Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2022.782922","usgsCitation":"Lara-Valencia, F., Garcia, M., Norman, L., Anides Morales, A., and Castellanos-Rubio, E.E., 2022, Integrating urban planning and water management through green infrastructure in the United States-Mexico border: Frontiers in Water, v. 4, 782922, 17 p., https://doi.org/10.3389/frwa.2022.782922.","productDescription":"782922, 17 p.","ipdsId":"IP-132393","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2022.782922","text":"Publisher Index Page"},{"id":395668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0772705078125,\n 31.23159167205059\n ],\n [\n -110.8685302734375,\n 31.23159167205059\n ],\n [\n -110.8685302734375,\n 31.423975737976697\n ],\n [\n -111.0772705078125,\n 31.423975737976697\n ],\n [\n -111.0772705078125,\n 31.23159167205059\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lara-Valencia, Francisco","contributorId":275344,"corporation":false,"usgs":false,"family":"Lara-Valencia","given":"Francisco","affiliations":[{"id":56763,"text":"Arizona State University, Phoenix, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":834019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Margaret","contributorId":275345,"corporation":false,"usgs":false,"family":"Garcia","given":"Margaret","email":"","affiliations":[{"id":56763,"text":"Arizona State University, Phoenix, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":834020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":834021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anides Morales, Alma","contributorId":275346,"corporation":false,"usgs":false,"family":"Anides Morales","given":"Alma","email":"","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":834022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Castellanos-Rubio, Edgar E.","contributorId":275347,"corporation":false,"usgs":false,"family":"Castellanos-Rubio","given":"Edgar","email":"","middleInitial":"E.","affiliations":[{"id":56764,"text":"Instituto Municipal de Investigación y Planeación de Nogales Sonora","active":true,"usgs":false}],"preferred":false,"id":834023,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231295,"text":"70231295 - 2022 - Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores","interactions":[],"lastModifiedDate":"2022-05-06T13:59:17.797111","indexId":"70231295","displayToPublicDate":"2022-02-01T09:46:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores","docAbstract":"We present results of slow (multiple day) depressurization experiments of pressure cores recovered from Green Canyon Block 955 in the northern Gulf of Mexico during The University of Texas at Austin Hydrate Pressure Coring Expedition (UT-GOM2-1). These stepwise depressurization experiments monitored the pressure and temperature within the core storage chamber during each pressure step, or “shut-in” period to better understand dissociation behavior and to provide insight on the thermodynamic state of gas hydrate reservoirs during production. The pressure rebound that occurs in response to a depressurization step occurs more slowly during later dissociation steps, likely reflecting a slower heat transfer rate, decreasing salinity gradient, and increased compressibility of the pore and surrounding fluids with progressive dissociation. We demonstrate that displacement of water by gas within the core storage chamber during successive dissociations both insulates the core and increases the compressibility of the pore and chamber fluid. The increased compressibility requires that a larger hydrate volume dissociates per unit of pressure recovery. Pressures observed during progressive dissociation steps are lower than predicted by the sample’s average salinity, with pressures approaching the freshwater phase boundary during frequent dissociation steps, suggesting that local pore-water freshening strongly influences dissociation behavior. To avoid underestimating the magnitude of pressure drawdown required to sustain dissociation in the reservoir, we suggest that hydrate production models use the freshwater phase boundary rather than a phase boundary determined from bulk salinity.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08182120216","usgsCitation":"Phillips, S.C., Flemings, P., You, K., and Waite, W., 2022, Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores: AAPG Bulletin, v. 106, no. 5, p. 1025-1049, https://doi.org/10.1306/08182120216.","productDescription":"25 p.","startPage":"1025","endPage":"1049","ipdsId":"IP-125570","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":400207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Green Canyon Block 955 (GC 955) study area, northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96,\n 25\n ],\n [\n -88,\n 25\n ],\n [\n -88,\n 30\n ],\n [\n -96,\n 30\n ],\n [\n -96,\n 25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Stephen C. 0000-0003-0858-4701","orcid":"https://orcid.org/0000-0003-0858-4701","contributorId":268177,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":842257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flemings, Peter B.","contributorId":242641,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":842258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"You, Kehua","contributorId":239915,"corporation":false,"usgs":false,"family":"You","given":"Kehua","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":842259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":842260,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236587,"text":"70236587 - 2022 - Predicting flood damage probability across the conterminous United States","interactions":[],"lastModifiedDate":"2022-09-12T14:44:05.940487","indexId":"70236587","displayToPublicDate":"2022-02-01T09:26:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Predicting flood damage probability across the conterminous United States","docAbstract":"Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of flood damage probability for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (n = 71,434) and a suite of geospatial predictors (e.g., flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a flood damage probability for each 100-m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g., forest) resulting in higher error rates than others (e.g., wetlands). Our results identified flood damage probability hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating flood damage probability estimates across the nation.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac4f0f","usgsCitation":"Collins, E., Sanchez, G., Terando, A., Stillwell, C.C., Mitasova, H., Sebastian, A., and Meentemeyer, R.K., 2022, Predicting flood damage probability across the conterminous United States: Environmental Research Letters, v. 17, 034006, 15 p., https://doi.org/10.1088/1748-9326/ac4f0f.","productDescription":"034006, 15 p.","ipdsId":"IP-133941","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":448948,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac4f0f","text":"Publisher Index Page"},{"id":435984,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954TTQN","text":"USGS data release","linkHelpText":"Data and Code for Predicting Flood Damage Probability Across the Conterminous United States"},{"id":406535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2022-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, Elyssa 0000-0002-8054-8468","orcid":"https://orcid.org/0000-0002-8054-8468","contributorId":294952,"corporation":false,"usgs":false,"family":"Collins","given":"Elyssa","email":"","affiliations":[{"id":63800,"text":"Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA","active":true,"usgs":false}],"preferred":false,"id":851449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":210477,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stillwell, Charles C. 0000-0002-4571-4897","orcid":"https://orcid.org/0000-0002-4571-4897","contributorId":270394,"corporation":false,"usgs":true,"family":"Stillwell","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mitasova, Helena 0000-0002-6906-3398","orcid":"https://orcid.org/0000-0002-6906-3398","contributorId":296416,"corporation":false,"usgs":false,"family":"Mitasova","given":"Helena","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sebastian, Antonia 0000-0002-4309-2561","orcid":"https://orcid.org/0000-0002-4309-2561","contributorId":296417,"corporation":false,"usgs":false,"family":"Sebastian","given":"Antonia","email":"","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":851454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851455,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236483,"text":"70236483 - 2022 - General guidance for custom-built structural equation models","interactions":[],"lastModifiedDate":"2022-09-09T10:55:46.482391","indexId":"70236483","displayToPublicDate":"2022-02-01T09:23:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"General guidance for custom-built structural equation models","docAbstract":"Structural Equation Modeling (SEM) represents a quantitative methodology for specifying and evaluating causal network hypotheses. The application of SEM typically involves the use of specialized software packages that implement estimation procedures and automate model checking and the output of summary results. There are times when the specification details an investigator wishes to implement to represent their data relationships are not supported by available SEM packages. In such cases, it may be desirable to develop and evaluate SE models “by hand”, using specialized regression tools. In this paper, I demonstrate a general approach to custom-built applications of SEM. The approach illustrated can be used for a wide array of specialized applications of non-linear, multi-level, and other custom specifications in SE models.","language":"English","publisher":"Pensoft Publishers","doi":"10.3897/oneeco.7.e72780","usgsCitation":"Grace, J., 2022, General guidance for custom-built structural equation models: One Ecosystem, v. 7, e72780, 13 p., https://doi.org/10.3897/oneeco.7.e72780.","productDescription":"e72780, 13 p.","ipdsId":"IP-132365","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":448951,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.7.e72780","text":"Publisher Index Page"},{"id":406379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.28380584716795,\n 44.41514524311076\n ],\n [\n -68.28861236572266,\n 44.411711905198004\n ],\n [\n -68.28895568847656,\n 44.40484462467532\n ],\n [\n -68.2803726196289,\n 44.403618239739515\n ],\n [\n -68.28243255615234,\n 44.396504700115536\n ],\n [\n -68.27659606933592,\n 44.37785821716272\n ],\n [\n -68.2855224609375,\n 44.377122049920416\n ],\n [\n -68.28311920166016,\n 44.381784286142434\n ],\n [\n -68.29479217529297,\n 44.38669150215206\n ],\n [\n -68.31161499023438,\n 44.382520394767475\n ],\n [\n -68.32122802734375,\n 44.37295026072434\n ],\n [\n -68.31607818603516,\n 44.363869456900595\n ],\n [\n -68.30646514892578,\n 44.36067856998804\n ],\n [\n -68.29032897949219,\n 44.36313311380771\n ],\n [\n -68.28998565673828,\n 44.359942186789134\n ],\n [\n -68.30303192138672,\n 44.349140836553076\n ],\n [\n -68.30509185791016,\n 44.340056341964114\n ],\n [\n -68.30852508544922,\n 44.328023358092224\n ],\n [\n -68.32157135009766,\n 44.327777761284416\n ],\n [\n -68.32569122314453,\n 44.33146160542641\n ],\n [\n -68.34457397460938,\n 44.322865609179345\n ],\n [\n -68.34697723388672,\n 44.3177074067743\n ],\n [\n -68.35590362548828,\n 44.315987905196906\n ],\n [\n -68.36311340332031,\n 44.32974250695349\n ],\n [\n -68.3737564086914,\n 44.337109717061864\n ],\n [\n -68.37272644042969,\n 44.34889532818253\n ],\n [\n -68.3792495727539,\n 44.35626013196649\n ],\n [\n -68.39298248291014,\n 44.35527821160296\n ],\n [\n -68.3902359008789,\n 44.33637303769866\n ],\n [\n -68.40843200683594,\n 44.33661859851472\n ],\n [\n -68.4122085571289,\n 44.33170718680922\n ],\n [\n -68.40259552001953,\n 44.319918120477425\n ],\n [\n -68.39950561523438,\n 44.30935506997107\n ],\n [\n -68.39710235595703,\n 44.29141809546585\n ],\n [\n -68.39984893798828,\n 44.2783919270538\n ],\n [\n -68.3737564086914,\n 44.26831309827905\n ],\n [\n -68.37890625,\n 44.263395969186675\n ],\n [\n -68.3730697631836,\n 44.25823254096765\n ],\n [\n -68.35418701171874,\n 44.25552770711544\n ],\n [\n -68.34423065185547,\n 44.25060950862162\n ],\n [\n -68.33667755126953,\n 44.23782026763518\n ],\n [\n -68.33358764648438,\n 44.22945656830167\n ],\n [\n -68.34388732910156,\n 44.22601234661563\n ],\n [\n -68.33942413330078,\n 44.22109168032017\n ],\n [\n -68.31916809082031,\n 44.222567923394735\n ],\n [\n -68.3023452758789,\n 44.23437653539356\n ],\n [\n -68.29444885253906,\n 44.24323143968471\n ],\n [\n -68.31127166748047,\n 44.24716652494939\n ],\n [\n -68.3133316040039,\n 44.255281806959275\n ],\n [\n -68.33118438720702,\n 44.26093725039923\n ],\n [\n -68.33736419677734,\n 44.25700308645885\n ],\n [\n -68.33736419677734,\n 44.27617964929813\n ],\n [\n -68.34354400634766,\n 44.2867486691176\n ],\n [\n -68.3517837524414,\n 44.2867486691176\n ],\n [\n -68.34354400634766,\n 44.297070062271175\n ],\n [\n -68.33942413330078,\n 44.29903583625904\n ],\n [\n -68.33221435546875,\n 44.297070062271175\n ],\n [\n -68.3236312866211,\n 44.29952726947296\n ],\n [\n -68.3133316040039,\n 44.29731578761873\n ],\n [\n -68.30337524414062,\n 44.31009208868226\n ],\n [\n -68.28174591064453,\n 44.307389641495384\n ],\n [\n -68.2638931274414,\n 44.307389641495384\n ],\n [\n -68.25084686279297,\n 44.31353138696479\n ],\n [\n -68.24913024902344,\n 44.30861804200505\n ],\n [\n -68.23230743408203,\n 44.299281553380155\n ],\n [\n -68.22269439697266,\n 44.302230078625456\n ],\n [\n -68.21617126464844,\n 44.29362979943358\n ],\n [\n -68.19007873535156,\n 44.30468706988256\n ],\n [\n -68.17188262939453,\n 44.327777761284416\n ],\n [\n -68.17256927490234,\n 44.33735527479289\n ],\n [\n -68.17806243896484,\n 44.34938634389529\n ],\n [\n -68.1808090209961,\n 44.36067856998804\n ],\n [\n -68.18286895751952,\n 44.36902359940364\n ],\n [\n -68.19282531738281,\n 44.37663126661702\n ],\n [\n -68.20690155029297,\n 44.37785821716272\n ],\n [\n -68.22269439697266,\n 44.386200799061896\n ],\n [\n -68.22269439697266,\n 44.395278140353646\n ],\n [\n -68.2357406616211,\n 44.40803310518651\n ],\n [\n -68.24810028076172,\n 44.41318336041361\n ],\n [\n -68.26663970947266,\n 44.41710705999032\n ],\n [\n -68.28380584716795,\n 44.41514524311076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":220095,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":851191,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227733,"text":"sir20215139 - 2022 - Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","interactions":[],"lastModifiedDate":"2026-04-08T16:30:38.872221","indexId":"sir20215139","displayToPublicDate":"2022-02-01T08:15:45","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5139","displayTitle":"Simulation of Groundwater and Surface-Water Resources of the San Antonio Creek Valley Watershed, Santa Barbara County, California","title":"Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","docAbstract":"In the San Antonio Creek Valley watershed (SACVW), western Santa Barbara County, California, groundwater is the primary source of water for agricultural irrigation, the town of Los Alamos, and supplemental water to Vandenberg Space Force Base (VSFB). Groundwater pumpage has increased since the 1970s as non-irrigated agricultural land has been converted to irrigated land and as local pumping for municipal use has increased. This increase in groundwater use has resulted in declining groundwater levels, adjustments in surface-water flows and species habitats, and changes in water quality. Water managers are addressing the challenges of meeting this increased demand while maintaining sustainable groundwater supplies. To address these challenges, Santa Barbara County Water Agency, Vandenberg Space Force Base (VSFB), and the U.S. Geological Survey (USGS) undertook a cooperative study to characterize the integrated hydrologic system of the SACVW and develop tools to better understand and manage the groundwater system. The objectives of this study were to improve the understanding of the integrated hydrologic system and incorporate the understanding into an integrated groundwater and surface-water flow model that can be used to help manage the water resources in the SACVW.
The San Antonio Creek integrated model (SACIM) was developed using the USGS coupled groundwater and surface-water flow model to simulate the hydrologic system of the SACVW and provide annual and average water budgets for 1948–2018 water years. Results from the SACIM indicated that between 1948 and 2018, total groundwater from storage (storage depletion) for the period was 453,300 acre-feet (acre-ft). Agricultural pumpage was the largest discharge and accounted for a total of 1,020,000 acre-ft of groundwater. Increased pumpage since the mid-1980s (of which agricultural pumpage is the primary component) is tied to an increased rate of storage depletion and reduced rates of groundwater evapotranspiration and surface leakage (groundwater discharge to the surface and soil zone). The increased pumpage also reduced subsurface inflow to Barka Slough, resulting in a decline in upward flow through the underlying hydrogeologic units and surface leakage. In addition to quantifying historical changes in the integrated hydrologic system, the SACIM is a tool than can be used by water managers to evaluate the effects of different climatic and hydrologic conditions and management strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215139","collaboration":"Prepared in cooperation with Santa Barbara County Water Agency and Vandenberg Space Force Base","programNote":"Groundwater Availability and Use Assessments","usgsCitation":"Woolfenden, L.R., Engott, J.A., Larsen, J.D., and Cromwell, G., 2022, Simulation of groundwater and surface-water resources of the San Antonio Creek Valley Watershed, Santa Barbara County, California: U.S. Geological Survey Scientific Investigations Report 2021–5139, 76 p., https://doi.org/10.3133/sir20215139.","productDescription":"Report: xii, 76 p.; Data Release","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-108916","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502284,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112321.htm","linkFileType":{"id":5,"text":"html"}},{"id":394985,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5139/covrthb.png"},{"id":394986,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5139/sir20215139.pdf","text":"Report","size":"11 Mb","linkFileType":{"id":1,"text":"pdf"}},{"id":394989,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225001","text":"Scientific Investigations Report 2022–5001","linkHelpText":"- Hydrogeologic characterization of the San Antonio Creek Valley watershed, Santa Barbara County, California"},{"id":394984,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P960EOK8","linkHelpText":"GSFLOW model used to evaluate the groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California"}],"country":"United States","state":"California","county":"Santa Barbara County","otherGeospatial":"San Antonio Creek Valley watershed,","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.61889648437501,\n 34.37517887533528\n ],\n [\n -119.75,\n 34.37517887533528\n ],\n [\n -119.75,\n 35\n ],\n [\n -120.61889648437501,\n 35\n ],\n [\n -120.61889648437501,\n 34.37517887533528\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (VTotal) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (
Forested stream ecosystems involve complex physical and biotic pathways that can influence fish in numerous ways. Consequently, the responses of fish communities to disturbance can be difficult to understand. In this study, we employed a food web model that links biotic (e.g., physiology, predator–prey interactions) and abiotic (e.g., temperature, sunlight) attributes to address fish responses to changes in stream-riparian ecosystems. We modeled responses to food web dynamics in four streams, using scenarios that included responses to riparian disturbance, climate change, and shifts in top consumers. The two consumers we focused on were coastal cutthroat trout (Oncorhynchus clarkii clarkii) and sculpin (Cottus spp., collectively treated as a functional group). We found the responses to environmental changes varied by fish species and among streams, and that responses were not independent due to exploitative interspecific competition. Simulations based on long-term data indicated that coastal cutthroat trout were responsive to changes in allochthonous resources including terrestrial detritus and invertebrates, whereas sculpin were more responsive to changes to autochthonous resources that included, periphyton and aquatic invertebrates. These results may be, in part, a consequence of species-specific foraging behavior. Trout have a higher propensity to drift feed and therefore receive a substantial subsidy from terrestrial invertebrates, whereas sculpin feed mostly on aquatic insects on the streambed. Simulations of changes in summer temperature and stream discharge suggest decreased biomass of both fish species because of physiological constraints on invertebrate prey which reduce fish foraging opportunities. Exploitative competition also may be important in fish responses: when one fish taxon was removed, the other showed increased biomass. Although the pattern of simulation results was consistent across the four streams, the magnitude of change varied among streams. Streams with food webs fueled by multiple energy sources may be more resilient to changes to riparian forests and climate. Through application of a systems model, we gained insights into pathways of productivity for fish in forested stream ecosystems that provide understanding of processes that influence fish and streams, as well as implications for management of both.
Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47 km of stream length across the Farmington River watershed (1,570 km2; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.
Sex‐related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture–recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex‐specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the “unguarded X/Z effect”) or repeat‐rich Y/W chromosome (the “toxic Y/W effect”) could accelerate aging in the heterogametic sex in some vertebrate clades.
","language":"English","publisher":"Oxford Academic","doi":"10.1111/evo.14410","usgsCitation":"Cayuela, H., Lemaître, J., Léna, J., Ronget, V., Martinez-Solano, I., Muths, E.L., Pilliod, D., Schmidt, B., Sanchez-Montes, G., Gutierrez-Rodriguez, J., Pyke, G., Grossenbacher, K., Lenzi, O., Bosch, J., Beard, K.H., Woolbright, L.L., Lambert, B., Green, D.M., Garwood, J.M., Fisher, R., Matthews, K., Dudgeon, D., Lau, A., Speybroeck, J., Homan, R., Jehle, R., Baskale, E., Mori, E., Arntzen, J.W., Joly, P., Stiles, R., Lannoo, M.J., Maerz, J.C., Lowe, W., Valenzuela-Sanchez, A., Christianson, D., Angelini, C., Thirion, J., Merila, J., Colli, G.R., Vasconcellos, M.M., Boas, T.C., Arantes, I.D., Levionnois, P., Reinke, B., Vieira, C., Marais, G.A., Gaillard, J., and Miller, D., 2022, Sex‐related differences in aging rate are associated with sex chromosome system in amphibians: Evolution, v. 76, no. 2, p. 346-356, https://doi.org/10.1111/evo.14410.","productDescription":"10 p.","startPage":"346","endPage":"356","ipdsId":"IP-122653","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/evo.14410","text":"Publisher Index Page"},{"id":414762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Cayuela, Hugo","contributorId":303576,"corporation":false,"usgs":false,"family":"Cayuela","given":"Hugo","affiliations":[{"id":65798,"text":"Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":867574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lemaître, Jean-François","contributorId":303577,"corporation":false,"usgs":false,"family":"Lemaître","given":"Jean-François","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Léna, Jean-Paul","contributorId":303578,"corporation":false,"usgs":false,"family":"Léna","given":"Jean-Paul","affiliations":[{"id":65800,"text":"Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR5023 LEHNA, F-69622 Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ronget, Victor","contributorId":303579,"corporation":false,"usgs":false,"family":"Ronget","given":"Victor","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinez-Solano, Inigo","contributorId":303580,"corporation":false,"usgs":false,"family":"Martinez-Solano","given":"Inigo","email":"","affiliations":[{"id":65801,"text":"Museo Nacional de Ciencias Naturales, CSIC, c/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain","active":true,"usgs":false}],"preferred":false,"id":867578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":867579,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":303582,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":867580,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmidt, Benedikt","contributorId":303583,"corporation":false,"usgs":false,"family":"Schmidt","given":"Benedikt","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":867581,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sanchez-Montes, Gregorio","contributorId":303584,"corporation":false,"usgs":false,"family":"Sanchez-Montes","given":"Gregorio","email":"","affiliations":[{"id":65801,"text":"Museo Nacional de Ciencias Naturales, CSIC, c/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain","active":true,"usgs":false}],"preferred":false,"id":867582,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gutierrez-Rodriguez, Jorge","contributorId":303585,"corporation":false,"usgs":false,"family":"Gutierrez-Rodriguez","given":"Jorge","email":"","affiliations":[{"id":65801,"text":"Museo Nacional de Ciencias Naturales, CSIC, c/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain","active":true,"usgs":false}],"preferred":false,"id":867583,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pyke, Graham","contributorId":303586,"corporation":false,"usgs":false,"family":"Pyke","given":"Graham","affiliations":[{"id":65802,"text":"Department of Biological Sciences, Macquarie University, Ryde, NSW, 2109, Australia","active":true,"usgs":false}],"preferred":false,"id":867584,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Grossenbacher, Kurt","contributorId":303587,"corporation":false,"usgs":false,"family":"Grossenbacher","given":"Kurt","affiliations":[{"id":65803,"text":"Abteilung Wirbeltiere, Naturhistorisches Museum,Bernastrasse 15, 3005 Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":867585,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lenzi, Omar","contributorId":303588,"corporation":false,"usgs":false,"family":"Lenzi","given":"Omar","affiliations":[{"id":65804,"text":"Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zürich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":867586,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bosch, Jaime","contributorId":167060,"corporation":false,"usgs":false,"family":"Bosch","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":867587,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Beard, Karen H.","contributorId":205934,"corporation":false,"usgs":false,"family":"Beard","given":"Karen","email":"","middleInitial":"H.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":867588,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Woolbright, Lawrence L.","contributorId":303589,"corporation":false,"usgs":false,"family":"Woolbright","given":"Lawrence","email":"","middleInitial":"L.","affiliations":[{"id":65805,"text":"Biology Department, Siena College, 515 Loudon Road, Loudonville, New York 12211, USA","active":true,"usgs":false}],"preferred":false,"id":867589,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Lambert, Brad A.","contributorId":245925,"corporation":false,"usgs":false,"family":"Lambert","given":"Brad A.","affiliations":[{"id":27518,"text":"Colorado Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":867590,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Green, David M.","contributorId":169671,"corporation":false,"usgs":false,"family":"Green","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":867591,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Garwood, Justin M","contributorId":217674,"corporation":false,"usgs":false,"family":"Garwood","given":"Justin","email":"","middleInitial":"M","affiliations":[{"id":39681,"text":"California Dept fish wildlife","active":true,"usgs":false}],"preferred":false,"id":867592,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867593,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Matthews, Kathleen","contributorId":171577,"corporation":false,"usgs":false,"family":"Matthews","given":"Kathleen","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":867594,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Dudgeon, David","contributorId":268103,"corporation":false,"usgs":false,"family":"Dudgeon","given":"David","email":"","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":867595,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Lau, Anthony","contributorId":303590,"corporation":false,"usgs":false,"family":"Lau","given":"Anthony","email":"","affiliations":[{"id":65806,"text":"Science Unit, Lingnan University, Hong Kong, China","active":true,"usgs":false}],"preferred":false,"id":867596,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Speybroeck, Jeroen","contributorId":303591,"corporation":false,"usgs":false,"family":"Speybroeck","given":"Jeroen","email":"","affiliations":[{"id":65807,"text":"Research Institute for Nature and Forest, Havenlaan 88 bus 73, 1000 Brussel, Belgium","active":true,"usgs":false}],"preferred":false,"id":867597,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Homan, Rebecca","contributorId":303592,"corporation":false,"usgs":false,"family":"Homan","given":"Rebecca","email":"","affiliations":[{"id":65808,"text":"Biology Department, Denison University, Granville, Ohio, USA","active":true,"usgs":false}],"preferred":false,"id":867598,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Jehle, Robert","contributorId":303593,"corporation":false,"usgs":false,"family":"Jehle","given":"Robert","affiliations":[{"id":65809,"text":"School of Science, Engineering and Environment, University of Salford, Salford, UK","active":true,"usgs":false}],"preferred":false,"id":867599,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Baskale, Eyup","contributorId":303594,"corporation":false,"usgs":false,"family":"Baskale","given":"Eyup","email":"","affiliations":[{"id":65810,"text":"Department of Biology, Faculty of Science and Arts, Pamukkale University, Denizli, Turkey","active":true,"usgs":false}],"preferred":false,"id":867600,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Mori, Emiliano","contributorId":303595,"corporation":false,"usgs":false,"family":"Mori","given":"Emiliano","email":"","affiliations":[{"id":65811,"text":"Consiglio Nazionale delle Ricerche, Istituto di Ricerca sugli Ecosistemi Terrestri, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy","active":true,"usgs":false}],"preferred":false,"id":867601,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Arntzen, Jan W.","contributorId":303596,"corporation":false,"usgs":false,"family":"Arntzen","given":"Jan","email":"","middleInitial":"W.","affiliations":[{"id":35982,"text":"Naturalis biodiversity Center, Leiden, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":867602,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Joly, Pierre","contributorId":303597,"corporation":false,"usgs":false,"family":"Joly","given":"Pierre","email":"","affiliations":[{"id":65800,"text":"Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR5023 LEHNA, F-69622 Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867603,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Stiles, Rochelle","contributorId":303598,"corporation":false,"usgs":false,"family":"Stiles","given":"Rochelle","affiliations":[{"id":65812,"text":"San Francisco Zoological Society, 1 Zoo Road, San Francisco, California, 94132, USA","active":true,"usgs":false}],"preferred":false,"id":867604,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Lannoo, Michael J","contributorId":229350,"corporation":false,"usgs":false,"family":"Lannoo","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":867605,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Maerz, John C.","contributorId":171763,"corporation":false,"usgs":false,"family":"Maerz","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":867606,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Lowe, Winsor H.","contributorId":64532,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor H.","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":867607,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Valenzuela-Sanchez, Andres","contributorId":256640,"corporation":false,"usgs":false,"family":"Valenzuela-Sanchez","given":"Andres","email":"","affiliations":[{"id":51816,"text":"1Instituto de Ciencias Ambientales y Evolutivas, Universidad Austral de Chile, Valdivia, Chile","active":true,"usgs":false}],"preferred":false,"id":867608,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Christianson, Ditte","contributorId":303599,"corporation":false,"usgs":false,"family":"Christianson","given":"Ditte","email":"","affiliations":[{"id":27368,"text":"University of Zurich","active":true,"usgs":false}],"preferred":false,"id":867609,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Angelini, Claudio","contributorId":303600,"corporation":false,"usgs":false,"family":"Angelini","given":"Claudio","affiliations":[{"id":65813,"text":"Salamandrina Sezzese Search Society, via G. Marconi 30, 04018 Sezze, Italy","active":true,"usgs":false}],"preferred":false,"id":867610,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Thirion, Jean-Marc","contributorId":303601,"corporation":false,"usgs":false,"family":"Thirion","given":"Jean-Marc","email":"","affiliations":[{"id":65814,"text":"Objectifs Biodiversité, 22 rue du Dr. Gilbert, 17250 Pont-l’Abbé-d’Arnoult, France","active":true,"usgs":false}],"preferred":false,"id":867611,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Merila, Juha","contributorId":303602,"corporation":false,"usgs":false,"family":"Merila","given":"Juha","affiliations":[{"id":65815,"text":"Ecological Genetics Research Unit, Research Programme in Organismal and Evolutionary Biology, Faculty of Biological and Environmental Sciences, University of Helsinki 00014 Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":867612,"contributorType":{"id":1,"text":"Authors"},"rank":39},{"text":"Colli, Guarino R.","contributorId":291665,"corporation":false,"usgs":false,"family":"Colli","given":"Guarino","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":867613,"contributorType":{"id":1,"text":"Authors"},"rank":40},{"text":"Vasconcellos, Mariana M.","contributorId":303603,"corporation":false,"usgs":false,"family":"Vasconcellos","given":"Mariana","email":"","middleInitial":"M.","affiliations":[{"id":65816,"text":"Department of Ecosystem Sciences and Management, The Pennsylvania State University, University Park, Pennsylvania, USA","active":true,"usgs":false}],"preferred":false,"id":867614,"contributorType":{"id":1,"text":"Authors"},"rank":41},{"text":"Boas, Taissa C.","contributorId":303604,"corporation":false,"usgs":false,"family":"Boas","given":"Taissa","email":"","middleInitial":"C.","affiliations":[{"id":65817,"text":"Departamento de Zoologia, Universidade de Brasília, 70910-900 Brasília, Distrito Federal, Brazil","active":true,"usgs":false}],"preferred":false,"id":867615,"contributorType":{"id":1,"text":"Authors"},"rank":42},{"text":"Arantes, Isis da C.","contributorId":303605,"corporation":false,"usgs":false,"family":"Arantes","given":"Isis","email":"","middleInitial":"da C.","affiliations":[{"id":65818,"text":"Department of Biology, University of Mississippi, Oxford, MS 38677, USA","active":true,"usgs":false}],"preferred":false,"id":867616,"contributorType":{"id":1,"text":"Authors"},"rank":43},{"text":"Levionnois, Pauline","contributorId":245936,"corporation":false,"usgs":false,"family":"Levionnois","given":"Pauline","email":"","affiliations":[{"id":49371,"text":"6Office National des Forêts, Direction territoriale Grand Est, France","active":true,"usgs":false}],"preferred":false,"id":867617,"contributorType":{"id":1,"text":"Authors"},"rank":44},{"text":"Reinke, Beth A.","contributorId":303606,"corporation":false,"usgs":false,"family":"Reinke","given":"Beth A.","affiliations":[{"id":65819,"text":"Department of Biology, Northeastern Illinois University, 5500 North St. Louis Avenue, Chicago, IL 60625, USA","active":true,"usgs":false}],"preferred":false,"id":867618,"contributorType":{"id":1,"text":"Authors"},"rank":45},{"text":"Vieira, Cristina","contributorId":303607,"corporation":false,"usgs":false,"family":"Vieira","given":"Cristina","email":"","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867619,"contributorType":{"id":1,"text":"Authors"},"rank":46},{"text":"Marais, Gabriel A. B.","contributorId":303608,"corporation":false,"usgs":false,"family":"Marais","given":"Gabriel","email":"","middleInitial":"A. B.","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867620,"contributorType":{"id":1,"text":"Authors"},"rank":47},{"text":"Gaillard, Jean-Michael","contributorId":303609,"corporation":false,"usgs":false,"family":"Gaillard","given":"Jean-Michael","email":"","affiliations":[{"id":65799,"text":"Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-769622, Villeurbanne, France","active":true,"usgs":false}],"preferred":false,"id":867621,"contributorType":{"id":1,"text":"Authors"},"rank":48},{"text":"Miller, David A.W.","contributorId":198461,"corporation":false,"usgs":false,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":867622,"contributorType":{"id":1,"text":"Authors"},"rank":49}]}} ,{"id":70262481,"text":"70262481 - 2022 - Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A","interactions":[],"lastModifiedDate":"2025-01-17T15:42:51.223433","indexId":"70262481","displayToPublicDate":"2022-02-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A","docAbstract":"Amphibians are vital elements of ecosystems, serving as predator and prey. Their biphasic nature makes them dependent on aquatic and terrestrial habitats; as wet-skinned ectotherms, they are vulnerable to a range of environmental threats, including climate change. Yellowstone National Park (YNP) is becoming warmer and drier, and some wetlands important to amphibians have diminished. Continued climate change is predicted to reduce snowpack, soil moisture, and forest cover. We used data from models of future climate and vegetation cover to mechanistically model how climate change might affect the movements of Western Toads (Anaxyrus boreas) across the landscape of three test areas in YNP for the years 2050 and 2090, compared to 2000 as a baseline. Least-cost path analysis produced mixed results: for 2050 and 2090, physiological costs of movement increased in one test area and decreased in another; they were mixed in the third. These changes generally reflect the preference by toads for more open forests. Estimating costs for other species of YNP amphibians produced more negative results. For Columbia Spotted Frogs (Rana luteiventris) and Boreal Chorus Frogs (Pseudacris maculata) (both more aquatic and less adapted to terrestrial habitats), movement costs increased by about 2–15X. Reduced frequency or duration of rain events might limit the nocturnal movements of Western Tiger Salamanders (Ambystoma mavortium). Climate change may not have negative impacts on all amphibians throughout YNP, but increased movement costs for terrestrial habitats will accentuate effects of drying wetlands in at least parts of YNP. Land management actions that preserve habitat structure of both forest and low shrub cover may help mitigate continued drying conditions of climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108575","usgsCitation":"Bartelt, P., Thornton, P., and Klaver, R.W., 2022, Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A: Ecological Indicators, v. 135, 108575, 12 p., https://doi.org/10.1016/j.ecolind.2022.108575.","productDescription":"108575, 12 p.","ipdsId":"IP-134835","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108575","text":"Publisher Index Page"},{"id":480736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.13112916264933,\n 45.42187928380869\n ],\n [\n -111.13112916264933,\n 43.87067545981952\n ],\n [\n -109.15957965370097,\n 43.87067545981952\n ],\n [\n -109.15957965370097,\n 45.42187928380869\n ],\n [\n -111.13112916264933,\n 45.42187928380869\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"135","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bartelt, Paul E.","contributorId":349463,"corporation":false,"usgs":false,"family":"Bartelt","given":"Paul E.","affiliations":[{"id":56262,"text":"Waldorf University","active":true,"usgs":false}],"preferred":false,"id":924323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thornton, Peter E.","contributorId":349464,"corporation":false,"usgs":false,"family":"Thornton","given":"Peter E.","affiliations":[{"id":83486,"text":"Oak Ridge National Laborabory","active":true,"usgs":false}],"preferred":false,"id":924324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":924322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227700,"text":"ofr20211103 - 2022 - Climate change adaptation thinking for managed wetlands","interactions":[],"lastModifiedDate":"2026-03-25T17:44:25.037268","indexId":"ofr20211103","displayToPublicDate":"2022-01-31T12:17:25","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1103","displayTitle":"Climate Change Adaptation Thinking for Managed Wetlands","title":"Climate change adaptation thinking for managed wetlands","docAbstract":"Climate change presents new and ongoing challenges to natural resource management. To confront these challenges effectively, managers need to develop proactive adaptation strategies to prepare for and deal with the effects of climate change. We engaged managers and biologists from several midwestern U.S. Fish and Wildlife Service field stations to understand recent and future climate change effects, identify adaptation barriers and opportunities, and pilot an approach for integrating adaptation thinking into management planning. To start, three structured discussions informed our understanding of how managers currently deal with climate change effects, the strategies being implemented to cope, and the barriers that limit climate change adaptation efforts. We used these insights to develop a multiday virtual workshop geared toward identifying potential adaptation strategies for managed wetlands. First, we developed a conceptual model to visualize how management actions are used to meet habitat objectives within wetland management systems. Next, we discussed how climate change may affect management actions and objectives; we used this understanding of potential effects to spatially assess vulnerability of managed wetlands to climate change. Using a scenario planning approach, we incorporated multiple potential future conditions and identified effects and adaptation strategies that could be considered for each scenario. As a result, several adaptation strategies for managed wetlands under dry and wet future scenarios were identified that can be applied when developing site-specific adaptation plans. Based on our piloted approach, we determined it would be important to have an adaptation team composed of scientists and managers to facilitate discussions, develop appropriate scenarios, and identify realistic adaptation options. We document the tools, findings, and adaptation thinking process taken to enhance adaptation efforts of managed wetlands. The adaptation thinking process can be applied to advance adaptation efforts in other habitats, ecosystems, and site-specific land management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211103","usgsCitation":"Delaney, J.T., Bouska, K.L., and Eash, J.D., 2021, Climate Change Adaptation Thinking for Managed Wetlands: U.S. Geological Survey Open-File Report 2021–1103, 25 p., https://doi.org/10.3133/ofr20211103.","productDescription":"Report: vi, 25 p.; 3 Data Releases","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-128227","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":394943,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Watershed-based Midwest Climate Change Vulnerability Assessment Tool"},{"id":394942,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"R code: Scripts used to analyze data for the Midwest Climate Change Vulnerability Assessment"},{"id":394941,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AL7GZM","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Model inputs: Midwest climate change vulnerability assessment for the U.S. Fish and Wildlife Service"},{"id":394938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.pdf","text":"Report","size":"44.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1103"},{"id":394937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1103/coverthb.jpg"},{"id":501530,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112325.htm","linkFileType":{"id":5,"text":"html"}},{"id":394940,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1103/images"},{"id":394939,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1103/ofr20211103.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1103 XML"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
The San Antonio Creek Valley watershed (SACVW) is located in western Santa Barbara County, about 15 miles south of Santa Maria and 55 miles north of Santa Barbara, California. The SACVW is about 135 square miles and encompasses the San Antonio Creek Valley groundwater basin; the SACVW is separated from adjacent groundwater basins by the Casmalia and Solomon Hills to the north, and the Purisima Hills to the south. At the western, downstream part of the valley, uplifted, consolidated rocks cause groundwater to discharge at land surface at Barka Slough. Since the late 1800s, groundwater has been the primary source of water for agricultural, military, municipal, and domestic uses. Groundwater withdrawal by pumping exceeded the amount of water replenishing the aquifer system during water years 1948–2018, causing groundwater-level declines of more than 150 feet in parts of the valley and reducing base flow at Barka Slough. Reliance on groundwater for agricultural water use (primarily for the irrigation and frost protection of vineyards, and fruit and berry crops) continues to strain the sustainability of the groundwater system.
Through a cooperative agreement, the Santa Barbara County Water Agency and Vandenberg Space Force Base invited the U.S. Geological Survey to address declines in groundwater levels, develop a better understanding of the hydrogeologic system, and provide tools to help evaluate and manage the effects of future development of the San Antonio Creek Valley groundwater basin within the encompassing San Antonio Creek Valley watershed (SACVW). The objectives of this study were to (1) refine the hydrogeologic framework of the San Antonio Creek Valley watershed, (2) quantify the hydrologic budget of the valley, and (3) develop hydrologic modeling tools to evaluate and aid in managing the groundwater resource. This report focuses on the first and second objectives to construct a hydrogeologic framework and characterize the historical and present-day hydrologic conditions of the SACVW during water years 1948–2018. As part of the second objective, work included quantifying the hydrologic budget and evaluating the hydrogeologic system using a combination of existing data and geologic and hydrologic data collected for this study.
The groundwater-flow system in the SACVW consists of five hydrogeologic units. These separate water-bearing units were identified based on hydrogeologic properties, such as sediment grain size, vertical-head differences in multiple-depth, monitoring-well sites, long-term groundwater level responses to pumping and climate, and the chemical character of groundwater and groundwater age in the mostly semi-consolidated to unconsolidated basin-fill sediments. The hydrogeologic units that comprise the different aquifers vary in their lithologic composition. The upper and lower aquifers (upper Paso Robles Formation, and lower Paso Robles Formation and Careaga Sandstone, respectively) are relatively coarse grained and are comprised of sand, gravel, and clay; the middle confining unit (the middle Paso Robles Formation) is relatively fine grained and is comprised of primarily clay, silt, and sand. The Pezzoni-Casmalia and Los Alamos faults, which are inferred to transect the SACVW between the western and eastern areas of the valley floor, do not appear to substantially affect the groundwater system.
Present-day recharge to the study area occurs primarily as infiltration from precipitation and streams in the upland areas of the Casmalia Hills and Solomon Hills, and along the main channel of San Antonio Creek. Reported estimates of annual natural recharge during water years 1948–2018 generally ranged from about 5,000 acre-feet to more than about 30,000 acre-feet. Stable and radioactive isotopes show that groundwater from the lower aquifer is old and probably was recharged as infiltration from precipitation and streams in the eastern upland areas of the Solomon Hills; however, the infiltration and recharge from these sources probably does not occur under present-day climatic conditions. Anthropogenic recharge, from sources such as return flow from agricultural irrigation, municipal water systems, and wastewater effluent, was estimated to range from about 600 acre-feet in 1948 to about 6,600 acre-feet in 2018. The average annual amount of groundwater removed from the SACVW by pumping during 1948–2018 was estimated to be about 17,200 acre-feet per year, increasing from about 3,000 acre-feet in 1948 to about 32,600 acre-feet in 2018. Estimates of annual pumpage generally exceeded estimates of annual recharge beginning in the mid-1970s and continuing through 2018. The predominant direction of groundwater flow under historical and present-day conditions was from the eastern uplands in the Solomon Hills to the west along San Antonio Creek to the discharge area in Barka Slough, and from the northern uplands in the Casmalia Hills south to San Antonio Creek.
Pumpage since the early 1900s and the subsequent groundwater-level declines have substantially reduced the amount of natural groundwater discharge at Barka Slough. Estimates of base flow to San Antonio Creek at the western, downstream extent of the SACVW have varied over time in response to changes in groundwater pumpage and climate; however, there was an overall decline in base flow during water years 1956–2018, decreasing from an average of about 1,700 acre-feet per year during 1956–69, to about 300 acre-feet per year during 2016–18. The long-term extraction of groundwater correlates with a decrease in groundwater levels by more than about 150 feet since the early 1940s in the eastern part of the basin near Los Alamos, and as much as about 50 feet in the upland areas and in the western part of the basin. At Barka Slough, groundwater levels have declined below land surface in some places, altering native riparian vegetation in and around the slough.
Surface-water quality in the SACVW varied depending on location and the time of year the samples were collected and on the amount of annual precipitation Most groundwater in the SACVW was calcium-bicarbonate-type water with total dissolved-solids concentrations of about 500–800 milligrams per liter generally representing water naturally recharged as infiltration from precipitation and streams. Total dissolved-solids concentrations in some wells ranged from 800 to 8,000 milligrams per liter, suggesting mixing of naturally recharged infiltrated water with water associated with oil-bearing geologic formations, agricultural products, or the evaporation of shallow groundwater. Concentrations of total dissolved solids and the chemical constituents chloride, nitrate plus nitrite (as nitrogen), calcium, and magnesium at selected wells generally increased during water years 1980–2018; increasing concentrations of these constituents may be associated with the expansion of agriculture in the watershed over time and the corresponding increase in the use of nitrates and calcium- and magnesium-based fertilizers and soil additives in modern agricultural practices.
The predominant direction of groundwater flow during historical and present-day conditions was from the eastern uplands in the Solomon Hills to the west along San Antonio Creek toward Barka Slough, and from the western uplands in the Casmalia Hills south to San Antonio Creek. The age of groundwater in the SACVW was evaluated using radioactive isotopes, and the flow of groundwater within the SACVW was evaluated using radioactive and stable isotopes. Modern groundwater (recharged after 1952) was generally found adjacent to San Antonio Creek and its tributaries in wells with perforated depths that averaged about 270 feet below land surface. Pre-modern groundwater (recharged before 1952) was found in wells that had average perforation depths of about 540 ft below land surface. Pre-modern groundwater identified in wells in the eastern upland area is interpreted to have had long, slow travel times to the western part of the SACVW where it was eventually discharged as base flow at Barka Slough or extracted as groundwater pumpage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225001","collaboration":"Prepared in cooperation with Santa Barbara County Water Agency and Vandenberg Space Force Base","programNote":"Groundwater Availability and Use Assessments","usgsCitation":"Cromwell, G., Sweetkind, D.S., Densmore, J.N., Engott, J.A., Seymour, W.A., Larsen, J.D., Ely, C.P., Stamos, C.L., and Faunt, C.C., 2022, Hydrogeologic characterization of the San Antonio Creek Valley watershed, Santa Barbara County, California: U.S. Geological Survey Scientific Investigations Report 2022–5001, 124 p., https://doi.org/10.3133/sir20225001.","productDescription":"Report: xiv, 124 p.; Data Release","numberOfPages":"124","onlineOnly":"Y","ipdsId":"IP-106483","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395163,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5001/images"},{"id":395162,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5001/sir20225001.xml"},{"id":502288,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112320.htm","linkFileType":{"id":5,"text":"html"}},{"id":395158,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AD7DL8","linkHelpText":"Data release of hydrogeologic data from the San Antonio Creek Valley watershed, Santa Barbara County, California"},{"id":395161,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5001/sir20225001.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":395160,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5001/covrthb.jpg"}],"country":"United States","state":"California","county":"Santa Barbara County","otherGeospatial":"San Antonio Creek Valley watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.50079345703125,\n 34.71113805795655\n ],\n [\n -120.09292602539062,\n 34.71113805795655\n ],\n [\n -120.09292602539062,\n 34.854382885097905\n ],\n [\n -120.50079345703125,\n 34.854382885097905\n ],\n [\n -120.50079345703125,\n 34.71113805795655\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The objectives of the study are to (1) develop an updated assessment of the hydrogeology and geochemistry of the Petaluma valley watershed (PVW) and (2) develop an integrated hydrologic model for the PVW. The purpose of this report is to describe the conceptual model of the hydrologic, hydrogeologic, and water-quality characteristics of the PVW and a numerical groundwater-flow model of PVW.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225009","collaboration":"Prepared in cooperation with the Sonoma County Water Agency and the City of Petaluma","programNote":"Water Availability and Use Science Program","usgsCitation":"Traum, J.A., Teague, N.F., Sweetkind, D.S., and Nishikawa, T., 2022, Hydrologic and geochemical characterization of the Petaluma River watershed, Sonoma County, California: U.S. Geological Survey Scientific Investigations Report 2022–5009, 217 p., https://doi.org/10.3133/sir20225009.","productDescription":"Report: xviii, 217 p.; Executive Summmary: 5 p.; 4 Data Releases","numberOfPages":"217","onlineOnly":"Y","ipdsId":"IP-081057","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":395150,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IRYFMB","linkHelpText":"Selected chemical and physical properties and inorganic constituents and time-series nitrate in samples from selected wells and/or springs, Petaluma Valley watershed, Sonoma County, California, 1959–2015"},{"id":395149,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NL90P8","linkHelpText":"Data release of three-dimensional hydrogeologic framework model of the Petaluma Valley watershed, Sonoma County, California"},{"id":395146,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IQDHIT","linkHelpText":"Petaluma Model GIS Data"},{"id":395147,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P965IDQZ","linkHelpText":"MODFLOW-OWHM used to characterize the flow system of the Petaluma River watershed, Sonoma County, California"},{"id":395154,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5009/images"},{"id":395153,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009.xml"},{"id":395166,"rank":9,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009_execSummary.pdf","text":"Executive Summary","size":"200 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Full Executive Summary from this report"},{"id":395151,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5009/covrthb.png"},{"id":395152,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5009/sir20225009.pdf","text":"Report","size":"130 MB"},{"id":502296,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112319.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Sonoma County","otherGeospatial":"Petaluma River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44674682617188,\n 38.11943249695316\n ],\n [\n -122.61428833007814,\n 38.37503882134334\n ],\n [\n -122.74887084960936,\n 38.361041528596026\n ],\n [\n -122.77359008789062,\n 38.293170153420135\n ],\n [\n -122.728271484375,\n 38.19718009396176\n ],\n [\n -122.48382568359374,\n 38.07187927827001\n ],\n [\n -122.44674682617188,\n 38.11943249695316\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is transforming ecosystems and affecting ecosystem goods and services. Along the Gulf of Mexico and Atlantic coasts of the southeastern United States, the frequency and intensity of extreme freeze events greatly influences whether coastal wetlands are dominated by freeze-sensitive woody plants (mangrove forests) or freeze-tolerant grass-like plants (salt marshes). In response to warming winters, mangroves have been expanding and displacing salt marshes at varying degrees of severity in parts of north Florida, Louisiana, and Texas. As winter warming accelerates, mangrove range expansion is expected to increasingly modify wetland ecosystem structure and function. Because there are differences in the ecological and societal benefits that salt marshes and mangroves provide, coastal environmental managers are challenged to anticipate effects of mangrove expansion on critical wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation. Mangrove range expansion may also affect wetland stability in the face of extreme climatic events and rising sea levels. Here, we review current understanding of the effects of mangrove range expansion and displacement of salt marshes on wetland ecosystem services in the southeastern United States. We also identify critical knowledge gaps and emerging research needs regarding the ecological and societal implications of salt marsh displacement by expanding mangrove forests. One consistent theme throughout our review is that there are ecological trade-offs for consideration by coastal managers. Mangrove expansion and marsh displacement can produce beneficial changes in some ecosystem services, while simultaneously producing detrimental changes in other services. Thus, there can be local-scale differences in perceptions of the impacts of mangrove expansion into salt marshes. For very specific local reasons, some individuals may see mangrove expansion as a positive change to be embraced, while others may see mangrove expansion as a negative change to be constrained.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16111","usgsCitation":"Osland, M., Hughes, A.R., Armitage, A.R., Scyphers, S.B., Cebrian, J., Swinea, S.H., Shepard, C., Allen, M.S., Feher, L., Nelson, J., O’Brien, C.L., Sanspree, C.R., Smee, D.L., Snyder, C.M., Stetter, A.P., Stevens, P.W., Swanson, K., Williams, L.H., Brush, J.M., Marchionno, J., and Bardou, R., 2022, The impacts of mangrove range expansion on wetland ecosystem services in the southeastern United States: Current understanding, knowledge gaps, and emerging research needs: Global Change Biology, v. 28, no. 10, p. 3163-3187, https://doi.org/10.1111/gcb.16111.","productDescription":"25 p.","startPage":"3163","endPage":"3187","ipdsId":"IP-132601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467202,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/43126","text":"External Repository"},{"id":395545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.12890625,\n 17.14079039331665\n ],\n [\n -79.1015625,\n 17.14079039331665\n ],\n [\n -79.1015625,\n 33.284619968887675\n ],\n [\n -102.12890625,\n 33.284619968887675\n ],\n [\n -102.12890625,\n 17.14079039331665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":219805,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hughes, A. Randall","contributorId":177827,"corporation":false,"usgs":false,"family":"Hughes","given":"A.","email":"","middleInitial":"Randall","affiliations":[],"preferred":false,"id":833325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Armitage, Anna R.","contributorId":218913,"corporation":false,"usgs":false,"family":"Armitage","given":"Anna","email":"","middleInitial":"R.","affiliations":[{"id":39935,"text":"Texas A&M Galveston, Galveston, TX USA","active":true,"usgs":false}],"preferred":false,"id":833326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scyphers, Steven B.","contributorId":274810,"corporation":false,"usgs":false,"family":"Scyphers","given":"Steven","middleInitial":"B.","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cebrian, Just","contributorId":218914,"corporation":false,"usgs":false,"family":"Cebrian","given":"Just","email":"","affiliations":[{"id":39936,"text":"Dauphin Island Sea Lab, Dauphin Island, AL USA","active":true,"usgs":false}],"preferred":false,"id":833328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Swinea, Savannah H.","contributorId":274811,"corporation":false,"usgs":false,"family":"Swinea","given":"Savannah","email":"","middleInitial":"H.","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shepard, Christine C.","contributorId":274812,"corporation":false,"usgs":false,"family":"Shepard","given":"Christine C.","affiliations":[{"id":56655,"text":"The Nature Conservancy, Gulf of Mexico Program, Key West, FL USA","active":true,"usgs":false}],"preferred":false,"id":833330,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allen, Michael S.","contributorId":274813,"corporation":false,"usgs":false,"family":"Allen","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":56658,"text":"University of Florida, Cedar Key, FL USA","active":true,"usgs":false}],"preferred":false,"id":833331,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":221894,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833332,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nelson, James A.","contributorId":274814,"corporation":false,"usgs":false,"family":"Nelson","given":"James A.","affiliations":[{"id":56659,"text":"University of Louisiana at Lafayette, Lafayette, LA USA","active":true,"usgs":false}],"preferred":false,"id":833333,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"O’Brien, Cherie L.","contributorId":274815,"corporation":false,"usgs":false,"family":"O’Brien","given":"Cherie","email":"","middleInitial":"L.","affiliations":[{"id":56660,"text":"Texas Parks and Wildlife Department, Dickinson, TX USA, 9U.S. Fish and Wildlife Service, Austwell, TX USA","active":true,"usgs":false}],"preferred":false,"id":833334,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sanspree, Colt R.","contributorId":274816,"corporation":false,"usgs":false,"family":"Sanspree","given":"Colt","email":"","middleInitial":"R.","affiliations":[{"id":56661,"text":"U.S. Fish and Wildlife Service, Austwell, TX USA","active":true,"usgs":false}],"preferred":false,"id":833335,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smee, Delbert L.","contributorId":274817,"corporation":false,"usgs":false,"family":"Smee","given":"Delbert","email":"","middleInitial":"L.","affiliations":[{"id":39936,"text":"Dauphin Island Sea Lab, Dauphin Island, AL USA","active":true,"usgs":false}],"preferred":false,"id":833336,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Snyder, Caitlin M.","contributorId":218921,"corporation":false,"usgs":false,"family":"Snyder","given":"Caitlin","email":"","middleInitial":"M.","affiliations":[{"id":39940,"text":"Apalachicola National Estuarine Research Reserve, Eastpoint, FL USA","active":true,"usgs":false}],"preferred":false,"id":833337,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Stetter, Andrew P.","contributorId":274818,"corporation":false,"usgs":false,"family":"Stetter","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":56661,"text":"U.S. Fish and Wildlife Service, Austwell, TX USA","active":true,"usgs":false}],"preferred":false,"id":833338,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Stevens, Philip W.","contributorId":274819,"corporation":false,"usgs":false,"family":"Stevens","given":"Philip","email":"","middleInitial":"W.","affiliations":[{"id":56662,"text":"Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, St. Petersburg, FL USA","active":true,"usgs":false}],"preferred":false,"id":833339,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Swanson, Kathleen M.","contributorId":195577,"corporation":false,"usgs":false,"family":"Swanson","given":"Kathleen M.","affiliations":[],"preferred":false,"id":833340,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Williams, Lauren H.","contributorId":274820,"corporation":false,"usgs":false,"family":"Williams","given":"Lauren","email":"","middleInitial":"H.","affiliations":[{"id":56663,"text":"The Nature Conservancy, Corpus Christi, TX USA","active":true,"usgs":false}],"preferred":false,"id":833341,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Brush, Janell M.","contributorId":264219,"corporation":false,"usgs":false,"family":"Brush","given":"Janell","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":833342,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Marchionno, Joseph","contributorId":274821,"corporation":false,"usgs":false,"family":"Marchionno","given":"Joseph","email":"","affiliations":[{"id":56664,"text":"Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, Gainesville, FL USA","active":true,"usgs":false}],"preferred":false,"id":833343,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Bardou, Remi","contributorId":274822,"corporation":false,"usgs":false,"family":"Bardou","given":"Remi","affiliations":[{"id":56654,"text":"Northeastern University Marine Science Center, 430 Nahant Rd, Nahant, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":833344,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70227735,"text":"sir20215098 - 2022 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","interactions":[],"lastModifiedDate":"2026-04-02T19:39:16.740242","indexId":"sir20215098","displayToPublicDate":"2022-01-31T10:11:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5098","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 9 bridges at 8 highway crossings of the Missouri River near Kansas City, Missouri, on August 13–14, 2019. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,550 to 1,660 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicated the channel conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except around the nose of one pier that was surrounded by a persistent debris raft. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. The observed scour holes at the surveyed bridges generally were examined with respect to shape and depth.
Comparisons between bathymetric surfaces from previous surveys and this study do not indicate any consistent correlation in channel-bed elevations with streamflow conditions at the times of the surveys. The predominant overall scour observed between the various surveys implies the channel bed in the 2019 surveys might have been rebounding from more substantial scour caused by the high streamflow earlier in March and June 2019, which was the highest streamflow since 1993. Pier size and nose shape had a substantial effect on the size of the scour hole observed at a given pier. Many of the piers at the Kansas City area bridges have wide or blunt noses caused by exposed footings, seal courses, or caissons, which resulted in large, deep scour holes at most piers. Several of the structures had piers that were skewed to primary approach flow; and, at most of the structures, the scour hole was deeper and longer on the side of the pier with impinging flow than the leeward side, with some amount of deposition on the leeward side, as typically has been observed at piers skewed to approach flow.
Limited additional bathymetric data were collected by the U.S. Geological Survey, in cooperation with Clarkson Construction, near the main channel piers of the U.S. Highway 169 (Broadway) and the Interstate 435 (Randolph) bridges on August 17 and October 23, 2020, to determine the channel-bed conditions before and after installation of scour countermeasures near those piers. Survey results from before and after installation of these countermeasures show these features had a substantial effect on mitigating the observed scour at these piers, particularly when compared to piers at other sites without such features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215098","collaboration":"Prepared in cooperation with the Missouri Department of Transportation and Clarkson Construction","usgsCitation":"Huizinga, R.J., 2022, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri River near Kansas City, Missouri, August 2019, August 2020, and October 2020: U.S. Geological Survey Scientific Investigations Report 2021–5098, 112 p., https://doi.org/10.3133/sir20215098.","productDescription":"Report: xii, 112 p.; Data Release; Dataset","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-124626","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":395010,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96TX8AE","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River in Kansas City, Missouri, in August 2019, August 2020, and October 2020"},{"id":395008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5098/coverthb.jpg"},{"id":395013,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5098/images"},{"id":395012,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5098/sir20215098.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5098 XML"},{"id":395011,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":395009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5098/sir20215098.pdf","text":"Report","size":"38.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5098"},{"id":502114,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112326.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","city":"Kansas City","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68086242675781,\n 39.102357437817595\n ],\n [\n -94.48722839355467,\n 39.102357437817595\n ],\n [\n -94.48722839355467,\n 39.193948213963665\n ],\n [\n -94.68086242675781,\n 39.193948213963665\n ],\n [\n -94.68086242675781,\n 39.102357437817595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation (Z*MHW), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z*MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z*MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z*MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z*MHW and Z*MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z*MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z*MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-021-01027-9","usgsCitation":"Holmquist, J.R., and Windham-Myers, L., 2022, A conterminous USA-scale map of relative tidal marsh elevation: Estuaries and Coasts, v. 45, p. 1596-1614, https://doi.org/10.1007/s12237-021-01027-9.","productDescription":"19 p.","startPage":"1596","endPage":"1614","ipdsId":"IP-120531","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-021-01027-9","text":"Publisher Index Page"},{"id":395143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-01-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Holmquist, James R.","contributorId":173462,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":832239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":832240,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256719,"text":"70256719 - 2022 - Tracking spatial regimes in animal communities: Implications for resilience-based management","interactions":[],"lastModifiedDate":"2024-09-03T16:17:05.511321","indexId":"70256719","displayToPublicDate":"2022-01-29T11:08:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Tracking spatial regimes in animal communities: Implications for resilience-based management","docAbstract":"Spatial regimes (the spatial extents of ecological states) exhibit strong spatiotemporal order as they expand or contract in response to retreating or encroaching adjacent spatial regimes (e.g., woody plant invasion of grasslands) and human management (e.g., fire treatments). New methods enable tracking spatial regime boundaries via vegetation landcover data, and this approach is being used for strategic management across biomes. A clear advancement would be incorporating animal community data to track spatial regime boundaries alongside vegetation data. In a 41,170-hectare grassland experiencing woody plant encroachment, we test the utility of using animal community data to track spatial regimes via two hypotheses. (H1) Spatial regime boundaries identified via independent vegetation and animal datasets will exhibit spatial synchrony; specifically, grassland:woodland bird community boundaries will synchronize with grass:woody vegetation boundaries. (H2) Negative feedbacks will stabilize spatial regimes identified via animal data; specifically, frequent fire treatments will stabilize grassland bird community boundaries. We used 26 years of bird community and vegetation data alongside 32 years of fire history data. We identified spatial regime boundaries with bird community data via a wombling approach. We identified spatial regime boundaries with vegetation data by calculating spatial covariance between remotely-sensed grass and woody plant cover per pixel. For fire history data, we calculated the cumulative number of fires per pixel. Setting bird boundary strength (wombling R2 values) as the response variable, we tested our hypotheses with a hierarchical generalized additive model (HGAM). Both hypotheses were supported: animal boundaries synchronized with vegetation boundaries in space and time, and grassland bird communities stabilized as fire frequency increased (HGAM explained 38% of deviance). We can now track spatial regimes via animal community data pixel-by-pixel and year-by-year. Alongside vegetation boundary tracking, tracking animal community boundaries can inform the scale of management necessary to maintain animal communities endemic to desirable ecological states. Our approach will be especially useful for conserving animal communities requiring large-scale, unfragmented landscapes—like grasslands and steppes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108567","usgsCitation":"Roberts, C.P., Uden, D.R., Allen, C., Angeler, D., Powell, L., Allred, B.W., Jones, M., Maestas, J.D., and Twidwell, D., 2022, Tracking spatial regimes in animal communities: Implications for resilience-based management: Ecological Indicators, v. 136, 108567, 9 p., https://doi.org/10.1016/j.ecolind.2022.108567.","productDescription":"108567, 9 p.","ipdsId":"IP-133356","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":448996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108567","text":"Publisher Index Page"},{"id":433414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Fort Riley Army Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.96213921773204,\n 39.311060889325915\n ],\n [\n -96.96422797821519,\n 39.2045692635035\n ],\n [\n -96.9059515607264,\n 39.170753927787935\n ],\n [\n -96.87441127742586,\n 39.12982552331178\n ],\n [\n -96.87065150855537,\n 39.06172160474132\n ],\n [\n -96.83117393541791,\n 39.03739558273512\n ],\n [\n -96.75242766519114,\n 39.027994666034715\n ],\n [\n -96.70313291778122,\n 39.08988085180364\n ],\n [\n -96.68057430455956,\n 39.133608110581775\n ],\n [\n -96.68057430455947,\n 39.2068138331922\n ],\n [\n -96.74490812745047,\n 39.242505115215266\n ],\n [\n -96.84683963904419,\n 39.30135970662323\n ],\n [\n -96.96213921773204,\n 39.311060889325915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uden, Daniel R.","contributorId":74258,"corporation":false,"usgs":true,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R.","contributorId":246029,"corporation":false,"usgs":false,"family":"Allen","given":"Craig R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angeler, David G.","contributorId":25027,"corporation":false,"usgs":true,"family":"Angeler","given":"David G.","affiliations":[],"preferred":false,"id":908770,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Powell, Larkin A.","contributorId":15100,"corporation":false,"usgs":true,"family":"Powell","given":"Larkin A.","affiliations":[],"preferred":false,"id":908771,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allred, Brady W","contributorId":216378,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W","affiliations":[{"id":39397,"text":"W.A. Franke College of Forestry and Conservation University of Montana, Missoula","active":true,"usgs":false}],"preferred":false,"id":908772,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Matthew O.","contributorId":341488,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew O.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":908773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maestas, Jeremy D","contributorId":191086,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":908774,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Twidwell, Dirac","contributorId":341491,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908775,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227859,"text":"70227859 - 2022 - Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation","interactions":[],"lastModifiedDate":"2022-02-01T17:43:25.670538","indexId":"70227859","displayToPublicDate":"2022-01-28T11:37:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation","docAbstract":"The lower 10 km of the Buffalo River, a tributary to Lake Erie, was designated as an Area of Concern (AOC) in 1987 through the Great Lakes Water Quality Agreement because sediment contamination and habitat alteration from past industrialization caused several Beneficial Use Impairments (BUIs). Extensive remediation efforts conducted between 2011 and 2015 removed approximately 688,100 cubic meters of contaminated sediment from the Buffalo River AOC, and subsequent chemical analysis of sediments indicated that most remedial goals had been achieved. Benthic macroinvertebrate communities and sediment toxicity were evaluated in the AOC and an upstream reference area in 2017 and 2020 to determine whether remediation has improved benthic conditions sufficiently that the benthos BUI designation can be removed. Community condition was characterized using the New York State multi-metric index of biological integrity and bed sediments were used for 10-day toxicity tests with Chironomus dilutus and Hyalella azteca. Macroinvertebrate communities were classified as moderately to slightly impacted at most AOC sites compared to slightly impacted at most reference sites, but toxicity tests did not identify any evidence of toxicity in sediments from the AOC. A linear mixed effects model indicated that total organic carbon concentration in sediments, distance upstream from the river mouth, and the relative dominance of zebra mussels Dreissena polymorpha were the primary predictors of macroinvertebrate community condition. These findings are consistent with those from other AOCs in New York which indicate that contemporary benthic communities are generally shaped by legacy habitat alterations rather than AOC-specific sediment contamination and toxicity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.11.002","usgsCitation":"George, S.D., Duffy, B.T., Baldigo, B., Skaros, D., and Smith, A., 2022, Condition of macroinvertebrate communities in the Buffalo River Area of Concern following sediment remediation: Journal of Great Lakes Research, v. 48, no. 1, p. 183-194, https://doi.org/10.1016/j.jglr.2021.11.002.","productDescription":"12 p.","startPage":"183","endPage":"194","ipdsId":"IP-129186","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":449003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2021.11.002","text":"Publisher Index Page"},{"id":395221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Buffalo River area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.87908935546874,\n 42.83015652099459\n ],\n [\n -78.74862670898438,\n 42.83015652099459\n ],\n [\n -78.74862670898438,\n 42.895585521720584\n ],\n [\n -78.87908935546874,\n 42.895585521720584\n ],\n [\n -78.87908935546874,\n 42.83015652099459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, Brian T.","contributorId":272971,"corporation":false,"usgs":false,"family":"Duffy","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skaros, Damianos","contributorId":272972,"corporation":false,"usgs":false,"family":"Skaros","given":"Damianos","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Alexander J.","contributorId":140345,"corporation":false,"usgs":false,"family":"Smith","given":"Alexander J.","affiliations":[{"id":13464,"text":"Environmental Analyst, NY State Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":832430,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238963,"text":"70238963 - 2022 - Forecasting species distributions: Correlation does not equal causation","interactions":[],"lastModifiedDate":"2022-12-19T14:24:41.325188","indexId":"70238963","displayToPublicDate":"2022-01-28T08:19:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting species distributions: Correlation does not equal causation","docAbstract":"Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects.
New Hampshire and Vermont, USA.
Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emission scenario (RCP8.5) of projected snow and forest biomass change.
Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high-elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx.
Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13480","usgsCitation":"Sirén, A., Sutherland, C., Karmalkar, A.V., Duveneck, M., and Morelli, T.L., 2022, Forecasting species distributions: Correlation does not equal causation: Diversity and Distributions, v. 28, no. 4, p. 756-769, https://doi.org/10.1111/ddi.13480.","productDescription":"14 p.","startPage":"756","endPage":"769","ipdsId":"IP-134781","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":449011,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13480","text":"Publisher Index Page"},{"id":410703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-71.502487,45.013367],[-71.499945,45.026323],[-71.491148,45.041774],[-71.49315,45.045772],[-71.500874,45.04511],[-71.505222,45.048791],[-71.497738,45.054751],[-71.498399,45.069629],[-71.467447,45.086851],[-71.464837,45.093023],[-71.449257,45.104522],[-71.445613,45.113367],[-71.428828,45.123881],[-71.427208,45.127364],[-71.437216,45.142333],[-71.427688,45.152251],[-71.415468,45.183309],[-71.39781,45.203553],[-71.403267,45.215348],[-71.415553,45.218001],[-71.443882,45.235462],[-71.442298,45.238547],[-71.420335,45.232719],[-71.402638,45.242589],[-71.394422,45.241216],[-71.385629,45.233214],[-71.37763,45.244203],[-71.357253,45.253336],[-71.362245,45.264738],[-71.360664,45.269835],[-71.336392,45.273066],[-71.331733,45.279969],[-71.309008,45.287238],[-71.301107,45.296563],[-71.284396,45.302434],[-71.28074,45.295188],[-71.266557,45.294589],[-71.263042,45.277401],[-71.236271,45.261126],[-71.231122,45.249712],[-71.221994,45.253543],[-71.2118,45.250457],[-71.198276,45.254257],[-71.180905,45.239858],[-71.162845,45.250332],[-71.148165,45.242412],[-71.13943,45.242958],[-71.133994,45.244167],[-71.116332,45.272322],[-71.107339,45.278612],[-71.105691,45.282498],[-71.110743,45.284576],[-71.105151,45.294635],[-71.097772,45.301906],[-71.085564,45.305476],[-71.076914,45.246912],[-71.037518,44.755607],[-71.012749,44.340784],[-70.989067,43.79244],[-70.972716,43.570255],[-70.957234,43.561358],[-70.951876,43.552238],[-70.955252,43.540887],[-70.962153,43.541036],[-70.963531,43.536756],[-70.954066,43.52261],[-70.954755,43.509802],[-70.969572,43.486201],[-70.967404,43.482635],[-70.974245,43.47742],[-70.961428,43.469696],[-70.9669,43.450458],[-70.961046,43.440475],[-70.968782,43.434891],[-70.971039,43.425606],[-70.986812,43.414264],[-70.982876,43.394808],[-70.98739,43.393457],[-70.987649,43.389521],[-70.967229,43.343777],[-70.953034,43.333257],[-70.932735,43.33676],[-70.931641,43.331019],[-70.912004,43.319821],[-70.909805,43.306682],[-70.900386,43.301358],[-70.907405,43.293582],[-70.886504,43.282783],[-70.882804,43.273183],[-70.86323,43.265109],[-70.859607,43.257342],[-70.843302,43.254321],[-70.839213,43.251224],[-70.838678,43.242931],[-70.817865,43.237911],[-70.815453,43.229023],[-70.80964,43.225407],[-70.816033,43.21568],[-70.820702,43.191663],[-70.828301,43.186685],[-70.823501,43.174585],[-70.828301,43.168985],[-70.8338,43.146886],[-70.8281,43.129086],[-70.779098,43.095887],[-70.767998,43.093588],[-70.757597,43.080888],[-70.737897,43.073488],[-70.708896,43.074989],[-70.704696,43.070989],[-70.703799,43.059574],[-70.71363,43.056006],[-70.71355,43.042077],[-70.718936,43.03235],[-70.730426,43.025392],[-70.734363,43.013307],[-70.743793,43.008027],[-70.749194,42.992677],[-70.761474,42.986681],[-70.7718,42.968064],[-70.771729,42.961321],[-70.793996,42.93989],[-70.798153,42.920926],[-70.810069,42.909549],[-70.817296,42.87229],[-70.848625,42.860939],[-70.886136,42.88261],[-70.914886,42.886564],[-70.930799,42.884589],[-70.9665,42.868989],[-71.031201,42.859089],[-71.047501,42.844089],[-71.064201,42.806289],[-71.132503,42.821389],[-71.165603,42.808689],[-71.186104,42.790689],[-71.181803,42.73759],[-71.223904,42.746689],[-71.245504,42.742589],[-71.267905,42.72589],[-71.294205,42.69699],[-73.276421,42.746019],[-73.290944,42.80192],[-73.28375,42.813864],[-73.287063,42.82014],[-73.285388,42.834093],[-73.278673,42.83341],[-73.256493,43.259249],[-73.247698,43.523173],[-73.241589,43.534973],[-73.250132,43.543429],[-73.24842,43.552577],[-73.258631,43.564949],[-73.284912,43.579272],[-73.295344,43.580235],[-73.292113,43.584509],[-73.296924,43.587323],[-73.292232,43.60255],[-73.304125,43.627057],[-73.310606,43.624114],[-73.317566,43.627355],[-73.342181,43.62607],[-73.347621,43.622509],[-73.371889,43.624489],[-73.36987,43.619711],[-73.376036,43.612596],[-73.373443,43.603292],[-73.383446,43.596778],[-73.383369,43.57677],[-73.395767,43.568087],[-73.430947,43.587036],[-73.421616,43.603023],[-73.423815,43.610989],[-73.417827,43.620586],[-73.42791,43.634428],[-73.426463,43.642598],[-73.415513,43.65245],[-73.402078,43.693106],[-73.370612,43.725329],[-73.370287,43.742269],[-73.350707,43.770463],[-73.357547,43.785933],[-73.376361,43.798766],[-73.380804,43.810951],[-73.392492,43.820779],[-73.388389,43.832404],[-73.372247,43.845337],[-73.382046,43.855008],[-73.37415,43.874163],[-73.383491,43.890951],[-73.395878,43.903044],[-73.408589,43.932933],[-73.406823,43.967317],[-73.412613,43.97998],[-73.405977,44.011485],[-73.407739,44.021312],[-73.410776,44.026944],[-73.43774,44.045006],[-73.429239,44.079414],[-73.411316,44.112686],[-73.41578,44.131523],[-73.403268,44.144295],[-73.395532,44.166122],[-73.395862,44.175785],[-73.390383,44.179486],[-73.390805,44.189072],[-73.362013,44.208545],[-73.349889,44.230356],[-73.342312,44.234531],[-73.34323,44.238049],[-73.324681,44.243614],[-73.313422,44.264199],[-73.311025,44.27424],[-73.322267,44.301523],[-73.323997,44.333842],[-73.334939,44.364441],[-73.333575,44.372288],[-73.315016,44.388513],[-73.296031,44.428339],[-73.293613,44.440559],[-73.300114,44.454711],[-73.298939,44.471304],[-73.306707,44.500334],[-73.319265,44.51196],[-73.322026,44.525289],[-73.330588,44.531034],[-73.342932,44.551907],[-73.355186,44.556918],[-73.374389,44.575455],[-73.381848,44.589316],[-73.376849,44.599598],[-73.382932,44.612184],[-73.38982,44.61721],[-73.387169,44.635542],[-73.379748,44.64036],[-73.383157,44.645764],[-73.374134,44.66234],[-73.37059,44.662518],[-73.371843,44.676956],[-73.367209,44.678513],[-73.369685,44.683758],[-73.361308,44.694523],[-73.36556,44.700297],[-73.365561,44.741786],[-73.354361,44.755296],[-73.347072,44.772988],[-73.335713,44.782086],[-73.333154,44.788759],[-73.335443,44.804602],[-73.369647,44.829136],[-73.378717,44.837358],[-73.381359,44.845021],[-73.379822,44.857037],[-73.369103,44.86668],[-73.35808,44.901325],[-73.338979,44.917681],[-73.338243,44.96475],[-73.350218,44.976222],[-73.354633,44.987352],[-73.343124,45.01084],[-72.845633,45.016659],[-72.67477,45.015459],[-72.58988,45.013237],[-72.555912,45.008304],[-72.310073,45.003822],[-71.502487,45.013367]]]},\"properties\":{\"name\":\"New Hampshire\",\"nation\":\"USA \"}}]}","volume":"28","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Sirén, Alexej","contributorId":300102,"corporation":false,"usgs":false,"family":"Sirén","given":"Alexej","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":859421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutherland, Christopher","contributorId":214549,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":859422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karmalkar, Ambarish V.","contributorId":243435,"corporation":false,"usgs":false,"family":"Karmalkar","given":"Ambarish","email":"","middleInitial":"V.","affiliations":[{"id":48712,"text":"Dept of Geosciences, UMass Amherst, Amherst MA","active":true,"usgs":false}],"preferred":false,"id":859423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duveneck, Matthew","contributorId":300105,"corporation":false,"usgs":false,"family":"Duveneck","given":"Matthew","affiliations":[{"id":65017,"text":"New England Conservatory","active":true,"usgs":false}],"preferred":false,"id":859424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859425,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231484,"text":"70231484 - 2022 - Modeling subsurface performance of a geothermal reservoir using machine learning","interactions":[],"lastModifiedDate":"2022-05-11T11:44:14.982345","indexId":"70231484","displayToPublicDate":"2022-01-28T06:42:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10757,"text":"Energies","active":true,"publicationSubtype":{"id":10}},"title":"Modeling subsurface performance of a geothermal reservoir using machine learning","docAbstract":"Physical processes driving barrier island change during storms are important to understand to mitigate coastal hazards and to evaluate conceptual models for barrier evolution. Spatial variations in barrier island topography, landcover characteristics, and nearshore and back-barrier hydrodynamics can yield complex morphological change that requires models of increasing resolution and physical complexity to predict. Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, we investigated two barrier island breaches that occurred on Fire Island, NY during Hurricane Sandy (2012) and at Matanzas, FL during Hurricane Matthew (2016). The model employed a recently implemented infragravity (IG) wave driver to represent the important effects of IG waves on nearshore water levels and sediment transport. The model simulated breaching and other changes with good skill at both locations, resolving differences in the processes and evolution. The breach simulated at Fire Island was 250 m west of the observed breach, whereas the breach simulated at Matanzas was within 100 m of the observed breach. Implementation of the vegetation module of COAWST to allow three-dimensional drag over dune vegetation at Fire Island improved model skill by decreasing flows across the back-barrier, as opposed to varying bottom roughness that did not positively alter model response. Analysis of breach processes at Matanzas indicated that both far-field and local hydrodynamics influenced breach creation and evolution, including remotely generated waves and surge, but also surge propagation through back-barrier waterways. This work underscores the importance of resolving the complexity of nearshore and back-barrier systems when predicting barrier island change during extreme events.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JF006307","usgsCitation":"Hegermiller, C., Warner, J.C., Olabarrieta, M., Sherwood, C.R., and Kalra, T., 2022, Modeling of barrier breaching during Hurricanes Sandy and Matthew: JGR-Earth Surface, v. 127, no. 3, e2021JF006307, 20 p., https://doi.org/10.1029/2021JF006307.","productDescription":"e2021JF006307, 20 p.","ipdsId":"IP-130367","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006307","text":"External Repository"},{"id":413242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, New York","city":"Matanzas","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.23320426968054,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 41.082997080822736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.335863306947,\n 26.295094198443238\n ],\n [\n -78.26241414026661,\n 27.205116096340547\n ],\n [\n -80.7016669317273,\n 31.630079958177035\n ],\n [\n -82.69413025464559,\n 30.949177652812494\n ],\n [\n -80.2897567468504,\n 26.26830908028633\n ],\n [\n -80.335863306947,\n 26.295094198443238\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Hegermiller, Christie 0000-0002-6383-7508","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":241895,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":true,"id":864757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":864759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":864762,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256727,"text":"70256727 - 2022 - Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","interactions":[],"lastModifiedDate":"2024-09-03T16:44:50.779458","indexId":"70256727","displayToPublicDate":"2022-01-26T11:39:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","docAbstract":"Ecosystem structure of wetlands in managed floodplains depends on hydrological processes controlled by geomorphology and water management. Overlapping effects of direct modifications and geomorphic adjustments to management can combine to trigger changes to floodplain ecosystem structure. We examined the case of woody vegetation encroaching into the depressional Catahoula Lake, Louisiana, in the context of regional hydrologic and geomorphic modification in the floodplain of the Mississippi River. Historical aerial photographs indicated woody encroachment into Catahoula Lake for at least 80 years, and the rate of expansion has increased in recent decades. Historical stage analysis revealed that the downstream Red–Atchafalaya–Mississippi River system controls the lower limit of the lake water level when the large rivers are high, but channel enlargement and other hydrological changes there have reduced the frequency of backwater flooding by 42% since 1880. In addition, operation of the water control structure on the lake has altered its hydrological regime to be more regular among years. Historic stage analysis revealed current lake levels are lower in the high-water spring, less variable in the dry period, and lack the extreme high-water events of 100+ years ago, all of which facilitate the expansion of woody vegetation.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5328","usgsCitation":"Keim, R., Dugue, L., Latuso, K., Joshi, S., King, S.L., and Willis, F., 2022, Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA: Earth Surface Processes and Landforms, v. 47, no. 6, p. 1466-1479, https://doi.org/10.1002/esp.5328.","productDescription":"14 p.","startPage":"1466","endPage":"1479","ipdsId":"IP-130218","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449025,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5328","text":"Publisher Index Page"},{"id":433416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Catahoula Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.20285714912983,\n 31.44173705449458\n ],\n [\n -92.12418940417722,\n 31.44557223555043\n ],\n [\n -92.0297881102343,\n 31.532780176376406\n ],\n [\n -92.03877870965712,\n 31.576832357365504\n ],\n [\n -92.0803602319895,\n 31.574917477714266\n ],\n [\n -92.13655147838381,\n 31.541400717897048\n ],\n [\n -92.17925682564407,\n 31.505955624529022\n ],\n [\n -92.21521922333639,\n 31.45899413301514\n ],\n [\n -92.20285714912983,\n 31.44173705449458\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Keim, R.F.","contributorId":264646,"corporation":false,"usgs":false,"family":"Keim","given":"R.F.","affiliations":[{"id":54524,"text":"Lousiiana State University","active":true,"usgs":false}],"preferred":false,"id":908787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugue, L.","contributorId":341705,"corporation":false,"usgs":false,"family":"Dugue","given":"L.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latuso, K.D.","contributorId":341706,"corporation":false,"usgs":false,"family":"Latuso","given":"K.D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, S.","contributorId":341707,"corporation":false,"usgs":false,"family":"Joshi","given":"S.","email":"","affiliations":[{"id":13314,"text":"Columbia River Inter-Tribal Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willis, F.L.","contributorId":341708,"corporation":false,"usgs":false,"family":"Willis","given":"F.L.","email":"","affiliations":[{"id":81776,"text":"Willis Engineering and Scientific","active":true,"usgs":false}],"preferred":false,"id":908792,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227681,"text":"70227681 - 2022 - The potential of wave energy conversion to mitigate coastal erosion from hurricanes","interactions":[],"lastModifiedDate":"2022-01-26T17:12:54.664026","indexId":"70227681","displayToPublicDate":"2022-01-26T11:03:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The potential of wave energy conversion to mitigate coastal erosion from hurricanes","docAbstract":"Wave energy conversion technologies have recently attracted more attention as part of global efforts to replace fossil fuels with renewable energy resources. While ocean waves can provide renewable energy, they can also be destructive to coastal areas that are often densely populated and vulnerable to coastal erosion. There have been a variety of efforts to mitigate the impacts of wave- and storm-induced erosion; however, they are either temporary solutions or approaches that are not able to adapt to a changing climate. This study explores a green and sustainable approach to mitigating coastal erosion from hurricanes through wave energy conversion. A barrier island, Dauphin Island, off the coast of Alabama, is used as a test case. The potential use of wave energy converter farms to mitigate erosion due to hurricane storm surges while simultaneously generating renewable energy is explored through simulations that are forced with storm data using the XBeach model. It is shown that wave farms can impact coastal morphodynamics and have the potential to reduce dune and beach erosion, predominantly in the western portion of the island. The capacity of wave farms to influence coastal morphodynamics varies with the storm intensity.
","language":"English","publisher":"MDPI AG","doi":"10.3390/jmse10020143","usgsCitation":"Ozkan, C., Mayo, T., and Passeri, D., 2022, The potential of wave energy conversion to mitigate coastal erosion from hurricanes: Journal of Marine Science and Engineering, v. 10, no. 2, p. 1-26, https://doi.org/10.3390/jmse10020143.","productDescription":"143, 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-126398","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449028,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020143","text":"Publisher Index Page"},{"id":394882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35411071777344,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.22317846163011\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Morales, Rafael","contributorId":272228,"corporation":false,"usgs":false,"family":"Morales","given":"Rafael","email":"","affiliations":[],"preferred":false,"id":831787,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ozkan, Cigdem","contributorId":272200,"corporation":false,"usgs":false,"family":"Ozkan","given":"Cigdem","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":831708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayo, Talea","contributorId":272201,"corporation":false,"usgs":false,"family":"Mayo","given":"Talea","email":"","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":831709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina L. 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":831710,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227685,"text":"70227685 - 2022 - Testing the potential of streamflow data to predict spring migration of an ungulate herds","interactions":[],"lastModifiedDate":"2022-01-26T16:07:24.226926","indexId":"70227685","displayToPublicDate":"2022-01-26T09:51:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Testing the potential of streamflow data to predict spring migration of an ungulate herds","docAbstract":"In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the ‘spring pulse.’ Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0262078","usgsCitation":"Alexander, J.S., Murr, M.L., and Eddy-Miller, C.A., 2022, Testing the potential of streamflow data to predict spring migration of an ungulate herds: PLoS ONE, v. 17, no. 1, p. 1-18, https://doi.org/10.1371/journal.pone.0262078.","productDescription":"e0262078, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-125176","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":449034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0262078","text":"Publisher Index Page"},{"id":394871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 40.45321727150385\n ],\n [\n -108.00933837890625,\n 40.70562793820589\n ],\n [\n -107.46826171874999,\n 40.84913799774759\n ],\n [\n -107.0892333984375,\n 40.86991083161536\n ],\n [\n -107.05078125,\n 41.00477542222947\n ],\n [\n -107.490234375,\n 41.539421883822854\n ],\n [\n -108.446044921875,\n 41.54764462357737\n ],\n [\n -108.8031005859375,\n 41.20552261955812\n ],\n [\n -108.45703125,\n 40.45321727150385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Grignolio, Stefano","contributorId":272227,"corporation":false,"usgs":false,"family":"Grignolio","given":"Stefano","email":"","affiliations":[{"id":35987,"text":"Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy","active":true,"usgs":false}],"preferred":false,"id":831783,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X jalexand@usgs.gov","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":261330,"corporation":false,"usgs":true,"family":"Alexander","given":"Jason","email":"jalexand@usgs.gov","middleInitial":"S.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":831740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":195780,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":831741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227686,"text":"70227686 - 2022 - Oxygen isotopes of land snail shells in high latitude regions","interactions":[],"lastModifiedDate":"2022-01-26T15:51:22.676505","indexId":"70227686","displayToPublicDate":"2022-01-26T09:41:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotopes of land snail shells in high latitude regions","docAbstract":"The present study investigates the environmental significance of the oxygen isotopic composition of several modern land snail species collected along two north-to-south transects in Alaska and Scandinavia at latitudes between 60 and 70 °N. We tested the hypothesis that land snail shell δ18O values primarily track precipitation δ18O. The results show that shell δ18O values from Scandinavia were ∼5.1‰ enriched in 18O with respect to snails from Alaska, equivalent to differences in precipitation δ18O values between the two regions. Within the Alaskan transect, shell δ18O values increased with observed increasing air temperature and precipitation δ18O, whereas shell δ18O values from Scandinavia did not correlate to instrumental climate data because of a reduced climatic gradient across the locations sampled. In addition, shell δ18O values differed significantly among sympatric species, with larger species consistently exhibiting higher δ18O values, which implies that species-level isotopic variations should be considered at the local and microhabitat scale. However, when snail shell δ18O values from this study are combined with previously published data from North America and Europe, we see evidence that shell δ18O values track precipitation δ18O across latitudes, even when different species are combined because climate gradients are greater than variations among taxa.