Frederick, P.C., Jodice, P.G., Ottinger, M.A., Reeves, D.B., Seymour, M.A., Sharuga, S.M., Tirpak, J., Vermillion, W.G., Zenzal, T.J., Lyons, J.E., and Woodrey, M.S., 2023, Structured decision making to prioritize regional bird monitoring needs: INFORMS Journal on Applied Analytics, v. 53, no. 3, p. 207-217, https://doi.org/10.1287/inte.2022.1154.","productDescription":"11 p.","startPage":"207","endPage":"217","ipdsId":"IP-102745","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":412274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fournier, Auriel M. V.","contributorId":176535,"corporation":false,"usgs":false,"family":"Fournier","given":"Auriel M. V.","affiliations":[],"preferred":false,"id":862219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, R. Randy","contributorId":288965,"corporation":false,"usgs":false,"family":"Wilson","given":"R. Randy","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":862221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gleason, Jeffrey S.","contributorId":264218,"corporation":false,"usgs":false,"family":"Gleason","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":862222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Evan M.","contributorId":139994,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"M.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":862223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brush, Janell M.","contributorId":264219,"corporation":false,"usgs":false,"family":"Brush","given":"Janell","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":862224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooper, Robert J.","contributorId":99245,"corporation":false,"usgs":false,"family":"Cooper","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":862225,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeMaso, Stephen J.","contributorId":86938,"corporation":false,"usgs":false,"family":"DeMaso","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":862226,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Driscoll, Melanie J. L.","contributorId":301147,"corporation":false,"usgs":false,"family":"Driscoll","given":"Melanie J. L.","affiliations":[{"id":65318,"text":"Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":862227,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Frederick, Peter C.","contributorId":215042,"corporation":false,"usgs":false,"family":"Frederick","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":39161,"text":"Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America","active":true,"usgs":false}],"preferred":false,"id":862228,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":862229,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ottinger, Mary Ann","contributorId":26422,"corporation":false,"usgs":false,"family":"Ottinger","given":"Mary","email":"","middleInitial":"Ann","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":862296,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Reeves, David B.","contributorId":181809,"corporation":false,"usgs":false,"family":"Reeves","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":862230,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Seymour, Michael A.","contributorId":38886,"corporation":false,"usgs":false,"family":"Seymour","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":862231,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sharuga, Stephanie M.","contributorId":301148,"corporation":false,"usgs":false,"family":"Sharuga","given":"Stephanie","email":"","middleInitial":"M.","affiliations":[{"id":65319,"text":"Genwest Systems","active":true,"usgs":false}],"preferred":false,"id":862232,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tirpak, John M.","contributorId":197496,"corporation":false,"usgs":false,"family":"Tirpak","given":"John M.","affiliations":[],"preferred":false,"id":862233,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vermillion, William G.","contributorId":301149,"corporation":false,"usgs":false,"family":"Vermillion","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":862234,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zenzal, Theodore J. Jr. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":224399,"corporation":false,"usgs":true,"family":"Zenzal","given":"Theodore","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":862235,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Lyons, James E.","contributorId":301146,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":862220,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Woodrey, Mark S.","contributorId":259212,"corporation":false,"usgs":false,"family":"Woodrey","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":862236,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70239823,"text":"70239823 - 2023 - Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models","interactions":[],"lastModifiedDate":"2023-09-06T16:04:00.742929","indexId":"70239823","displayToPublicDate":"2023-01-17T09:12:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models","docAbstract":"Non-native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non-native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel-2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel-2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape-level mapping.
","language":"English","publisher":"Wiley","doi":"10.1002/rse2.325","usgsCitation":"Preston, T.M., Johnston, A.N., Ebenhoch, K.G., and Diehl, R.H., 2023, Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models: Remote Sensing in Ecology and Conservation, v. 9, no. 4, p. 512-526, https://doi.org/10.1002/rse2.325.","productDescription":"15 p.","startPage":"512","endPage":"526","ipdsId":"IP-135782","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444792,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.325","text":"Publisher Index Page"},{"id":435499,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91X4EPQ","text":"USGS data release","linkHelpText":"Fractional cover estimates of sweet clover derived from UAV, aerial, and Sentinel-2 imagery for central Montana and northwest South Dakota, 2019"},{"id":412216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, South Dakota","county":"Butte County, Harding County, Musselshell County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-108.6276,46.7487],[-108.5911,46.7491],[-108.5699,46.7489],[-108.5494,46.7486],[-108.5163,46.7481],[-108.4991,46.7483],[-108.4772,46.7489],[-108.4567,46.7491],[-108.3586,46.7503],[-108.3373,46.7509],[-108.2618,46.7524],[-108.2399,46.7525],[-108.2187,46.7526],[-108.1981,46.7532],[-108.1763,46.7533],[-107.8833,46.7568],[-107.878,46.7571],[-107.8276,46.7566],[-107.8279,46.7502],[-107.8221,46.7455],[-107.8162,46.7444],[-107.8164,46.7394],[-107.8118,46.7375],[-107.8101,46.7306],[-107.8155,46.7279],[-107.8295,46.7254],[-107.829,46.7218],[-107.8212,46.7184],[-107.8287,46.7126],[-107.819,46.7083],[-107.8238,46.7038],[-107.8186,46.7],[-107.811,46.693],[-107.8043,46.6942],[-107.8026,46.6873],[-107.7955,46.6835],[-107.7957,46.6784],[-107.7998,46.6758],[-107.802,46.6707],[-107.7983,46.6643],[-107.8024,46.6602],[-107.8044,46.6607],[-107.8082,46.6631],[-107.8111,46.659],[-107.8073,46.6539],[-107.8153,46.6522],[-107.8217,46.6413],[-107.8224,46.639],[-107.8152,46.638],[-107.8148,46.6325],[-107.8184,46.6243],[-107.829,46.6231],[-107.8298,46.6204],[-107.8188,46.6147],[-107.8367,46.5976],[-107.7971,46.5954],[-107.7986,46.495],[-107.7781,46.4951],[-107.7829,46.3948],[-107.9102,46.3931],[-107.9299,46.3935],[-107.9299,46.3779],[-107.947,46.3773],[-107.9482,46.3649],[-107.9693,46.3644],[-107.9712,46.3493],[-107.9915,46.3502],[-107.9901,46.335],[-108.0099,46.3358],[-108.0112,46.3171],[-108.0116,46.3065],[-108.0254,46.3068],[-108.0266,46.2761],[-108.0271,46.2624],[-108.0896,46.2626],[-108.1113,46.263],[-108.131,46.2628],[-108.3197,46.2632],[-108.3195,46.2504],[-108.3622,46.2502],[-108.3627,46.2351],[-108.3838,46.2354],[-108.4035,46.2352],[-108.4033,46.2196],[-108.4035,46.1949],[-108.4032,46.1812],[-108.4037,46.1665],[-108.4035,46.1528],[-108.4035,46.1326],[-108.5301,46.1327],[-108.5518,46.133],[-108.6574,46.1331],[-108.7782,46.1328],[-108.7778,46.2762],[-108.7988,46.2765],[-108.7993,46.3072],[-108.8211,46.307],[-108.822,46.3216],[-108.8325,46.3222],[-108.8318,46.3511],[-108.8423,46.3517],[-108.8419,46.3668],[-108.8636,46.3666],[-108.8634,46.3781],[-108.8642,46.4509],[-108.8846,46.4525],[-108.8856,46.4915],[-108.9074,46.4918],[-108.906,46.5775],[-108.9912,46.5775],[-108.9902,46.622],[-109.0101,46.6209],[-109.0104,46.6649],[-109.0094,46.7378],[-109.0091,46.7516],[-108.9038,46.7504],[-108.8647,46.7504],[-108.817,46.7507],[-108.7567,46.75],[-108.7382,46.7497],[-108.6958,46.7496],[-108.6276,46.7487]]],[[[-102.9587,45.2128],[-102.958,45.1251],[-102.9581,45.0388],[-102.9576,44.7781],[-102.9589,44.69],[-102.9653,44.6898],[-102.966,44.6036],[-103.1861,44.6039],[-103.2066,44.6039],[-103.3273,44.6042],[-103.4467,44.6053],[-103.5666,44.6044],[-103.8156,44.6048],[-103.8258,44.6023],[-103.8256,44.5982],[-103.8234,44.5937],[-103.8324,44.5939],[-103.8309,44.5889],[-103.8373,44.5888],[-103.8384,44.586],[-103.8417,44.5877],[-103.8464,44.5913],[-103.8533,44.5884],[-103.8567,44.5915],[-103.8641,44.5854],[-103.8702,44.5925],[-103.884,44.5985],[-103.8883,44.5952],[-103.8934,44.5942],[-103.8973,44.595],[-103.9018,44.5954],[-103.9061,44.5916],[-103.9053,44.5889],[-103.9105,44.5892],[-103.9144,44.59],[-103.9179,44.5849],[-103.9262,44.5838],[-103.9344,44.5799],[-103.9396,44.5812],[-103.9446,44.5783],[-103.9454,44.5819],[-103.9511,44.5808],[-103.9549,44.5789],[-103.9671,44.5791],[-103.9761,44.5811],[-103.9813,44.5814],[-103.983,44.5777],[-103.9997,44.5773],[-104.0183,44.5773],[-104.0229,44.5799],[-104.035,44.5782],[-104.0399,44.574],[-104.0457,44.5734],[-104.0564,44.5717],[-104.0571,44.9818],[-104.0571,44.9987],[-104.0397,44.9986],[-104.0399,45.0602],[-104.0402,45.1563],[-104.0403,45.169],[-104.0403,45.1774],[-104.0403,45.1832],[-104.0406,45.2143],[-104.041,45.2639],[-104.0425,45.5572],[-104.0426,45.5736],[-104.0424,45.6245],[-104.0425,45.6437],[-104.0425,45.6578],[-104.0425,45.6656],[-104.0426,45.6717],[-104.0426,45.6835],[-104.0433,45.7735],[-104.0434,45.7951],[-104.0435,45.8098],[-104.0437,45.8405],[-104.0439,45.8799],[-104.0441,45.9063],[-104.0443,45.9438],[-102.9956,45.944],[-102.9425,45.944],[-102.9445,45.8189],[-102.9439,45.7311],[-102.955,45.7318],[-102.9558,45.5584],[-102.9565,45.4711],[-102.9539,45.3852],[-102.9578,45.3851],[-102.9605,45.2982],[-102.9587,45.2128]]]]},\"properties\":{\"name\":\"Musselshell\",\"state\":\"MT\"}}]}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Preston, Todd M. 0000-0002-8812-9233","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":204676,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebenhoch, Kyle Gregory 0000-0001-7046-5557","orcid":"https://orcid.org/0000-0001-7046-5557","contributorId":299946,"corporation":false,"usgs":true,"family":"Ebenhoch","given":"Kyle","email":"","middleInitial":"Gregory","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Robert H. 0000-0001-9141-1734 rhdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9141-1734","contributorId":3396,"corporation":false,"usgs":true,"family":"Diehl","given":"Robert","email":"rhdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240165,"text":"70240165 - 2023 - Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River","interactions":[],"lastModifiedDate":"2023-01-31T13:13:29.009178","indexId":"70240165","displayToPublicDate":"2023-01-17T07:11:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River","docAbstract":"Information on velocity fields in rivers is critical for designing infrastructure, modeling contaminant transport, and assessing habitat. Although non-contact approaches to measuring flow velocity are well established, these methods assume a stationary imaging platform. This study eliminates this constraint by introducing a framework for moving aircraft river velocimetry (MARV). The workflow takes as input images acquired from an airplane and involves orthorectification, frame overlap analysis, image enhancement, particle image velocimetry (PIV), and aggregation of the resulting velocity vectors onto a prediction grid. We also use new metrics to quantify the agreement between image-derived and field-measured velocity vectors in terms of both orientation and magnitude. The potential of MARV was evaluated using data from two Alaskan rivers: a large, highly turbid channel and its smaller, clearer tributary. Sediment boil vortices on the mainstem provided natural features trackable via PIV and estimated velocities corresponded closely with field measurements (R2 up to 0.911). We compared an exhaustive approach that evaluates overlap for all frame combinations to a simpler rolling window implementation and found that the more efficient algorithm did not compromise accuracy. Sensitivity analysis suggested that the method was robust to window parameterization. Comparing PIV output from different flying heights and imaging systems indicated that larger pixels led to higher accuracy and that a more advanced dual-camera system provided superior performance. Results from the tributary were less encouraging, presumably due to a lack of trackable features in visible images. Testing across a range of rivers is needed to assess the generality of MARV.
Continued climate warming is reducing seasonal snowpacks in the western United States, where >50% of historical water supplies were snowmelt-derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100-m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo-global-warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation-dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow-dominated regions.
Globally, Marine Protected Areas are an important tool in the conservation of large marine vertebrates. Recent studies have highlighted the use of protected areas by imperiled green turtles (Chelonia mydas) in the southern Gulf of Mexico. To identify and characterize inter-nesting, migratory, and foraging areas for green turtles that nest in the northern Gulf of Mexico, we deployed 14 satellite tags on 13 individual green turtles after nesting in Northwest Florida. We used switching state-space modeling to highlight turtle use in the Florida Keys National Marine Sanctuary and in habitat outside of protected areas such as near Cape Sable, Florida and off the Yucatán Peninsula, Mexico. Turtles were tracked for 21–217 days and migrated for a mean of 22 days. Five individuals used stopover sites during migration; these sites were in areas of dense seagrass habitat, often within boundaries of existing Aquatic Preserves. Turtles established mean foraging home ranges of 118.0 km2 (50% kernel density estimate) with foraging centroids that were 0.33–7.3 km apart. The area off Cape Sable, Florida, which lies outside of currently protected area boundaries, appears to be a hotspot for green turtles that nest throughout the Gulf of Mexico. While protected areas in the Gulf of Mexico are used by this subset of nesting green turtles, several key sites remain unprotected. These findings are relevant when considering expansion of currently protected areas and in defining critical habitat for this species.
In the United States and globally, contaminant exposure in unregulated private-well point-of-use tapwater (TW) is a recognized public-health data gap and an obstacle to both risk-management and homeowner decision making. To help address the lack of data on broad contaminant exposures in private-well TW from hydrologically-vulnerable (alluvial, karst) aquifers in agriculturally-intensive landscapes, samples were collected in 2018–2019 from 47 northeast Iowa farms and analyzed for 35 inorganics, 437 unique organics, 5 in vitro bioassays, and 11 microbial assays. Twenty-six inorganics and 51 organics, dominated by pesticides and related transformation products (35 herbicide-, 5 insecticide-, and 2 fungicide-related), were observed in TW. Heterotrophic bacteria detections were near ubiquitous (94 % of the samples), with detection of total coliform bacteria in 28 % of the samples and growth on at least one putative-pathogen selective media across all TW samples. Health-based hazard index screening levels were exceeded frequently in private-well TW and attributed primarily to inorganics (nitrate, uranium). Results support incorporation of residential treatment systems to protect against contaminant exposure and the need for increased monitoring of rural private-well homes. Continued assessment of unmonitored and unregulated private-supply TW is needed to model contaminant exposures and human-health risks.
Land-based sources of groundwater pollution can be a critical threat to coral reefs, and a better understanding of “ridge-to-reef” water movement is required to advance management and coral survival in the Anthropocene. In this study a more complete understanding of the geological, atmospheric, and oceanic drivers behind coastal groundwater exchange on the Kalaupapa peninsula, on Moloka‘i, Hawai‘i, is obtained by analyzing high resolution geochemical and geophysical time-series data. In concert with multiyear water level analyses, a tidally and precipitation-driven groundwater connection between Kauhakō Crater lake and submarine groundwater discharge (SGD) fluxes are demonstrated. Results include an average discharge rate of 190 cm d−1 and the detection of water-flow pathways past cesspools that likely contribute to higher nutrient loading near the SGD sites. This underlines the importance of managing anthropogenic nutrients that enter the shallow freshwater lens such as through cesspools and are consequently discharged via SGD onto coral reef habitats.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2022.114509","usgsCitation":"Oberle, F.K., Cheriton, O.M., Swarzenski, P., Brown, E.K., and Storlazzi, C.D., 2023, Physicochemical coastal groundwater dynamics between Kauhakō Crater lake and Kalaupapa settlement, Moloka‘i, Hawai‘i: Marine Pollution Bulletin, v. 187, 114509, 12 p., https://doi.org/10.1016/j.marpolbul.2022.114509.","productDescription":"114509, 12 p.","ipdsId":"IP-146121","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":435502,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQAMW8","text":"USGS data release","linkHelpText":"Physicochemical measurements of the coastal aquifer and coastal groundwater discharge on Kalaupapa, Moloka'i, Hawaii"},{"id":411967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","city":"Kalaupapa","otherGeospatial":"Kauhakō Crater Lake, Kalaupapa Peninsula, Moloka'i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.9876032907954,\n 21.20057697124608\n ],\n [\n -156.9876032907954,\n 21.184169607286606\n ],\n [\n -156.9619419645661,\n 21.184169607286606\n ],\n [\n -156.9619419645661,\n 21.20057697124608\n ],\n [\n -156.9876032907954,\n 21.20057697124608\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"187","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Oberle, Ferdinand K.J. 0000-0001-8871-3619","orcid":"https://orcid.org/0000-0001-8871-3619","contributorId":214402,"corporation":false,"usgs":true,"family":"Oberle","given":"Ferdinand","middleInitial":"K.J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swarzenski, Peter W 0000-0003-0116-0578","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":225227,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter W","affiliations":[],"preferred":true,"id":861703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Eric K.","contributorId":41956,"corporation":false,"usgs":true,"family":"Brown","given":"Eric","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":861727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861728,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239727,"text":"70239727 - 2023 - Subaqueous clinoforms created by sandy wave-supported gravity flows: Lessons from the central California shelf","interactions":[],"lastModifiedDate":"2023-01-16T19:30:15.171651","indexId":"70239727","displayToPublicDate":"2023-01-16T13:25:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Subaqueous clinoforms created by sandy wave-supported gravity flows: Lessons from the central California shelf","docAbstract":"Subaqueous clinoforms are an important yet underappreciated shelf feature. Their origins are typically associated with subaerial deltas but recent work has identified similar features in settings without a significant fluvial source. These other studies have shown that such subaqueous clinoforms, also known as infralittoral prograding wedges (IPWs), are created largely by wave-induced processes. This study uses geophysical, sedimentological, and radiocarbon data to determine the sedimentary characteristics and genesis of a shore-parallel subaqueous clinoform developed far from any significant river on the central California continental shelf; a feature known locally as the Cross Hosgri Slope. Sediment cores through the feature reveal that it is composed of beds with an erosive base, followed by a thin coarsening upward sequence of shelly fine sands transitioning to a fining upward sequence marked by alternating parallel and ripple cross laminated very fine sands. The deposit is often capped by fine silts that are commonly interbedded with thin very fine sand beds. Radiocarbon dating of shells within the cores paired with seismic profiles indicate the subaqueous clinoform initiated progradation ~7 ka, nucleating on an older Younger Dryas relict shoreface. We suggest the CHS was created by winter-storm waves mobilizing sands in water depths up to ~ 70 m that transitioned into wave-supported gravity flows. The wave-supported gravity flows traveled downslope to water depths of up to ~85 m, corresponding to the foot of the subaqueous clinoform. They did not travel beyond this depth as wave influence at these depths is negligible and the shelf slope is insufficient to maintain movement of the load alone. Our work suggests that wave-supported gravity flows can entrain very fine sands and silts and build subaqueous clinoforms, even in the absence of a significant river source. Furthermore, we provide a facies model for sandy wave-supported gravity flow deposits.","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2022.106977","usgsCitation":"Medri, E., Simms, A.R., Kluesner, J., Johnson, S., Nishenko, S., Greene, H.G., and Conrad, J.E., 2023, Subaqueous clinoforms created by sandy wave-supported gravity flows: Lessons from the central California shelf: Marine Geology, v. 456, 106977, 13 p., https://doi.org/10.1016/j.margeo.2022.106977.","productDescription":"106977, 13 p.","ipdsId":"IP-144443","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/62t4p940","text":"Publisher Index Page"},{"id":411965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.56611710043825,\n 36.61947594158909\n ],\n [\n -123.56611710043825,\n 34.32371030361945\n ],\n [\n -119.09307635415979,\n 34.32371030361945\n ],\n [\n -119.09307635415979,\n 36.61947594158909\n ],\n [\n -123.56611710043825,\n 36.61947594158909\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"456","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Medri, Elisa","contributorId":300974,"corporation":false,"usgs":false,"family":"Medri","given":"Elisa","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":861657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simms, Alexander R.","contributorId":52887,"corporation":false,"usgs":true,"family":"Simms","given":"Alexander","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":861658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":206367,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":221270,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nishenko, Stuart P.","contributorId":82219,"corporation":false,"usgs":true,"family":"Nishenko","given":"Stuart P.","affiliations":[],"preferred":false,"id":861661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greene, H. Gary","contributorId":139063,"corporation":false,"usgs":false,"family":"Greene","given":"H.","email":"","middleInitial":"Gary","affiliations":[{"id":12639,"text":"Moss Landing Marine Labs","active":true,"usgs":false}],"preferred":false,"id":861662,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":861663,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239723,"text":"70239723 - 2023 - Hydrodynamics and habitat interact to structure fish communities within terminal channels of a tidal freshwater delta","interactions":[],"lastModifiedDate":"2023-01-16T18:55:39.202204","indexId":"70239723","displayToPublicDate":"2023-01-16T12:45:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Hydrodynamics and habitat interact to structure fish communities within terminal channels of a tidal freshwater delta","docAbstract":"Terminal channels were historically a common feature of tidal delta ecosystems but have become increasingly rare as landscapes have been modified. Tidal hydrodynamics are a defining feature in tidal terminal channel ecosystems from which native aquatic communities have evolved. However, few studies have explored the relationship between fish community structure and hydrodynamics in these tidal terminal channel ecosystems. We sampled fish communities throughout a network of terminal channels within the northeasternmost region of the San Francisco Estuary to determine the relationship between fish community structure and hydrodynamics within these environments. We collected two years (2017 and 2018) of fish community samples using gill nets and analyzed data using multivariate community analyses and count models. We found metrics of fish diversity and counts of native fishes to be greatest upstream (farthest from tidal influence) of the tidal excursion within terminal channels. Counts of non-native fishes were less affected by this hydrodynamic feature of terminal channels and more tightly correlated to local habitat conditions (e.g., water temperature, depth). Our results suggest that channel hydrodynamics plays a role in structuring fish communities within terminal channels, particularly native fishes. These results indicate that hydrodynamics in tidal delta ecosystems may be able to be altered in ways that benefit native fishes without the cost of water pumping.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4339","usgsCitation":"Huntsman, B., Young, M.J., Feyrer, F.V., Stumpner, P., Brown, L.R., and Burau, J.R., 2023, Hydrodynamics and habitat interact to structure fish communities within terminal channels of a tidal freshwater delta: Ecosphere, v. 14, no. 1, e4339, 18 p., https://doi.org/10.1002/ecs2.4339.","productDescription":"e4339, 18 p.","ipdsId":"IP-139147","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":444812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4339","text":"Publisher Index Page"},{"id":411962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cache Slough Complex, San Francisco Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.69932887248329,\n 38.24856975517184\n ],\n [\n -121.67220637492461,\n 38.24776088323978\n ],\n [\n -121.71752497844034,\n 38.3229474560282\n ],\n [\n -121.80987879924118,\n 38.31217280176293\n ],\n [\n -121.77760646037407,\n 38.28495967449561\n ],\n [\n -121.72713801554991,\n 38.28064774752275\n ],\n [\n -121.69932887248329,\n 38.24856975517184\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stumpner, Paul 0000-0002-0933-7895 pstump@usgs.gov","orcid":"https://orcid.org/0000-0002-0933-7895","contributorId":5667,"corporation":false,"usgs":true,"family":"Stumpner","given":"Paul","email":"pstump@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Larry R. 0000-0001-6702-4531","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":269405,"corporation":false,"usgs":false,"family":"Brown","given":"Larry","email":"","middleInitial":"R.","affiliations":[{"id":55970,"text":"USGS CAWSC (not in system - posthumous)","active":true,"usgs":false}],"preferred":false,"id":861639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burau, Jon R. 0000-0002-5196-5035 jrburau@usgs.gov","orcid":"https://orcid.org/0000-0002-5196-5035","contributorId":1500,"corporation":false,"usgs":true,"family":"Burau","given":"Jon","email":"jrburau@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861640,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239726,"text":"70239726 - 2023 - Enhancements to population monitoring of Yellowstone grizzly bears","interactions":[],"lastModifiedDate":"2023-01-16T18:08:30.013759","indexId":"70239726","displayToPublicDate":"2023-01-16T12:00:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"Enhancements to population monitoring of Yellowstone grizzly bears","docAbstract":"In the Greater Yellowstone Ecosystem, counts of female grizzly bears (Ursus arctos) with cubs-of-the-year (females with cubs) from systematic aerial surveys and opportunistic ground sightings are combined with demographic data to derive annual population estimates. We addressed 2 limitations to the monitoring approach. As part of a rule set, a conservative distance of >30 km currently is used as a threshold to assign sightings to unique females with cubs, resulting in underestimation bias. Using telemetry locations of females with cubs collected during 1997–2019, we created 1,000 data sets for each of 5 levels of simulated number of females with cubs, simulated sightings by selecting among these locations, and evaluated the classification performance of alternative distance criteria (12–30 km). Under all scenarios, 12–16-km criteria maximized classification performance and minimized estimation bias; the 16-km criterion was optimal for current conditions and sampling efforts. Our second objective was to test generalized additive models (GAMs) as a flexible trend analysis technique. We simulated 1,000 time series for each of 10 scenarios (10, 15, and 20% decline over periods of 5, 10, and 15 yrs, plus stability), applied GAMs, and assessed metrics associated with the posterior distribution of the instantaneous rate of change. We detected declines among >99.6% of replicates under the 15 and 20% decline scenarios and in 84.7–94.7% of replicates under the 10% decline scenario. From decline onset to first detection, periods ranged from 3.7 (20% decline over 5 yrs) to 11.1 (10% decline over 15 yrs), with 3.9–8.8 years mean duration of detection events. The GAM approach allows detection of directional changes in population trend, including early warning metrics, and stabilization after such changes. Retrospective application of the 16-km distance criterion and GAMs resulted in higher population estimates and growth rates than are reported using current methods.
","language":"English","publisher":"International Association for Bear Research and Management","doi":"10.2192/URSUS-D-22-00002.2","usgsCitation":"van Manen, F.T., Ebinger, M., Costello, C., Bjornlie, D., Clapp, J., Thompson, D., Haroldson, M.A., Frey, K.L., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2023, Enhancements to population monitoring of Yellowstone grizzly bears: Ursus, v. 33, e17, 19 p., https://doi.org/10.2192/URSUS-D-22-00002.2.","productDescription":"e17, 19 p.","ipdsId":"IP-137270","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444815,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-22-00002.2","text":"Publisher Index Page"},{"id":411958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.06216250119708,\n 45.005442783577365\n ],\n [\n -111.05388206352849,\n 43.99158796485162\n ],\n [\n -110.88275301837602,\n 44.04716331869298\n ],\n [\n -110.79994864168938,\n 44.08682810596011\n ],\n [\n -110.62881959653659,\n 44.134390765324724\n ],\n [\n -110.49081230205884,\n 44.20170567654927\n ],\n [\n -110.40248763359303,\n 44.19181124293098\n ],\n [\n -110.31968325690639,\n 44.160137890711866\n ],\n [\n -110.18167596242864,\n 44.26696726895861\n ],\n [\n -110.17615567064948,\n 44.342027324830866\n ],\n [\n -110.21479771310338,\n 44.39727332056478\n ],\n [\n -110.12647304463755,\n 44.44064433112848\n ],\n [\n -110.16235494120168,\n 44.49973477113454\n ],\n [\n -110.17063537887029,\n 44.51941828998153\n ],\n [\n -110.05746939739868,\n 44.61773617210656\n ],\n [\n -110.0602295432881,\n 44.68842214103671\n ],\n [\n -110.18995640009723,\n 44.87258486724454\n ],\n [\n -110.3031223815692,\n 44.92341978198769\n ],\n [\n -110.27000063089446,\n 45.003491217871584\n ],\n [\n -110.41076807126196,\n 45.00153958568268\n ],\n [\n -110.45217025960528,\n 44.997636121855464\n ],\n [\n -110.67850222254884,\n 44.993732392095296\n ],\n [\n -110.78890805813103,\n 45.009345715540036\n ],\n [\n -111.06216250119708,\n 45.005442783577365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":861641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebinger, Michael","contributorId":300973,"corporation":false,"usgs":false,"family":"Ebinger","given":"Michael","affiliations":[{"id":35211,"text":"Montana Department of Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":861642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":861643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bjornlie, Daniel D.","contributorId":145512,"corporation":false,"usgs":false,"family":"Bjornlie","given":"Daniel D.","affiliations":[{"id":16140,"text":"Wyoming Game & Fish Department, Large Carnivore Section, Lander, Wyoming 82520, USA","active":true,"usgs":false}],"preferred":false,"id":861644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clapp, Justin","contributorId":256932,"corporation":false,"usgs":false,"family":"Clapp","given":"Justin","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":861645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Daniel","contributorId":225736,"corporation":false,"usgs":false,"family":"Thompson","given":"Daniel","affiliations":[{"id":13584,"text":"Natural Resources Canada, Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":861646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":861647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frey, Kevin L.","contributorId":124580,"corporation":false,"usgs":false,"family":"Frey","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":5125,"text":"Montana Fish Wildlife and Parks, Bear Management Office, 1400 South 19th Avenue, Bozeman, MT 59718","active":true,"usgs":false}],"preferred":false,"id":861648,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendricks, Curtis","contributorId":256933,"corporation":false,"usgs":false,"family":"Hendricks","given":"Curtis","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":861649,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nicholson, Jeremy M.","contributorId":256934,"corporation":false,"usgs":false,"family":"Nicholson","given":"Jeremy M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":861650,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gunther, Kerry A.","contributorId":84621,"corporation":false,"usgs":false,"family":"Gunther","given":"Kerry","email":"","middleInitial":"A.","affiliations":[{"id":5118,"text":"Yellowstone National Park, Yellowstone Center for Resources, Bear Management Office, P.O. Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":861651,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilmot, Katharine R.","contributorId":244265,"corporation":false,"usgs":false,"family":"Wilmot","given":"Katharine","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":861652,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":861653,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fortin-Noreus, Jennifer","contributorId":200746,"corporation":false,"usgs":false,"family":"Fortin-Noreus","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":861654,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hnilicka, Pat","contributorId":289731,"corporation":false,"usgs":false,"family":"Hnilicka","given":"Pat","affiliations":[],"preferred":false,"id":861655,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tyers, Daniel B.","contributorId":124587,"corporation":false,"usgs":false,"family":"Tyers","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":5129,"text":"U.S. Forest Service, 2327 University Way, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":861656,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70239942,"text":"70239942 - 2023 - Genetic diversity goals and targets have improved, but remain insufficient for clear implementation of the post-2020 global biodiversity framework","interactions":[],"lastModifiedDate":"2025-02-07T16:12:42.07767","indexId":"70239942","displayToPublicDate":"2023-01-16T07:21:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity goals and targets have improved, but remain insufficient for clear implementation of the post-2020 global biodiversity framework","docAbstract":"Genetic diversity among and within populations of all species is necessary for people and nature to survive and thrive in a changing world. Over the past three years, commitments for conserving genetic diversity have become more ambitious and specific under the Convention on Biological Diversity’s (CBD) draft post-2020 global biodiversity framework (GBF). This Perspective article comments on how goals and targets of the GBF have evolved, the improvements that are still needed, lessons learned from this process, and connections between goals and targets and the actions and reporting that will be needed to maintain, protect, manage and monitor genetic diversity. It is possible and necessary that the GBF strives to maintain genetic diversity within and among populations of all species, to restore genetic connectivity, and to develop national genetic conservation strategies, and to report on these using proposed, feasible indicators.
Reducing phosphorus (P) concentrations in aquatic ecosystems, is necessary to improve water quality and reduce the occurrence of harmful cyanobacterial algal blooms. Managing P reduction requires information on the role rivers play in P transport from land to downstream water bodies, but we have a poor understanding of when and where river systems are P sources or sinks. During the summers of 2019 and 2021, we sampled streambed sediment at 78 sites throughout the Maumee River network (a major source of P loads to Lake Erie) focusing on the zero equilibrium P concentration (EPC0), the soluble reactive phosphorus (SRP) concentration at which sediment neither sorbs nor desorbs P. We used structural equation modeling to identify direct and indirect drivers of EPC0. Stream sediment was a P sink at 40 % and 67 % of sites in 2019 and 2021, respectively. During both years, spatial variation in EPC0 was shaped by stream water SRP concentrations, sediment P saturation, and sediment physicochemical characteristics. In turn, SRP concentrations and sediment P saturation (PSR) were influenced by agricultural land use and stream size. Effect of stream size differed among years with stream size having a greater effect on SRP in 2019 and on PSR in 2021. Streambed sediment is currently a net P sink across the sites sampled in the Maumee River network during summer, but sediment at these locations, especially sites in headwater streams, may become a P source if stream water SRP concentrations decrease. Our results improve the understanding of watershed- and reach-scale controls on EPC0 but also indicate the need for further research on how changes in SRP concentration as a result of conservation management implementation influences the role of streambed sediment in P transport to Lake Erie.
Resource managers and scientists across western U.S. agencies seek methodologies for identifying environmental attributes important to both wildlife conservation and broad-scale land stewardship. The Greater Sage-Grouse (Centrocercus urophasianus; hereafter, sage-grouse) exemplifies a species in need of this broad-scale approach given widespread population declines that have resulted from loss and degradation of habitat from natural and anthropogenic disturbances. These include agricultural land conversion, conifer expansion, energy development, and wildfire coupled with ecological conversion by invasive plants such as cheatgrass (Bromus tectorum). Development of habitat assessments and conservation actions for sage-grouse benefit from studies that link demographic responses to habitat selection patterns. To address this, we examined nest survival of sage-grouse in relation to fine-scale habitat patterns (i.e., field-based habitat measurements) that influenced nest site selection, using data from nests of telemetered females at 17 sites over 6 years in Nevada and northeastern California, USA. Importantly, sites spanned mesic and xeric average precipitation conditions that contributed substantially to vegetation community structure across cold desert ecosystems of the North American Great Basin. Vegetative cover immediately surrounding sage-grouse nests was important for both nest site selection and nest survival, but responses varied between mesic and xeric sites. For example, while taller perennial grasses were selected at xeric sites, we found no evidence of selection for perennial grass at mesic sites, indicating a functional response to availability of habitat features between hydrographic regions. Furthermore, perennial grass height and forb height both had positive effects on nest survival at xeric sites, but we found varying effects at mesic sites. We emphasize that precipitation conditions driving ecosystem productivity vary regionally among sagebrush communities, shaping vegetation structure and suitable habitat conditions for nesting sage-grouse.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/ornithapp/duac052","usgsCitation":"Brussee, B.E., Coates, P.S., O’Neil, S.T., Ricca, M.A., Dudko, J.E., Espinosa, S.P., Gardner, S.C., Casazza, M.L., and Delehanty, D.J., 2023, Influence of fine-scale habitat characteristics on sage-grouse nest site selection and nest survival varies by mesic and xeric site conditions: Ornithological Applications, v. 125, no. 1, duac052, 15 p., https://doi.org/10.1093/ornithapp/duac052.","productDescription":"duac052, 15 p.","ipdsId":"IP-123231","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":444825,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duac052","text":"Publisher Index Page"},{"id":435505,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EWKWJ3","text":"USGS data release","linkHelpText":"Code to Examine How the Influence of Fine-Scale Habitat Characteristics on Greater Sage-Grouse (Centrocercus urophasianus) Nest Site Selection and Nest Survival Varies by Mesic and Xeric Site Conditions version 1.0"},{"id":435504,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJ747B","text":"USGS data release","linkHelpText":"Influence of microhabitat characteristics on sage-grouse nest site selection and nest survival depends on ecological site potential"},{"id":412804,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dudko, Jonathan E.","contributorId":210049,"corporation":false,"usgs":false,"family":"Dudko","given":"Jonathan","email":"","middleInitial":"E.","affiliations":[{"id":38059,"text":"Idaho State U","active":true,"usgs":false}],"preferred":false,"id":863779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":863780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":863781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240633,"text":"70240633 - 2023 - Dissolved carbon export by large river systems is influenced by source area heterogeneity","interactions":[],"lastModifiedDate":"2023-02-10T12:54:31.17047","indexId":"70240633","displayToPublicDate":"2023-01-16T06:52:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved carbon export by large river systems is influenced by source area heterogeneity","docAbstract":"Rivers and streams export inorganic and organic carbon derived from contributing landscapes and so downstream carbon fluxes are important quantitative indicators of change in ecosystem function and for the full accounting of terrestrial carbon budgets. Carbon concentration-discharge (C-Q) relationships in rivers provide important information about carbon source and behavior in watersheds and are useful for estimating carbon export. However, C-Q relationships are complex in large river systems because of spatial and temporal heterogeneity in carbon dynamics across the watershed and river networks. We quantified dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) fluxes in the Upper Mississippi River basin and investigated their relationships with land cover and hydrology. The magnitude of dissolved carbon yields ranged widely among stations, 0.6–5.7 g DOC m−2 yr−1 and 2.9–11.8 g DIC m−2 yr−1. Spatial patterns in carbon fluxes were strongly related to land cover, with agricultural sites having high DIC/low DOC exports and forested and wetland areas having the opposite. DIC was always negatively related to discharge (Q), while the DOC-Q relationship varied with land cover. Differential behavior of carbon across the basin resulted in Q having a weak relationship with DOC and DIC at the basin outlet. Hence, there is a need to improve understanding of headwater terrestrial-to-aquatic carbon connections in order to improve basin-to-continental-scale carbon export estimates. Our results demonstrate that quantitative understanding of carbon export by large rivers can be improved by incorporating stream network information, such as the timing, location, and source of constituent flux, rather than relying solely upon relationships between constituent behavior and seasonality or discharge at the basin outlet.
Genetic diversity is critical to a population’s ability to overcome gradual environment change. Large-bodied wildlife existing in regions with relatively high human population density are vulnerable to isolation-induced genetic drift, population bottlenecks, and loss of genetic diversity. Moose (Alces americanus americanus) in eastern North America have a complex history of drastic population changes. Current and potential threats to moose populations in this region could be exacerbated by loss of genetic diversity and connectivity among subpopulations. Existing genetic diversity, gene flow, and population clustering and fragmentation of eastern North American moose are not well quantified, while physical and anthropogenic barriers to population connectivity already exist. Here, single nucleotide polymorphism (SNP) genotyping of 507 moose spanning five northeastern U.S. states and one southeastern Canadian province indicated low diversity, with a high proportion of the genomes sharing identity-by-state, with no consistent evidence of non-random mating. Gene flow estimates indicated bidirectionality between all pairs of sampled areas, with magnitudes reflecting clustering and differentiation patterns. A Discriminant Analysis of Principal Components analysis indicated that these genotypic data were best described with four clusters and indicated connectivity across the Saint Lawrence River and Seaway, a potential physical barrier to gene flow. Tests for genetic differentiation indicated restricted gene flow between populations across the Saint Lawrence River and Seaway, and between many sampled areas facing expanding human activity. These results document current genetic variation and connectivity of moose populations in eastern North America, highlight potential challenges to current population connectivity, and identify areas for future research and conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-022-01496-w","usgsCitation":"Rosenblatt, E., Gieder, K., Donovan, T.M., Murdoch, J., Smith, T., Stephanie McKay, Heaton, M., Kalbfleisch, T., Murdoch, B., Bhattarai, S., Pacht, E., Verbist, E., Basnayake, V., and McKay, S., 2023, Genetic diversity and connectivity of moose (Alces americanus americanus) in eastern North America: Conservation Genetics, v. 24, p. 235-248, https://doi.org/10.1007/s10592-022-01496-w.","productDescription":"14 p.","startPage":"235","endPage":"248","ipdsId":"IP-139721","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481070,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-022-01496-w","text":"Publisher Index Page"},{"id":480758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"New York, Vermont, New Hampshire, Maine, and Massachusetts","otherGeospatial":"Quebec","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.93230224047862,\n 46.77041749427818\n ],\n [\n -73.10392768038764,\n 45.36416223111472\n ],\n [\n -76.0820355810502,\n 44.501512257211175\n ],\n [\n -78.14823440598961,\n 42.227731281773856\n ],\n [\n -70.01581937845573,\n 41.76312420903159\n ],\n [\n -66.879327626342,\n 44.56814221402969\n ],\n [\n -67.8315462407186,\n 47.380624153749466\n ],\n [\n -69.33446774900295,\n 47.532790444215905\n ],\n [\n -71.93230224047862,\n 46.77041749427818\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2023-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":348978,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gieder, Katherina","contributorId":348979,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":923908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James","contributorId":348980,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Timothy P.L.","contributorId":349566,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy P.L.","affiliations":[],"preferred":false,"id":924440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephanie McKay","contributorId":348981,"corporation":false,"usgs":false,"family":"Stephanie McKay","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923911,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heaton, Michael P.","contributorId":348982,"corporation":false,"usgs":false,"family":"Heaton","given":"Michael P.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":923912,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kalbfleisch, Theodore S.","contributorId":348983,"corporation":false,"usgs":false,"family":"Kalbfleisch","given":"Theodore S.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":923913,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murdoch, Brenda M.","contributorId":348984,"corporation":false,"usgs":false,"family":"Murdoch","given":"Brenda M.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":923914,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bhattarai, Suraj","contributorId":348985,"corporation":false,"usgs":false,"family":"Bhattarai","given":"Suraj","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923915,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pacht, Emory","contributorId":348986,"corporation":false,"usgs":false,"family":"Pacht","given":"Emory","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923916,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verbist, Emma","contributorId":348987,"corporation":false,"usgs":false,"family":"Verbist","given":"Emma","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923917,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Basnayake, Veronica","contributorId":348988,"corporation":false,"usgs":false,"family":"Basnayake","given":"Veronica","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":923918,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McKay, Stephanie","contributorId":348990,"corporation":false,"usgs":false,"family":"McKay","given":"Stephanie","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923919,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70239758,"text":"70239758 - 2023 - Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California","interactions":[],"lastModifiedDate":"2023-01-18T14:25:55.153483","indexId":"70239758","displayToPublicDate":"2023-01-15T08:20:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California","docAbstract":"In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail (Anas acuta), Green-winged Teal (Anas crecca), and Northern Shoveler (Anas clypeata), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4367","usgsCitation":"Conlisk, E.E., Byrd, K.B., Matchett, E., Lorenz, A., Casazza, M.L., Golet, G.H., Reynolds, M.D., Sesser, K.A., and Reiter, M.E., 2023, Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California: Ecosphere, v. 14, e4367, 19 p., https://doi.org/10.1002/ecs2.4367.","productDescription":"e4367, 19 p.","ipdsId":"IP-144890","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4367","text":"Publisher Index Page"},{"id":412024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.81873286495954,\n 35.04394124445325\n ],\n [\n -118.8570190310794,\n 36.52123291574787\n ],\n [\n -120.23927537042829,\n 37.988003366747364\n ],\n [\n -121.61872476287942,\n 40.10174582877633\n ],\n [\n -121.96284031570764,\n 40.846007013038246\n ],\n [\n -123.06491347085935,\n 40.526780450482676\n ],\n [\n -122.81752903048022,\n 39.240089518991965\n ],\n [\n -122.53157865344912,\n 38.316070964197905\n ],\n [\n -121.80393628808622,\n 37.96304395659068\n ],\n [\n -121.19117392124522,\n 37.28315868783392\n ],\n [\n -120.09976879490424,\n 36.11749270862333\n ],\n [\n -119.50335982480208,\n 35.23834283652615\n ],\n [\n -118.81873286495954,\n 35.04394124445325\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2023-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Conlisk, Erin E.","contributorId":301022,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":861775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golet, Gregory H.","contributorId":89844,"corporation":false,"usgs":false,"family":"Golet","given":"Gregory","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":861780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Mark D.","contributorId":301023,"corporation":false,"usgs":false,"family":"Reynolds","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":861781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sesser, Kristin A.","contributorId":215294,"corporation":false,"usgs":false,"family":"Sesser","given":"Kristin","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":861782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":861783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239935,"text":"70239935 - 2023 - Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","interactions":[],"lastModifiedDate":"2023-03-28T14:38:56.423278","indexId":"70239935","displayToPublicDate":"2023-01-15T07:09:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","docAbstract":"dictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest-site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest-site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.
Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.
As a part of the Texas Water Development Board groundwater availability modeling program, the U.S. Geological Survey developed the Gulf Coast Land Subsidence and Groundwater-Flow model (hereinafter, the “GULF model”) and ensemble to simulate groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system (the study area) in Texas from predevelopment (1897) through 2018. Since the publication of a previous groundwater model for the greater Houston area in 2012, there have been changes to the distribution of groundwater withdrawals and advances in modeling tools. To reflect these changes and to simulate more recent conditions, the GULF model was developed in cooperation with the Harris-Galveston and Fort Bend Subsidence Districts to provide an updated Groundwater Availability Model.
Since the early 1900s, most of the groundwater withdrawals in the study area have been from three of the hydrogeologic units that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers and, more recently, from the Catahoula confining unit. Withdrawals from these hydrogeologic units are used for municipal supply, commercial and industrial use, and irrigation purposes. Withdrawals of large quantities of groundwater in the greater Houston area have caused widespread groundwater-level declines in the Chicot, Evangeline, and Jasper aquifers of more than 300 feet (ft). Early development of the aquifer system, which began before 1900, resulted in nearly 50 percent of the eventual historical groundwater-level minimums having been reached as early as 1946 in some areas. These groundwater-level declines led to more than 9 ft of land-surface subsidence—historically in central and southeastern Harris County and Galveston County, but more recently in northern, northwestern, and western Harris County, Montgomery County, and northern Fort Bend County—from depressurization and compaction of clay and silt layers interbedded in the aquifer sediments.
In a generalized conceptual model of the Gulf Coast aquifer system, water enters the groundwater system in topographically high outcrops of the hydrogeologic units in the northwestern part of the aquifer system. Groundwater that does not discharge to streams flows to intermediate and deep zones of the aquifer system southeastward of the outcrop areas where it is discharged by wells and by upward leakage in topographically low areas near the coast. The uppermost parts of the aquifer system, which include outcrop areas, are under water-table (unconfined) conditions where the groundwater is not confined under pressure. As depth increases in the aquifer system and interbedded clay and silt layers accumulate, water-table conditions evolve into confined conditions where the groundwater is under pressure.
Groundwater flow and land-surface subsidence in the GULF model and ensemble were simulated by using MODFLOW 6 with the Skeletal Storage, Compaction, and Subsidence package. The model consists of six layers, one for each of the five hydrogeologic units in the northern part of the Gulf Coast aquifer system and a surficial top layer that includes part of each hydrogeologic unit. Transient groundwater flow was simulated during 1897–2018 by using a combination of multiyear, annual, and monthly stress periods. An initial steady-state stress period was configured to represent predevelopment mean annual inflows and outflows. The subsidence package used in the GULF model and ensemble uses a head-based subsidence formulation that simulates the delayed drainage response from clay and silt sediment to changes in groundwater levels.
The GULF model and ensemble were history matched to groundwater-level observations at selected wells, land-surface subsidence at benchmarks, aquifer compaction at borehole extensometers, and vertical displacement from Global Positioning System stations. A Bayesian framework was used to represent uncertainty in modeled parameters and simulated outputs of interest. History matching and uncertainty quantification were performed by using a Monte Carlo approach enabled through iterative ensemble smoother software to produce an ensemble of models fit to historical data. The iterative ensemble smoother substantially reduced the computational demand of parameter estimation by approximating the first-order relation between model inputs and outputs, thereby allowing 183,207 adjustable parameters to be used for history matching at a relatively low computational and time cost.
The history-matched parameter values are within the ranges of previously published values and agree with the current understanding of the spatial and temporal patterns of parameter uncertainty for the Gulf Coast aquifer system. A good agreement between the observed (or estimated) and simulated groundwater levels, land-surface subsidence, compaction, and vertical displacement was obtained across the modeled area based on qualitative and quantitative comparisons. Ensemble mean annual groundwater-flow rates to the Chicot, Evangeline, Jasper aquifers and Catahoula confining unit were 0.0–0.49 inch (in.), 0.09–0.33 in., 0.01–0.07 in., and 0.01–0.05 in., respectively. GULF model mean annual groundwater-flow rates to the Chicot, Evangeline, and Jasper aquifers and Catahoula confining unit were 0.31 in., 0.19 in., 0.03 in., and 0.03 in., respectively.
The GULF-model-simulated recharge to the outcrop area was the largest inflow (75 percent), and recharge to other areas was 25 percent of the model inflow. The simulated outflows included (1) net surface-water/groundwater exchange with study area streams (50 percent), (2) groundwater use (49 percent), and (3) net surface-water/groundwater exchange with the Gulf of Mexico (1 percent). The sum of the simulated values of the outflows (1,041,973 acre-feet per year [acre-ft/yr]) and the elastic expansion of the fine-grained sediment and numerical solver error (339 acre-ft/yr) minus the inflows (654,172 acre-ft/yr) represents the reduction of storage from the Gulf Coast aquifer system (388,140 acre-ft/yr). Most of the storage depletion is caused by the long-term groundwater-level declines that have resulted primarily in inelastic compaction.
The GULF model was used to estimate Jasper aquifer compaction at selected benchmarks in Montgomery County and northern Harris County, which are the primary locations of Jasper aquifer groundwater use. Simulated Jasper aquifer compaction in northern Harris County was between 0.2 and 0.5 ft, or between about 5 and 16 percent of simulated subsidence at the benchmark locations. Simulated Jasper aquifer compaction in Montgomery County was between 0.8 and 1.2 ft, or between about 33 and 57 percent of simulated subsidence at the benchmark locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1877","issn":"ISSN 2330-7102","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District and the Fort Bend Subsidence District","usgsCitation":"Ellis, J.H., Knight, J.E., White, J.T., Sneed, M., Hughes, J.D., Ramage, J.K., Braun, C.L., Teeple, A., Foster, L., Rendon, S.H., and Brandt, J., 2023, Hydrogeology, land-surface subsidence, and documentation of the Gulf Coast Land Subsidence and Groundwater-Flow (GULF) model, southeast Texas, 1897–2018 (ver. 1.1, November 2023): U.S. Geological Survey Professional Paper 1877, 425 p., https://doi.org/10.3133/pp1877.","productDescription":"Report: xx, 425 p., 8 Appendixes; Data Release","numberOfPages":"450","onlineOnly":"Y","ipdsId":"IP-127938","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":500160,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114230.htm","linkFileType":{"id":5,"text":"html"}},{"id":422702,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/pp1877/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":411889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM8A1P","text":"USGS Data Release","linkHelpText":"MODFLOW 6 model and ensemble used in the simulation of groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system, 1897–2018"},{"id":422705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/pp1877/coverthb2.jpg"},{"id":411888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/pp1877/pp1877.pdf","text":"Report","size":"184 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.54245107965883,\n 31.199747848944256\n ],\n [\n -96.33297842340923,\n 30.997489619299927\n ],\n [\n -96.79440420465926,\n 30.136679255787612\n ],\n [\n -96.02536123590899,\n 28.551820525825022\n ],\n [\n -95.36068838434645,\n 28.86498475853952\n ],\n [\n -94.72348135309639,\n 29.28746086219381\n ],\n [\n -94.65207022028405,\n 29.402380282489133\n ],\n [\n -94.23458975153387,\n 29.574516044800063\n ],\n [\n -93.82809561090883,\n 29.670020494605353\n ],\n [\n -93.89401357965892,\n 29.803574466610613\n ],\n [\n -93.6907665093464,\n 30.05113045792723\n ],\n [\n -93.67428701715903,\n 30.307554456695556\n ],\n [\n -93.6687938530968,\n 30.563309394138372\n ],\n [\n -93.49301260309677,\n 30.841976559030968\n ],\n [\n -93.4765331109094,\n 31.077503645282718\n ],\n [\n -93.54245107965883,\n 31.199747848944256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 13, 2023; Version 1.1: November 28, 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Groundwater is the primary source of drinking water in the Coachella Valley in the desert region of southern California. Although most people in Coachella Valley are served by public drinking-water systems, about 20,000 people rely on private domestic or small-system wells (referred to herein as domestic wells). Recently, the U.S. Geological Survey (USGS) found that 39 percent of the groundwater resources used by domestic wells in Coachella Valley contained arsenic, fluoride, or both constituents at concentrations greater than the maximum contaminant levels established for public drinking-water systems. Uranium, chromium, nitrate, and perchlorate were detected at moderate concentrations below maximum contaminant levels. Elevated (above background) perchlorate concentrations in some areas indicate that domestic wells may receive recharge from Colorado River water used for irrigation or aquifer replenishment. Moderate total dissolved solids (TDS) concentrations throughout the study area and the co-occurrence of high concentrations of TDS and perchlorate indicates that Colorado River water is a source of recharge in the southeastern Indio groundwater subbasin. Four volatile organic compounds were detected at low concentrations, and pesticides and per- and polyfluoroalkyl substances were not detected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221122","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Soldavini, A.L., Harkness, J.S., Levy, Z.F., and Fram, M.S., 2023, Quality of groundwater used for domestic drinking-water supply in the Coachella Valley, 2020: U.S. Geological Survey Open-File Report 2022-1122, 6 p., https://doi.org/10.3133/ofr20221122.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127493","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411823,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UYXI95","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater-quality data in the Coachella Valley Domestic Supply Aquifer Study Unit, 2020: Results from the California GAMA Priority Basin Project (ver. 2.0, May 2022)"},{"id":411820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1122/coverthb.jpg"},{"id":411821,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1122/ofr20221122.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1122"},{"id":411824,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1122/images"},{"id":411825,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1122/ofr20221122.XML"},{"id":411822,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221122/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1122"},{"id":499728,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114228.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.98413827160624,\n 32.63858258656499\n ],\n [\n -114.72345711926295,\n 32.70563059371426\n ],\n [\n -114.70423104504415,\n 32.728738925902874\n ],\n [\n -114.63007333020037,\n 32.71718550821652\n ],\n [\n -114.51746346691932,\n 32.74491119548779\n ],\n [\n -114.51197030285675,\n 32.79110150468742\n ],\n [\n -114.45429208020033,\n 32.8303444161111\n ],\n [\n -114.45154549816922,\n 32.95718796101524\n ],\n [\n -114.4982373927005,\n 33.03320684616142\n ],\n [\n -114.55316903332549,\n 33.04932364290714\n ],\n [\n -114.605354091919,\n 33.042416805374316\n ],\n [\n -114.65753915051286,\n 33.05853191700412\n ],\n [\n -114.6904981348879,\n 33.104558838077764\n ],\n [\n -114.66303231457542,\n 33.178151787352334\n ],\n [\n -114.65479256848174,\n 33.26087003826156\n ],\n [\n -114.7179639552004,\n 33.299904238797865\n ],\n [\n -114.6904981348879,\n 33.348098849652715\n ],\n [\n -114.69599129895046,\n 33.39855984369588\n ],\n [\n -114.6218335841067,\n 33.41231685135915\n ],\n [\n -114.59711434582533,\n 33.47190536957288\n ],\n [\n -114.51471688488786,\n 33.54518886895207\n ],\n [\n -114.525703213013,\n 33.58638355432218\n ],\n [\n -114.5092237208253,\n 33.66871393677968\n ],\n [\n -114.48450448254427,\n 33.698425009231286\n ],\n [\n -114.51471688488786,\n 33.77608232074\n ],\n [\n -114.80860116223127,\n 33.654997671177824\n ],\n [\n -115.12445809582485,\n 33.760099797805324\n ],\n [\n -115.27826668957496,\n 33.85366929765736\n ],\n [\n -115.35791756848131,\n 33.928906968365624\n ],\n [\n -115.44580819348133,\n 33.919790785917314\n ],\n [\n -115.54743172863758,\n 33.83085690437406\n ],\n [\n -115.56391122082495,\n 33.74639812007295\n ],\n [\n -115.74243905285608,\n 33.730410062240736\n ],\n [\n -115.95941903332486,\n 33.73497838280909\n ],\n [\n -116.16541268566883,\n 33.91523232887485\n ],\n [\n -116.37689950207502,\n 34.01773859930471\n ],\n [\n -116.53620125988738,\n 34.03594899816602\n ],\n [\n -116.65979745129357,\n 34.01090869230403\n ],\n [\n -116.6735303614498,\n 33.899275808947905\n ],\n [\n -116.57465340832499,\n 33.796626897998294\n ],\n [\n -116.38788583019982,\n 33.670999768438705\n ],\n [\n -116.24781014660628,\n 33.53603182590945\n ],\n [\n -116.13794686535634,\n 33.31597207046798\n ],\n [\n -116.27802254894988,\n 33.41690203644977\n ],\n [\n -116.43183114269999,\n 33.407731424191894\n ],\n [\n -116.45380379894993,\n 33.22181838755475\n ],\n [\n -116.39612557629384,\n 33.129863375314955\n ],\n [\n -116.17639901379363,\n 33.042416805374316\n ],\n [\n -116.50598885754378,\n 33.042416805374316\n ],\n [\n -116.4153516505123,\n 32.90647233908673\n ],\n [\n -116.32196786145008,\n 32.84649822426155\n ],\n [\n -116.20111825207499,\n 32.70563059371426\n ],\n [\n -115.98413827160624,\n 32.63858258656499\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"GAMA Project Chief
U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
Unit Chief State Water Resources Control Board Division of Water Quality
P.O. Box 2231, Sacramento, CA 95812
Telephone number: (916) 341-5779
Differences in the life history pathways (LHPs) of juvenile animals are often associated with differences in demographic rates in later life stages. For migratory animals, different LHPs often result in animals from the same population occupying distinct habitats subjected to different environmental drivers. Understanding how demographic rates differ among animals expressing different LHPs may reveal fitness trade-offs that drive the expression of alternative LHPs and enable better prediction of population dynamics in a changing environment. To understand how demographic outcomes and their relationships with environmental variables differ among animals with different LHPs, we analyzed a long-term (2006–2021) mark–recapture dataset for Chinook salmon (Oncorhynchus tshawytscha) from the Wenatchee River, Washington, USA. Distinct LHPs represented in this population include either remaining in the natal stream until emigrating to the ocean as a 1-year-old (natal-reach rearing) or emigrating from the natal stream and rearing in downstream habitats for several months before completing the emigration to the ocean as a 1-year-old (downstream rearing). We found that downstream-rearing fish emigrated to the ocean 19 days earlier on average and returned as adults from the ocean at higher rates. We detected a positive correlation between rate of return from the ocean by downstream-rearing fish and coastal upwelling in their spring of outmigration, whereas for natal-reach-rearing fish we detected a positive correlation with sea surface temperature during their first marine summer. Different responses to environmental variability should lead to asynchrony in adult abundance among juvenile LHPs. A higher proportion of downstream-rearing fish returned at younger ages compared with natal-reach-rearing fish, which contributed to variability in age at reproduction and greater mixing across generations. Our results demonstrate how diversity in juvenile LHPs is associated with heterogeneity in demographic rates during subsequent life stages, which can in turn affect variance in aggregate population abundance and response to environmental change. Our findings underscore the importance of considering life history diversity in demographic analyses and provide insights into the effects of life history diversity on population dynamics and trade-offs that contribute to the maintenance of life history diversity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4366","usgsCitation":"Sorel, M.H., Murdoch, A.R., Zabel, R.W., Jorgensen, J.C., Kamphaus, C.M., and Converse, S.J., 2023, Juvenile life history diversity is associated with lifetime individual heterogeneity in a migratory fish: Ecosphere, v. 14, no. 1, e4366, 14 p., https://doi.org/10.1002/ecs2.4366.","productDescription":"e4366, 14 p.","ipdsId":"IP-141079","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4366","text":"Publisher Index Page"},{"id":430212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Wenatchee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.2,\n 48.2\n ],\n [\n -121.2,\n 47.4\n ],\n [\n -120.2,\n 47.4\n ],\n [\n -120.2,\n 48.2\n ],\n [\n -121.2,\n 48.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":903770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murdoch, Andrew R.","contributorId":339213,"corporation":false,"usgs":false,"family":"Murdoch","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":903771,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zabel, Richard W.","contributorId":272049,"corporation":false,"usgs":false,"family":"Zabel","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903772,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jorgensen, Jeffrey C.","contributorId":339208,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":903773,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kamphaus, Cory M.","contributorId":339215,"corporation":false,"usgs":false,"family":"Kamphaus","given":"Cory","email":"","middleInitial":"M.","affiliations":[{"id":39287,"text":"Yakama Nation Fisheries","active":true,"usgs":false}],"preferred":false,"id":903774,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903775,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}