Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state-of-the-art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year−1 for the period 2000–2019 (0.31 and 0.44 PgC year−1 in each decade); split into 0.18 ± 0.12 PgC year−1 in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year−1 in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year−1). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental-scale land C cycle along with the spatial distribution of local sink-source dynamics.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG006904","usgsCitation":"Murray-Tortarolo, G., Poulter, B., Vargas, R., Hayes, D., Michalak, A., Williams , C., Windham-Myers, L., Wang, J., Wickland, K., Butman, D., Tian, H., Sitch, S., Friedlingstein, P., O’Sullivan, M., Briggs, P., Arora, V., Lombardozzi, D., Jain, A., Yuan, W., Seferian, R., Nabel, J., Wiltshire, A., Arneth, A., Lienerte, S., Zaehle, S., Bastrikov, V., Goll, D., Vuichard, N., Walker, A.P., Kato, E., Xu, Y., Zhang, Z., Chaterjee, A., and Kurz, W., 2022, A process-model perspective on recent changes in the carbon cycle of North America: Journal of Geophysical Research Biogeosciences, v. 127, no. 9, e2022JG006904, 19 p., https://doi.org/10.1029/2022JG006904.","productDescription":"e2022JG006904, 19 p.","ipdsId":"IP-144503","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":446574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg006904","text":"Publisher Index Page"},{"id":406674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -42.1875,\n 60.413852350464914\n ],\n [\n -20.7421875,\n 70.61261423801925\n ],\n [\n -12.3046875,\n 82.02137801950887\n ],\n [\n -28.828124999999996,\n 83.63810565804015\n ],\n [\n -83.671875,\n 83.31873282163234\n ],\n [\n -130.78125,\n 75.58493740869223\n ],\n [\n -131.1328125,\n 70.72897946208789\n ],\n [\n -164.1796875,\n 71.74643171904148\n ],\n [\n -172.96875,\n 62.91523303947614\n ],\n [\n -159.9609375,\n 52.696361078274485\n ],\n [\n -142.03125,\n 58.07787626787517\n ],\n [\n -128.671875,\n 47.989921667414194\n ],\n [\n -120.9375,\n 24.84656534821976\n ],\n [\n -83.3203125,\n 4.915832801313164\n ],\n [\n -76.640625,\n 11.178401873711785\n ],\n [\n -67.8515625,\n 17.97873309555617\n ],\n [\n -42.1875,\n 60.413852350464914\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Murray-Tortarolo, Guillermo","contributorId":296446,"corporation":false,"usgs":false,"family":"Murray-Tortarolo","given":"Guillermo","email":"","affiliations":[{"id":64038,"text":"Instituto de Investigaciones en Ecosistemas y Sustentabilidad. Universidad Nacional Autónoma de México, Morelia, México","active":true,"usgs":false}],"preferred":false,"id":851583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poulter, Benjamin 0000-0002-9493-8600","orcid":"https://orcid.org/0000-0002-9493-8600","contributorId":200477,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":851584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vargas, Rodrigo 0000-0001-6829-5333","orcid":"https://orcid.org/0000-0001-6829-5333","contributorId":224770,"corporation":false,"usgs":false,"family":"Vargas","given":"Rodrigo","email":"","affiliations":[{"id":39556,"text":"U. Delaware","active":true,"usgs":false}],"preferred":false,"id":851585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Daniel B.","contributorId":248252,"corporation":false,"usgs":false,"family":"Hayes","given":"Daniel B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":851586,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Michalak, Anna M.","contributorId":296447,"corporation":false,"usgs":false,"family":"Michalak","given":"Anna M.","affiliations":[{"id":64040,"text":"Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States","active":true,"usgs":false}],"preferred":false,"id":851587,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams , Christopher J. ","contributorId":223677,"corporation":false,"usgs":false,"family":"Williams ","given":"Christopher J. ","affiliations":[],"preferred":false,"id":851588,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":851589,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Jonathan","contributorId":296448,"corporation":false,"usgs":false,"family":"Wang","given":"Jonathan","affiliations":[{"id":64041,"text":"UC Irvine, California","active":true,"usgs":false}],"preferred":false,"id":851590,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":206313,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":851591,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":851592,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tian, Hanqin","contributorId":296449,"corporation":false,"usgs":false,"family":"Tian","given":"Hanqin","affiliations":[{"id":64042,"text":"Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, United States","active":true,"usgs":false}],"preferred":false,"id":851593,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sitch, Stephen 0000-0003-1821-8561","orcid":"https://orcid.org/0000-0003-1821-8561","contributorId":245070,"corporation":false,"usgs":false,"family":"Sitch","given":"Stephen","email":"","affiliations":[{"id":49080,"text":"College of Life and Environmental Sciences, University of Exeter, Exeter, Laver Building EX4 4QF UK","active":true,"usgs":false}],"preferred":false,"id":851594,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Friedlingstein, Pierre","contributorId":220198,"corporation":false,"usgs":false,"family":"Friedlingstein","given":"Pierre","email":"","affiliations":[{"id":40149,"text":"5Grantham Institute for Climate Change & Division of Ecology & Evolution, Imperial College, UK","active":true,"usgs":false}],"preferred":false,"id":851595,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"O’Sullivan, Michael","contributorId":296450,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Michael","email":"","affiliations":[{"id":64043,"text":"College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":851596,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Briggs, Peter","contributorId":296451,"corporation":false,"usgs":false,"family":"Briggs","given":"Peter","email":"","affiliations":[{"id":64044,"text":"Commonwealth Scientific and Industrial Research Organization (CSIRO) Oceans and Atmosphere, Aspendale, Australia","active":true,"usgs":false}],"preferred":false,"id":851597,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Arora, Vivek","contributorId":296452,"corporation":false,"usgs":false,"family":"Arora","given":"Vivek","email":"","affiliations":[{"id":64046,"text":"Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, University of Victoria, Victoria, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":851598,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Lombardozzi, Danielle","contributorId":296453,"corporation":false,"usgs":false,"family":"Lombardozzi","given":"Danielle","email":"","affiliations":[{"id":64047,"text":"Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, United States","active":true,"usgs":false}],"preferred":false,"id":851599,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Jain, Atul","contributorId":296454,"corporation":false,"usgs":false,"family":"Jain","given":"Atul","email":"","affiliations":[{"id":64048,"text":"Department of Atmospheric Sciences, University of Illinois, Urbana, IL, United States","active":true,"usgs":false}],"preferred":false,"id":851600,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Yuan, Wenping","contributorId":274900,"corporation":false,"usgs":false,"family":"Yuan","given":"Wenping","affiliations":[{"id":56683,"text":"Sun Yat-sen University, China","active":true,"usgs":false}],"preferred":false,"id":851601,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Seferian, Roland","contributorId":296455,"corporation":false,"usgs":false,"family":"Seferian","given":"Roland","email":"","affiliations":[{"id":64049,"text":"CNRM, Université de Toulouse, Meteo-France, CNRS, Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":851602,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Nabel, Julia","contributorId":296456,"corporation":false,"usgs":false,"family":"Nabel","given":"Julia","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":851603,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wiltshire, Andrea","contributorId":296457,"corporation":false,"usgs":false,"family":"Wiltshire","given":"Andrea","email":"","affiliations":[{"id":64050,"text":"Met Office Hadley Centre, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":851604,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Arneth, Almuth","contributorId":296458,"corporation":false,"usgs":false,"family":"Arneth","given":"Almuth","email":"","affiliations":[{"id":64051,"text":"Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch–Partenkirchen, Germany","active":true,"usgs":false}],"preferred":false,"id":851605,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Lienerte, Sebastian","contributorId":296459,"corporation":false,"usgs":false,"family":"Lienerte","given":"Sebastian","email":"","affiliations":[{"id":64052,"text":"Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":851606,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Zaehle, Sonke","contributorId":210474,"corporation":false,"usgs":false,"family":"Zaehle","given":"Sonke","affiliations":[],"preferred":false,"id":851607,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Bastrikov, Vladislov","contributorId":296460,"corporation":false,"usgs":false,"family":"Bastrikov","given":"Vladislov","email":"","affiliations":[{"id":64053,"text":"Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, CEA-CNRS-UVSQ, CE Orme des Merisiers, Gif-sur-Yvette CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":851608,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Goll, Daniel","contributorId":296461,"corporation":false,"usgs":false,"family":"Goll","given":"Daniel","email":"","affiliations":[{"id":64054,"text":"Université Paris Saclay, CEA-CNRS-UVSQ, LSCE/IPSL, Gif sur Yvette, France","active":true,"usgs":false}],"preferred":false,"id":851609,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Vuichard, Nicholas","contributorId":296462,"corporation":false,"usgs":false,"family":"Vuichard","given":"Nicholas","email":"","affiliations":[{"id":64053,"text":"Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, CEA-CNRS-UVSQ, CE Orme des Merisiers, Gif-sur-Yvette CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":851610,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Walker, Anthony P. 0000-0003-0557-5594","orcid":"https://orcid.org/0000-0003-0557-5594","contributorId":167843,"corporation":false,"usgs":false,"family":"Walker","given":"Anthony","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":851611,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Kato, Etushi","contributorId":296463,"corporation":false,"usgs":false,"family":"Kato","given":"Etushi","email":"","affiliations":[{"id":64055,"text":"Institute of Applied Energy, Tokyo, Japan","active":true,"usgs":false}],"preferred":false,"id":851612,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Xu, Yue","contributorId":220833,"corporation":false,"usgs":false,"family":"Xu","given":"Yue","email":"","affiliations":[],"preferred":false,"id":851704,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Zhang, Zhen","contributorId":94945,"corporation":false,"usgs":true,"family":"Zhang","given":"Zhen","affiliations":[],"preferred":false,"id":851705,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Chaterjee, Abishek","contributorId":296525,"corporation":false,"usgs":false,"family":"Chaterjee","given":"Abishek","email":"","affiliations":[],"preferred":false,"id":851706,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Kurz, Werner A.","contributorId":50644,"corporation":false,"usgs":true,"family":"Kurz","given":"Werner A.","affiliations":[],"preferred":false,"id":851707,"contributorType":{"id":1,"text":"Authors"},"rank":34}]}} ,{"id":70236341,"text":"70236341 - 2022 - Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","interactions":[],"lastModifiedDate":"2022-09-02T14:17:32.925032","indexId":"70236341","displayToPublicDate":"2022-09-01T09:13:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12565,"text":"GeoHazards","active":true,"publicationSubtype":{"id":10}},"title":"Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","docAbstract":"Although accurate root cohesion model estimates are essential to quantify the effect of vegetation roots on shallow slope stability, few means exist to independently validate such model outputs. One validation approach for cohesion estimates is back-calculation of apparent root cohesion at a landslide site with well-documented failure conditions. The catchment named CB1, near Coos Bay, Oregon, USA, which experienced a shallow landslide in 1996, is a prime locality for cohesion model validation, as an abundance of data and observations from the site generated broad insights related to hillslope hydrology and slope stability. However, previously published root cohesion values at CB1 used the Wu and Waldron model (WWM), which assumes simultaneous root failure and therefore likely overestimates root cohesion. Reassessing published cohesion estimates from this site is warranted, as more recently developed models include the fiber bundle model (FBM), which simulates progressive failure with load redistribution, and the root bundle model-Weibull (RBMw), which accounts for differential strain loading. We applied the WWM, FBM, and RBMw at CB1 using post-failure root data from five vegetation species. At CB1, the FBM and RBMw predict values that are less than 30% of the WWM-estimated values. All three models show that root cohesion has substantial spatial heterogeneity. Most parts of the landslide scarp have little root cohesion, with areas of high cohesion concentrated near plant roots. These findings underscore the importance of using physically realistic models and considering lateral and vertical spatial heterogeneity of root cohesion in shallow landslide initiation and provide a necessary step towards independently assessing root cohesion model validity.
","language":"English","publisher":"MDPI","doi":"10.3390/geohazards3030022","usgsCitation":"Cronkite-Ratcliff, C., Schmidt, K.M., and Wirion, C., 2022, Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range: GeoHazards, v. 3, no. 3, p. 428-451, https://doi.org/10.3390/geohazards3030022.","productDescription":"24 p.","startPage":"428","endPage":"451","ipdsId":"IP-133079","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geohazards3030022","text":"Publisher Index Page"},{"id":406136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","city":"Coos Bay","otherGeospatial":"Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.56298828125001,\n 43.14909399920127\n ],\n [\n -123.50830078125,\n 43.14909399920127\n ],\n [\n -123.50830078125,\n 43.75522505306928\n ],\n [\n -124.56298828125001,\n 43.75522505306928\n ],\n [\n -124.56298828125001,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wirion, Charlotte 0000-0003-0721-3036","orcid":"https://orcid.org/0000-0003-0721-3036","contributorId":296101,"corporation":false,"usgs":false,"family":"Wirion","given":"Charlotte","email":"","affiliations":[{"id":63984,"text":"ETH Zurich, Switzerland (now at WEO, Luxembourg)","active":true,"usgs":false}],"preferred":false,"id":850666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256644,"text":"70256644 - 2022 - Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2024-08-29T14:09:34.922004","indexId":"70256644","displayToPublicDate":"2022-09-01T09:02:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico","docAbstract":"The northern Gulf of Mexico (nGoM) is a globally important region for oil extraction and supports a diverse assemblage of marine birds. Due to their frequent contact with surface waters, diverse foraging strategies, and the ease with which oil adheres to feathers, seabirds are particularly susceptible to hydrocarbon contamination. Given the chronic and acute exposure of seabirds to oiling and a lack of studies that focus on the exposure of seabirds to oiling in sub-tropical and tropical regions, a greater understanding of the vulnerability of seabirds to oil in the nGoM appears warranted. We present an oil vulnerability index for seabirds in the nGoM tailored to the current state of knowledge using new, spatiotemporally expensive vessel-based seabird observations. We use information on the exposure and sensitivity of seabirds to oil to rank seabird vulnerability. Exposure variables characterized the potential to encounter oil and gas (O&G). Sensitivity variables characterized the potential impact of seabirds interacting with O&G and are related to life history and productivity. We also incorporated uncertainty in each variable, identifying data gaps. We found that the percent of seabirds’ habitat defined as highly suitable within 10 km of an O&G platform ranged from 0%-65% among 24 species. Though O&G platforms only overlap with 15% of highly suitable seabird habitat, overlap occurs in areas of moderate to high vulnerability of seabirds, particularly along the shelf-slope. Productivity-associated sensitivity variables were primarily responsible for creating the gradient in vulnerability scores and had greater uncertainty than exposure variables. Highly vulnerable species (e.g., Northern gannet (Morus bassanus)) tended to have high exposure to the water surface via foraging behaviors (e.g., plunge-diving), older age at first breeding, and an extended incubating and fledging period compared to less vulnerable species (e.g., Pomarine jaeger (Stercorarius pomarinus)). Uncertainty related to productivity could be reduced through at-colony monitoring. Strategic seabird satellite tagging could help target monitoring efforts to colonies known to use the nGoM, and continued vessel-based observations could improve habitat characterization. As offshore energy development in the nGoM continues, managers and researchers could use these vulnerability ranks to identify information gaps to prioritize research and focal species.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.880750","usgsCitation":"Michael, P.E., Hixson, K.M., Haney, J., Satge, Y., Gleason, J., and Jodice, P.G., 2022, Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico: Frontiers in Marine Science, v. 9, 880750, 20 p., https://doi.org/10.3389/fmars.2022.880750.","productDescription":"880750, 20 p.","ipdsId":"IP-136731","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446580,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.880750","text":"Publisher Index Page"},{"id":433301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.65064732144825,\n 24.16865530700734\n ],\n [\n -80.74028091647656,\n 24.49674089923498\n ],\n [\n -80.6110475132637,\n 25.375716312633244\n ],\n [\n -82.48124501487644,\n 27.87595132514076\n ],\n [\n -82.50401354053463,\n 28.975470074471573\n ],\n [\n -84.01377542803263,\n 30.278116834370664\n ],\n [\n -85.21105352771927,\n 29.813919882762193\n ],\n [\n -86.41030861654544,\n 30.53771106924384\n ],\n [\n -88.05949534476994,\n 30.614388650912403\n ],\n [\n -89.250977846713,\n 30.128789114669402\n ],\n [\n -88.84045762268858,\n 28.94436305824601\n ],\n [\n -90.45644421555457,\n 29.213975074748845\n ],\n [\n -91.6391457751865,\n 29.567867845383958\n ],\n [\n -92.61153919455367,\n 29.52398569897167\n ],\n [\n -94.15861129214014,\n 29.615060750195013\n ],\n [\n -95.85111266718562,\n 28.497269906948034\n ],\n [\n -97.05244440317941,\n 27.930797727982196\n ],\n [\n -97.4997935110664,\n 26.841188159759383\n ],\n [\n -97.23172322974906,\n 26.093166420472286\n ],\n [\n -86.07549373074224,\n 26.17610757333493\n ],\n [\n -85.31650372657558,\n 24.614391708025977\n ],\n [\n -83.65064732144825,\n 24.16865530700734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Pamela E.","contributorId":341457,"corporation":false,"usgs":false,"family":"Michael","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hixson, K. M.","contributorId":341458,"corporation":false,"usgs":false,"family":"Hixson","given":"K.","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, J.C.","contributorId":288019,"corporation":false,"usgs":false,"family":"Haney","given":"J.C.","email":"","affiliations":[{"id":61685,"text":"Terra Mar Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":908455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Satge, Y.G.","contributorId":279816,"corporation":false,"usgs":false,"family":"Satge","given":"Y.G.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gleason, J.S.","contributorId":288017,"corporation":false,"usgs":false,"family":"Gleason","given":"J.S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249920,"text":"70249920 - 2022 - Puerto Rico’s state of the climate 2014-2021","interactions":[],"lastModifiedDate":"2023-11-07T14:32:49.34824","indexId":"70249920","displayToPublicDate":"2022-09-01T08:28:32","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Puerto Rico’s State of the Climate 2014-2021","title":"Puerto Rico’s state of the climate 2014-2021","docAbstract":"The climate of Puerto Rico is influenced by the changing global climate. The following chapters present the current knowledge of the geophysical and chemical drivers and signals of global climate change as they affect the climate of Puerto Rico and influence the climate-dependent services, risks, and vulnerabilities that govern human well-being. These include sustainable economic development, delivery of ecosystem services, the conservation of natural and cultural resources, resiliency in built and natural systems, and food security. The chapters draw on global expertise of land, atmosphere, and ocean geophysical interactions associated with increasing greenhouse gases that drive global warming and on local scientific expertise, data, observations, and modeled projections. They present the global warming scenario (section 1), the contribution of Puerto Rico to global climate change as GHG emissions and aerosols (section 2), the context of natural climate variability (section 3), observed and projected trends in temperature (section 4), rainfall (section 5), sea level rise (section 6), ocean acidification and sea surface warming (section 7), and the expected implications of warming climate on tropical cyclones affecting Puerto Rico (section 8).","language":"English","publisher":"Puerto Rico Climate Change Council","usgsCitation":"Gould, W.A., Dias, E., Terando, A., Jury, M., Bowden, J., Chardon, P., Melendez Oyola, M., and Morell, J., 2022, Puerto Rico’s state of the climate 2014-2021, 260 p.","productDescription":"260 p.","ipdsId":"IP-133876","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":422426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":422406,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.drna.pr.gov/wp-content/uploads/2022/10/PR_StateOfTheClimate_2014-2021_PRCCC-09-2022.pdf"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.3277,18.295843],[-65.337451,18.308308],[-65.327318,18.323666],[-65.342068,18.34529],[-65.335701,18.349535],[-65.329334,18.341955],[-65.321754,18.338316],[-65.309833,18.337973],[-65.304409,18.332054],[-65.298328,18.330529],[-65.255933,18.342117],[-65.221568,18.320959],[-65.222853,18.310464],[-65.249857,18.296691],[-65.260282,18.290823],[-65.283269,18.280214],[-65.3277,18.295843]]],[[[-67.89174,18.11397],[-67.887099,18.112574],[-67.87643,18.114157],[-67.869804,18.118851],[-67.861548,18.122144],[-67.848245,18.10832],[-67.843202,18.094858],[-67.843615,18.085099],[-67.845293,18.081938],[-67.853098,18.078195],[-67.865598,18.06544],[-67.871462,18.0578],[-67.895921,18.052342],[-67.904431,18.05913],[-67.918778,18.063116],[-67.927841,18.068572],[-67.940799,18.079716],[-67.934479,18.111306],[-67.932185,18.113221],[-67.91088,18.119668],[-67.89174,18.11397]]],[[[-65.308717,18.145172],[-65.302295,18.141089],[-65.294896,18.14283],[-65.287962,18.148097],[-65.275165,18.13443],[-65.276214,18.131936],[-65.283248,18.132999],[-65.296036,18.12799],[-65.322794,18.126589],[-65.327184,18.124106],[-65.338506,18.112439],[-65.342037,18.11138],[-65.350493,18.111914],[-65.364733,18.120377],[-65.397837,18.110873],[-65.399791,18.108832],[-65.411767,18.106211],[-65.423765,18.097764],[-65.426311,18.093749],[-65.45138,18.086096],[-65.45681,18.087778],[-65.465849,18.087715],[-65.468768,18.092643],[-65.47979,18.096352],[-65.507265,18.091646],[-65.524209,18.081977],[-65.542087,18.081177],[-65.558646,18.08566],[-65.569305,18.091616],[-65.570628,18.097325],[-65.57686,18.103224],[-65.575579,18.115669],[-65.546199,18.119329],[-65.511712,18.13284],[-65.489829,18.135912],[-65.46791,18.143767],[-65.437058,18.15766],[-65.399517,18.161935],[-65.371373,18.157517],[-65.334289,18.147761],[-65.313476,18.144296],[-65.308717,18.145172]]],[[[-66.438813,18.485713],[-66.420921,18.488639],[-66.410344,18.489886],[-66.394287,18.489748],[-66.377286,18.488044],[-66.37282,18.487726],[-66.349647,18.486335],[-66.337728,18.48562],[-66.315477,18.474724],[-66.31503,18.47468],[-66.291225,18.472347],[-66.283675,18.472203],[-66.276599,18.478129],[-66.269799,18.480281],[-66.258015,18.476906],[-66.251547,18.472464],[-66.241797,18.46874],[-66.220148,18.466],[-66.199032,18.466163],[-66.192664,18.466212],[-66.183886,18.460506],[-66.179218,18.455305],[-66.172315,18.451462],[-66.159796,18.451706],[-66.153037,18.454457],[-66.14395,18.459761],[-66.139572,18.462317],[-66.139451,18.462387],[-66.139443,18.462315],[-66.138532,18.453305],[-66.133085,18.445881],[-66.127938,18.444632],[-66.125198,18.451209],[-66.124284,18.456324],[-66.123188,18.45943],[-66.123343,18.460363],[-66.125015,18.470435],[-66.118338,18.469581],[-66.092098,18.466535],[-66.083254,18.462022],[-66.073987,18.4581],[-66.043272,18.453655],[-66.03944,18.454441],[-66.036559,18.450216],[-66.036491,18.450117],[-66.023221,18.443875],[-66.006523,18.444347],[-65.99718,18.449895],[-65.992935,18.457489],[-65.992793,18.458102],[-65.992349,18.460024],[-65.99079,18.460419],[-65.958492,18.451354],[-65.92567,18.444881],[-65.916843,18.444619],[-65.907756,18.446893],[-65.904988,18.450926],[-65.878683,18.438322],[-65.838825,18.431865],[-65.831476,18.426849],[-65.828457,18.423543],[-65.816691,18.410663],[-65.794556,18.402845],[-65.787666,18.402544],[-65.774937,18.413951],[-65.77053,18.41294],[-65.769749,18.409473],[-65.771695,18.406277],[-65.750455,18.385208],[-65.750179,18.38505],[-65.742154,18.380459],[-65.733567,18.382211],[-65.699069,18.368156],[-65.669636,18.362102],[-65.668845,18.361939],[-65.634431,18.369835],[-65.627246,18.376436],[-65.626527,18.381728],[-65.624975,18.386553],[-65.622761,18.387771],[-65.618229,18.386496],[-65.614891,18.382473],[-65.619068,18.367755],[-65.628198,18.353711],[-65.63419,18.338965],[-65.628047,18.328252],[-65.626456,18.298982],[-65.634389,18.292349],[-65.635826,18.288271],[-65.634893,18.283923],[-65.630833,18.264989],[-65.623111,18.248012],[-65.597618,18.234289],[-65.589947,18.228225],[-65.593795,18.224059],[-65.615981,18.227389],[-65.626731,18.235484],[-65.638181,18.229121],[-65.637565,18.224444],[-65.628414,18.205149],[-65.635281,18.199975],[-65.639688,18.205656],[-65.662185,18.207018],[-65.664127,18.207136],[-65.690749,18.19499],[-65.694515,18.187011],[-65.691021,18.178998],[-65.695856,18.179324],[-65.710895,18.186963],[-65.712533,18.189146],[-65.717999,18.190176],[-65.728471,18.185588],[-65.734664,18.180368],[-65.738834,18.174066],[-65.739125,18.173453],[-65.743632,18.163957],[-65.758728,18.156601],[-65.766919,18.148424],[-65.777584,18.129239],[-65.796711,18.083746],[-65.796289,18.079835],[-65.794686,18.078607],[-65.795028,18.073561],[-65.796711,18.069842],[-65.801831,18.058527],[-65.809174,18.056818],[-65.817107,18.063378],[-65.825848,18.057482],[-65.83109,18.050664],[-65.834274,18.038988],[-65.832429,18.014916],[-65.839591,18.015077],[-65.850913,18.011954],[-65.870335,18.006597],[-65.875122,18.002826],[-65.884937,17.988521],[-65.896102,17.99026],[-65.905319,17.983974],[-65.910537,17.981855],[-65.924738,17.976087],[-65.976611,17.967669],[-65.98455,17.969411],[-65.985358,17.971854],[-65.995185,17.978989],[-66.007731,17.980541],[-66.017308,17.979583],[-66.019539,17.978354],[-66.024,17.975896],[-66.046585,17.954853],[-66.049033,17.954561],[-66.058217,17.959238],[-66.068678,17.966335],[-66.069979,17.966357],[-66.08141,17.966552],[-66.116194,17.949141],[-66.127009,17.946953],[-66.140661,17.94102],[-66.147912,17.933963],[-66.155387,17.929406],[-66.159742,17.928613],[-66.161232,17.931747],[-66.175626,17.933565],[-66.186914,17.935363],[-66.189726,17.933936],[-66.200174,17.929515],[-66.206961,17.932268],[-66.213374,17.944614],[-66.202655,17.944753],[-66.185554,17.940997],[-66.179548,17.943727],[-66.174839,17.948214],[-66.176814,17.950438],[-66.206207,17.96305],[-66.206807,17.963307],[-66.215355,17.959376],[-66.218081,17.95729],[-66.231519,17.943912],[-66.229181,17.934651],[-66.232013,17.931154],[-66.252737,17.934574],[-66.260684,17.936083],[-66.270905,17.947098],[-66.275651,17.94826],[-66.290782,17.946491],[-66.297679,17.959148],[-66.31695,17.976683],[-66.323659,17.978536],[-66.338152,17.976492],[-66.33839,17.976458],[-66.362511,17.968231],[-66.365098,17.964832],[-66.368777,17.957717],[-66.371591,17.951469],[-66.385059,17.939004],[-66.391227,17.945819],[-66.398945,17.950925],[-66.412131,17.957286],[-66.445481,17.979379],[-66.450368,17.983226],[-66.454888,17.986784],[-66.461342,17.990273],[-66.491396,17.990262],[-66.510143,17.985618],[-66.540537,17.975476],[-66.583233,17.961229],[-66.589658,17.969386],[-66.594392,17.970682],[-66.605035,17.969015],[-66.623788,17.98105],[-66.631944,17.982746],[-66.645651,17.98026],[-66.657797,17.974605],[-66.664391,17.968259],[-66.672819,17.966451],[-66.699115,17.977568],[-66.709856,17.982109],[-66.713394,17.987763],[-66.716957,17.990344],[-66.731118,17.991658],[-66.746248,17.990349],[-66.750427,17.995443],[-66.753964,17.99959],[-66.755341,18.001203],[-66.764491,18.006317],[-66.770307,18.005955],[-66.799656,17.99245],[-66.806866,17.983786],[-66.807924,17.979606],[-66.806903,17.976046],[-66.805683,17.975052],[-66.795106,17.977438],[-66.789302,17.980793],[-66.784953,17.978326],[-66.787245,17.972914],[-66.80827,17.965635],[-66.8224,17.954499],[-66.838584,17.949931],[-66.852288,17.955004],[-66.856474,17.956553],[-66.859471,17.954316],[-66.862545,17.952022],[-66.871697,17.952707],[-66.88344,17.952526],[-66.899639,17.948298],[-66.904585,17.950527],[-66.906532,17.955356],[-66.906276,17.963368],[-66.924529,17.972808],[-66.928651,17.970204],[-66.930414,17.963127],[-66.916127,17.959102],[-66.909483,17.952559],[-66.909359,17.94988],[-66.912522,17.947446],[-66.930313,17.943389],[-66.932636,17.939998],[-66.931581,17.9369],[-66.919298,17.932062],[-66.923826,17.926923],[-66.927261,17.926875],[-66.959998,17.940216],[-66.980516,17.951648],[-66.98105,17.952505],[-66.982669,17.9551],[-66.982206,17.961192],[-66.987287,17.970663],[-66.996738,17.972899],[-67.003972,17.970799],[-67.014744,17.968468],[-67.024522,17.970722],[-67.062478,17.973819],[-67.076534,17.967759],[-67.089827,17.951418],[-67.101468,17.946621],[-67.109985,17.945806],[-67.109986,17.945806],[-67.128251,17.948153],[-67.133733,17.951919],[-67.167031,17.963073],[-67.178566,17.964792],[-67.183508,17.962706],[-67.188717,17.950989],[-67.187474,17.946252],[-67.183694,17.937982],[-67.183457,17.931135],[-67.194785,17.932826],[-67.196924,17.935651],[-67.197273,17.937461],[-67.197517,17.941514],[-67.197668,17.943549],[-67.198988,17.94782],[-67.200973,17.949896],[-67.210034,17.953595],[-67.212101,17.956027],[-67.21433,17.962436],[-67.215271,17.983464],[-67.211973,17.992993],[-67.207694,17.998019],[-67.177893,18.008882],[-67.174299,18.011149],[-67.172397,18.014906],[-67.172138,18.021422],[-67.173761,18.024548],[-67.193269,18.03185],[-67.209887,18.035439],[-67.196694,18.066491],[-67.190656,18.064269],[-67.184589,18.06775],[-67.183938,18.069914],[-67.186465,18.074195],[-67.192999,18.076877],[-67.198212,18.076828],[-67.199314,18.091135],[-67.19529,18.096149],[-67.183921,18.103683],[-67.182182,18.108507],[-67.176554,18.151046],[-67.178618,18.159318],[-67.180822,18.168055],[-67.180701,18.168182],[-67.155185,18.195001],[-67.152665,18.203493],[-67.158001,18.216719],[-67.173,18.230666],[-67.175429,18.248008],[-67.187843,18.266671],[-67.187873,18.266874],[-67.189971,18.281015],[-67.196056,18.290443],[-67.209963,18.294974],[-67.225403,18.296648],[-67.226081,18.296722],[-67.235137,18.299935],[-67.267484,18.353149],[-67.27135,18.362329],[-67.268259,18.366989],[-67.260671,18.370197],[-67.23909,18.375318],[-67.226744,18.378247],[-67.216998,18.382078],[-67.202167,18.389908],[-67.160144,18.415587],[-67.159608,18.415915],[-67.156599,18.418983],[-67.155245,18.424401],[-67.156619,18.439562],[-67.161746,18.453462],[-67.169011,18.466352],[-67.169016,18.478488],[-67.164144,18.487396],[-67.14283,18.505485],[-67.138249,18.507776],[-67.125655,18.511706],[-67.103468,18.514523],[-67.093752,18.515757],[-67.07929,18.513256],[-67.020276,18.510603],[-66.988958,18.497724],[-66.95954,18.489878],[-66.957733,18.489129],[-66.957517,18.489171],[-66.944636,18.491693],[-66.906872,18.483556],[-66.90143,18.484552],[-66.867386,18.490785],[-66.849673,18.490745],[-66.83694,18.487659],[-66.836635,18.487701],[-66.79932,18.492775],[-66.780311,18.491411],[-66.764893,18.484097],[-66.749301,18.476701],[-66.742067,18.474681],[-66.733986,18.473457],[-66.710743,18.472611],[-66.683719,18.481367],[-66.679876,18.484944],[-66.664364,18.487809],[-66.645839,18.488777],[-66.624618,18.494199],[-66.586778,18.484948],[-66.584074,18.484287],[-66.565241,18.485523],[-66.562916,18.48845],[-66.563485,18.490512],[-66.558503,18.489987],[-66.53484,18.481253],[-66.533487,18.481663],[-66.529476,18.482877],[-66.511609,18.476848],[-66.470292,18.46907],[-66.456486,18.46892],[-66.449184,18.470991],[-66.441852,18.479751],[-66.439961,18.485525],[-66.438813,18.485713]]]]},\"properties\":{\"name\":\"Puerto Rico\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Diaz, Ernesto L.","contributorId":212197,"corporation":false,"usgs":false,"family":"Diaz","given":"Ernesto","email":"","middleInitial":"L.","affiliations":[{"id":38453,"text":"Department of Natural and Environmental Resources, Coastal Zone Management Program","active":true,"usgs":false}],"preferred":false,"id":887762,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Gonzalez, Melissa","contributorId":331459,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Melissa","email":"","affiliations":[],"preferred":false,"id":887763,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Terando, Adam 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":197511,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":887764,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Gould, William A. 0000-0002-3720-9735","orcid":"https://orcid.org/0000-0002-3720-9735","contributorId":212196,"corporation":false,"usgs":false,"family":"Gould","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":38452,"text":"USDA Forest Service International Institute of Tropical Forestry","active":true,"usgs":false}],"preferred":false,"id":887713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dias, Ernesto","contributorId":331443,"corporation":false,"usgs":false,"family":"Dias","given":"Ernesto","email":"","affiliations":[{"id":79212,"text":"Tetra Tech Inc.","active":true,"usgs":false}],"preferred":false,"id":887711,"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":887712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jury, Mark","contributorId":331445,"corporation":false,"usgs":false,"family":"Jury","given":"Mark","affiliations":[{"id":79213,"text":"University of Puerto Rico - Mayagüez","active":true,"usgs":false}],"preferred":false,"id":887716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowden, Jared","contributorId":197528,"corporation":false,"usgs":false,"family":"Bowden","given":"Jared","affiliations":[],"preferred":false,"id":887714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chardon, Patricia","contributorId":331444,"corporation":false,"usgs":false,"family":"Chardon","given":"Patricia","email":"","affiliations":[{"id":79213,"text":"University of Puerto Rico - Mayagüez","active":true,"usgs":false}],"preferred":false,"id":887715,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Melendez Oyola, Melissa","contributorId":212213,"corporation":false,"usgs":false,"family":"Melendez Oyola","given":"Melissa","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":887717,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morell, Julio","contributorId":331446,"corporation":false,"usgs":false,"family":"Morell","given":"Julio","email":"","affiliations":[{"id":79213,"text":"University of Puerto Rico - Mayagüez","active":true,"usgs":false}],"preferred":false,"id":887718,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236514,"text":"70236514 - 2022 - Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","interactions":[],"lastModifiedDate":"2022-09-09T13:32:07.905743","indexId":"70236514","displayToPublicDate":"2022-09-01T08:22:11","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","docAbstract":"The Eocene Green River Formation contains the largest oil shale deposits in the world and is a welldocumented example of a lacustrine depositional system. In addition, mineral resources associated with oil shale in the Piceance Basin nahcolite [NaHCO3] and dawsonite [NaAl(CO3)(OH)2)] are of current and potential economic value, respectively. Detailed geochemical analysis across the basin can aid in the understanding of the depositional environment, sedimentary processes, and water-chemistry evolution in this system. Quantitative geochemical data for Green River oil shale from the Piceance Basin of Colorado were collected by inductively coupled plasma optical emission spectroscopy and mass spectrometry as part of this study. The basin margin is represented by samples from exposures at Douglas Pass (Garfield County) and the basin center area is characterized by core samples from two drilled wells: the Shell 23X-2 and John Savage 24-1 (Rio Blanco County). Major elements and groups of elements are used as proxies for clastic influx (Si, Al, K, Ti), carbonate deposition (Ca, Mg), salinity (Na), paleo-productivity (P), and redox state (Fe, S), respectively. Minor and trace elements reinforce observations based on major elements, including Rb, Zr, Nb for clastic influx and Mn, Sr for carbonate. Trace elements are used to characterize redox conditions (As, Mo, U, V, Co, Ni, Cu, Zn) and salinity (Rb/K, B/Ga). Chemical distinctions between the basin margin and the basin center, in terms of these components and total organic carbon concentrations, support the model of a permanently stratified lake through most of the depositional interval. A primary purpose of the study was to conduct more extensive sampling to confirm conclusions of a previous reconnaissance study. Geochemical data from this study indicates elevated Na around the basin margin occurring earlier than in the deeper basin. Early in the history of Lake Uinta, the salinity may have been elevated first in the shallower marginal waters, due to increased evaporation, which then led to elevated salinity in the basin center through transport of saline density currents. Other indicators of salinity (Rb/K, B/Ga) do not track Na content in intervals where clay minerals are absent due to diagenetic alteration under hypersaline conditions but may be used to indicate the salinities at which authigenic Na-bearing minerals begin to form. Most Na-rich samples show high proportions of clastic constituents (Si, Al, K, Ti) compared to conventional carbonate constituents (Ca, Mg). Redox-sensitive period IV transition metal elements (V, Co, Ni, Cu, Zn) show only local occurrence of significant enrichment relative to average shale abundances. Analysis of Fe/Al ratios for this dataset suggests that the depletion of these elements may be related to source rocks depleted in mafic constituents, with apparent redox-related enrichments subdued by this effect. The basin margin samples reflect generally oxic bottom waters, with some intervals deposited under more reducing, possibly dysoxic to anoxic conditions. The basin center results indicate more reducing conditions, with Mo and U enrichment factors suggesting operation of a particulate shuttle mechanism that scavenged Mo on Fe/Mn-oxyhydroxides that redissolved at depth, with Mo precipitating along with sulfides and/or organic matter at or near the sediment/water interface.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Utah Geological Association","doi":"10.31711/ugap.v50i.114","usgsCitation":"Boak, J., Wu, T., and Birdwell, J.E., 2022, Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 266-297, https://doi.org/10.31711/ugap.v50i.114.","productDescription":"32 p.","startPage":"266","endPage":"297","ipdsId":"IP-127516","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446590,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.114","text":"Publisher Index Page"},{"id":435705,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q5VOQB","text":"USGS data release","linkHelpText":"Geochemical data for the Green River Formation in the Piceance Basin, Colorado: Major and trace element concentrations and total organic carbon content"},{"id":406448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Green River Formation, Piceance Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.1439208984375,\n 39.48284540453334\n ],\n [\n -107.8692626953125,\n 39.64799732373418\n ],\n [\n -107.91320800781249,\n 40.027614437486655\n ],\n [\n -108.2647705078125,\n 40.17467622056341\n ],\n [\n -108.6492919921875,\n 40.069664523297774\n ],\n [\n -108.7811279296875,\n 39.88023492849342\n ],\n [\n -108.5394287109375,\n 39.6437675734185\n ],\n [\n -108.1439208984375,\n 39.48284540453334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"editors":[{"text":"Hurst, C. J.","contributorId":206942,"corporation":false,"usgs":false,"family":"Hurst","given":"C.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":851360,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Boak, Jeremy 0000-0003-0251-434X","orcid":"https://orcid.org/0000-0003-0251-434X","contributorId":296328,"corporation":false,"usgs":false,"family":"Boak","given":"Jeremy","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Tengfei 0000-0003-2804-5537","orcid":"https://orcid.org/0000-0003-2804-5537","contributorId":296330,"corporation":false,"usgs":false,"family":"Wu","given":"Tengfei","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":851290,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238985,"text":"70238985 - 2022 - Potential cheatgrass abundance within lightly invaded areas of the Great Basin","interactions":[],"lastModifiedDate":"2022-12-20T14:13:06.35989","indexId":"70238985","displayToPublicDate":"2022-09-01T08:06:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Potential cheatgrass abundance within lightly invaded areas of the Great Basin","docAbstract":"Context
Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high.
Objectives
We aimed to support management by developing predictive maps of potential cover for cheatgrass (Bromus tectorum), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas.
Methods
We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data.
Results
Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts.
Conclusions
We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.
Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.
Fish behavior after passage or transfer around dams is a critical component in determining whether the goals of these efforts are achieved, but these behaviors are often poorly understood. An elevator was constructed in the lowermost hydroelectric dam on the Menominee River, Wisconsin–Michigan; it is the first elevator specifically designed to capture Lake Sturgeon Acipenser fulvescens for upstream transfer above two dams, providing access to high-quality spawning and early life habitat. Our objectives were to determine whether (1) Lake Sturgeon transferred upstream remained upstream for at least one spawning opportunity; (2) spawning opportunity, time to reach the next dam upstream, and residency in different segments of the river were related to sex, capture method (elevator versus electrofishing), and season of transfer; and (3) the probability of fish transitioning back downstream of the two dams varied among months. We evaluated posttransfer behaviors of 139 Lake Sturgeon that were captured in the elevator or by electrofishing, implanted with acoustic transmitters, transferred upstream (in spring or fall) from fall 2014 to spring 2017, and monitored until fall 2018 using 20–23 stationary acoustic receivers deployed throughout the river. Most Lake Sturgeon (91%) remained upstream for at least one spawning opportunity. The probability of remaining for one spawning opportunity was not related to sex, fish capture method, or season of transfer. Residency times within the two impoundments and time to reach the next dam upstream varied among individual fish. A multistate model indicated that monthly survival after upstream transfer was high and that Lake Sturgeon typically remained above both dams in late fall to early spring, with most downstream movements occurring in April and May. Our results indicate that Lake Sturgeon transferred upstream have the potential to contribute offspring that may help to bolster the Lake Sturgeon population in Lake Michigan, but additional research may help in determining whether these contributions occur.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10379","usgsCitation":"Isermann, D.A., Raabe, J., Easterly, E., Schulze, J., Porter, N., Dembkowski, D., Donofrio, M., Kramer, D., and Elliott, R., 2022, Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan: Transactions of the American Fisheries Society, v. 151, no. 5, p. 611-629, https://doi.org/10.1002/tafs.10379.","productDescription":"19 p.","startPage":"611","endPage":"629","ipdsId":"IP-137127","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Menominee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.52923848272457,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.083121335445355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raabe, Joshua K.","contributorId":348735,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":348736,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma G.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulze, Joshua C.","contributorId":348738,"corporation":false,"usgs":false,"family":"Schulze","given":"Joshua C.","affiliations":[{"id":83404,"text":"USDA Forest Service Region 1","active":true,"usgs":false}],"preferred":false,"id":923729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Nicholas J.","contributorId":348741,"corporation":false,"usgs":false,"family":"Porter","given":"Nicholas J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dembkowski, Daniel J.","contributorId":348743,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":923731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael C.","contributorId":348744,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923732,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kramer, Darren R.","contributorId":348745,"corporation":false,"usgs":false,"family":"Kramer","given":"Darren R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923733,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elliott, Robert F.","contributorId":348746,"corporation":false,"usgs":false,"family":"Elliott","given":"Robert F.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923734,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237577,"text":"70237577 - 2022 - Causality guided machine learning model on wetland CH4 emissions across global wetlands","interactions":[],"lastModifiedDate":"2022-10-14T13:48:15.777176","indexId":"70237577","displayToPublicDate":"2022-08-31T16:42:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Causality guided machine learning model on wetland CH4 emissions across global wetlands","title":"Causality guided machine learning model on wetland CH4 emissions across global wetlands","docAbstract":"Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2022.109115","usgsCitation":"Yuan, K., Zhu, Q., Li, F., Riley, W.J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K.B., Wu, H., Baldocchi, D., Ma, H., Desai, A.R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., Tuittila, E., Jurasinski, G., Koebsch, F., Campbell, D.I., Schmid, H.P., Lohila, A., Goeckede, M., Nilsson, M.B., Friborg, T., Jansen, J., Zona, D., Euskirchen, E.S., Ward, E., Bohrer, G., Jin, Z., Liu, L., Iwata, H., Goodrich, J.P., and Jackson, R.B., 2022, Causality guided machine learning model on wetland CH4 emissions across global wetlands: Agricultural and Forest Meteorology, v. 324, 109115, 10 p., https://doi.org/10.1016/j.agrformet.2022.109115.","productDescription":"109115, 10 p.","ipdsId":"IP-140199","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2022.109115","text":"Publisher Index Page"},{"id":408305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Kunxiaojia","contributorId":297856,"corporation":false,"usgs":false,"family":"Yuan","given":"Kunxiaojia","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Qing","contributorId":260547,"corporation":false,"usgs":false,"family":"Zhu","given":"Qing","affiliations":[],"preferred":false,"id":854488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Fa","contributorId":297906,"corporation":false,"usgs":false,"family":"Li","given":"Fa","email":"","affiliations":[],"preferred":false,"id":854608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riley, William J. 0000-0002-4615-2304","orcid":"https://orcid.org/0000-0002-4615-2304","contributorId":194645,"corporation":false,"usgs":false,"family":"Riley","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":854490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torn, Margaret","contributorId":240709,"corporation":false,"usgs":false,"family":"Torn","given":"Margaret","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chu, Housen","contributorId":217396,"corporation":false,"usgs":false,"family":"Chu","given":"Housen","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":854492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNicol, Gavin 0000-0002-6655-8045","orcid":"https://orcid.org/0000-0002-6655-8045","contributorId":260536,"corporation":false,"usgs":false,"family":"McNicol","given":"Gavin","email":"","affiliations":[],"preferred":false,"id":854493,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chen, Mingshu","contributorId":220088,"corporation":false,"usgs":false,"family":"Chen","given":"Mingshu","email":"","affiliations":[{"id":37968,"text":"Sun Yat-Sen University","active":true,"usgs":false}],"preferred":false,"id":854494,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Knox, Sara","contributorId":272638,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":854495,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Delwiche, Kyle B.","contributorId":139866,"corporation":false,"usgs":false,"family":"Delwiche","given":"Kyle","email":"","middleInitial":"B.","affiliations":[{"id":13299,"text":"Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA","active":true,"usgs":false}],"preferred":false,"id":854496,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wu, Huayi","contributorId":297858,"corporation":false,"usgs":false,"family":"Wu","given":"Huayi","email":"","affiliations":[{"id":39129,"text":"Wuhan University","active":true,"usgs":false}],"preferred":false,"id":854497,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":854498,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ma, Hongxu","contributorId":297860,"corporation":false,"usgs":false,"family":"Ma","given":"Hongxu","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":854499,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Desai, Ankur R. 0000-0002-5226-6041","orcid":"https://orcid.org/0000-0002-5226-6041","contributorId":20622,"corporation":false,"usgs":false,"family":"Desai","given":"Ankur","email":"","middleInitial":"R.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":854500,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Chen, Jiquan 0000-0003-0761-9458","orcid":"https://orcid.org/0000-0003-0761-9458","contributorId":146126,"corporation":false,"usgs":false,"family":"Chen","given":"Jiquan","email":"","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":854501,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sachs, Torsten 0000-0002-9959-4771","orcid":"https://orcid.org/0000-0002-9959-4771","contributorId":208637,"corporation":false,"usgs":false,"family":"Sachs","given":"Torsten","email":"","affiliations":[{"id":34716,"text":"GFZ German Research Centre for Geosciences, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":854502,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ueyama, Masahito 0000-0002-4000-4888","orcid":"https://orcid.org/0000-0002-4000-4888","contributorId":217432,"corporation":false,"usgs":false,"family":"Ueyama","given":"Masahito","email":"","affiliations":[{"id":39629,"text":"Osaka Prefecture University","active":true,"usgs":false}],"preferred":false,"id":854503,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Sonnentag, Oliver 0000-0001-9333-9721","orcid":"https://orcid.org/0000-0001-9333-9721","contributorId":225735,"corporation":false,"usgs":false,"family":"Sonnentag","given":"Oliver","email":"","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":854504,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Helbig, Manuel 0000-0003-1996-8639","orcid":"https://orcid.org/0000-0003-1996-8639","contributorId":217411,"corporation":false,"usgs":false,"family":"Helbig","given":"Manuel","email":"","affiliations":[{"id":25502,"text":"McMaster University","active":true,"usgs":false}],"preferred":false,"id":854505,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Tuittila, Eeva-Stiina 0000-0001-8861-3167","orcid":"https://orcid.org/0000-0001-8861-3167","contributorId":169412,"corporation":false,"usgs":false,"family":"Tuittila","given":"Eeva-Stiina","email":"","affiliations":[{"id":25501,"text":"University of Eastern Finland","active":true,"usgs":false}],"preferred":false,"id":854506,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Jurasinski, Gerald 0000-0002-6248-9388","orcid":"https://orcid.org/0000-0002-6248-9388","contributorId":260569,"corporation":false,"usgs":false,"family":"Jurasinski","given":"Gerald","email":"","affiliations":[],"preferred":false,"id":854507,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Koebsch, Franziska 0000-0003-1045-7680","orcid":"https://orcid.org/0000-0003-1045-7680","contributorId":260571,"corporation":false,"usgs":false,"family":"Koebsch","given":"Franziska","email":"","affiliations":[],"preferred":false,"id":854508,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Campbell, David I. 0000-0003-3432-4372","orcid":"https://orcid.org/0000-0003-3432-4372","contributorId":217402,"corporation":false,"usgs":false,"family":"Campbell","given":"David","email":"","middleInitial":"I.","affiliations":[{"id":12678,"text":"University of Waikato","active":true,"usgs":false}],"preferred":false,"id":854509,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Schmid, Hans Peter","contributorId":217429,"corporation":false,"usgs":false,"family":"Schmid","given":"Hans","email":"","middleInitial":"Peter","affiliations":[{"id":39624,"text":"Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":854510,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Lohila, Annalea 0000-0003-3541-672X","orcid":"https://orcid.org/0000-0003-3541-672X","contributorId":217418,"corporation":false,"usgs":false,"family":"Lohila","given":"Annalea","email":"","affiliations":[{"id":39618,"text":"Finnish Meteorological Institute","active":true,"usgs":false}],"preferred":false,"id":854511,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Goeckede, Mathias 0000-0003-2833-8401","orcid":"https://orcid.org/0000-0003-2833-8401","contributorId":217409,"corporation":false,"usgs":false,"family":"Goeckede","given":"Mathias","email":"","affiliations":[{"id":39621,"text":"Max Planck Institute for Biogeochemistry","active":true,"usgs":false}],"preferred":false,"id":854512,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Nilsson, Mats B. 0000-0003-3765-6399","orcid":"https://orcid.org/0000-0003-3765-6399","contributorId":217421,"corporation":false,"usgs":false,"family":"Nilsson","given":"Mats","email":"","middleInitial":"B.","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":854513,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Friborg, Thomas 0000-0001-5633-6097","orcid":"https://orcid.org/0000-0001-5633-6097","contributorId":217406,"corporation":false,"usgs":false,"family":"Friborg","given":"Thomas","email":"","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":854514,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Jansen, Joachim","contributorId":265843,"corporation":false,"usgs":false,"family":"Jansen","given":"Joachim","email":"","affiliations":[],"preferred":false,"id":854515,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Zona, Donatella","contributorId":217433,"corporation":false,"usgs":false,"family":"Zona","given":"Donatella","email":"","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":854516,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Euskirchen, Eugenie S. 0000-0002-0848-4295","orcid":"https://orcid.org/0000-0002-0848-4295","contributorId":173730,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugenie","email":"","middleInitial":"S.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":854517,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":217389,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854518,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Bohrer, Gil 0000-0002-9209-9540","orcid":"https://orcid.org/0000-0002-9209-9540","contributorId":217401,"corporation":false,"usgs":false,"family":"Bohrer","given":"Gil","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":854519,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Jin, Zhenong","contributorId":297865,"corporation":false,"usgs":false,"family":"Jin","given":"Zhenong","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854520,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Liu, Licheng","contributorId":297866,"corporation":false,"usgs":false,"family":"Liu","given":"Licheng","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854521,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Iwata, Hiroki 0000-0002-8962-8982","orcid":"https://orcid.org/0000-0002-8962-8982","contributorId":217413,"corporation":false,"usgs":false,"family":"Iwata","given":"Hiroki","email":"","affiliations":[{"id":39622,"text":"Shinshu University","active":true,"usgs":false}],"preferred":false,"id":854522,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Goodrich, Jordan P.","contributorId":243112,"corporation":false,"usgs":false,"family":"Goodrich","given":"Jordan","email":"","middleInitial":"P.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":854523,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Jackson, Robert B. 0000-0001-8846-7147","orcid":"https://orcid.org/0000-0001-8846-7147","contributorId":34252,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert","email":"","middleInitial":"B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":854524,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70236107,"text":"sir20225058 - 2022 - Evaluation of machine learning approaches for predicting streamflow metrics across the conterminous United States","interactions":[],"lastModifiedDate":"2026-04-23T16:41:32.595712","indexId":"sir20225058","displayToPublicDate":"2022-08-31T14:10:00","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":"2022-5058","displayTitle":"Evaluation of Machine Learning Approaches for Predicting Streamflow Metrics Across the Conterminous United States","title":"Evaluation of machine learning approaches for predicting streamflow metrics across the conterminous United States","docAbstract":"Few regional or national scale studies have evaluated machine learning approaches for predicting streamflow metrics at ungaged locations. Most such studies are limited by the number of dimensions of the streamflow regime investigated. This study, in contrast, provides a comprehensive evaluation of the streamflow regime based on three widely available machine learning approaches (support vector regression, random forest, and cubist regression) and on multiple linear regression to predict 106 natural streamflow metrics at ungaged locations. This evaluation is done for 545 streamgages across the northwest United States for recurrence-interval flood metrics and for 1,851 sites in the conterminous United States for non-flood metrics. The results indicate that for flood metrics, predictions by cubist regression and support vector regressions have substantially less error than the other approaches. For all the remaining streamflow metrics, random forest models outperform the other methods.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225058","usgsCitation":"Eng, K., and Wolock, D.M., 2022, Evaluation of machine learning approaches for predicting streamflow metrics across the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2022–5058, 27 p., https://doi.org/10.3133/sir20225058.","productDescription":"Report: iv, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120123","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":405778,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VQAZN7","text":"USGS data release","linkHelpText":"Calculated streamflow metrics for machine learning regionalization across the conterminous United States, 1950 to 2018"},{"id":405776,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5058/sir20225058.XML"},{"id":405774,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5058/sir20225058.pdf","text":"Report","size":"4.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5058"},{"id":405773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5058/coverthb.jpg"},{"id":405777,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5058/images/"},{"id":405775,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225058/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5058"},{"id":503374,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113428.htm","linkFileType":{"id":5,"text":"html"}}],"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}","contact":"Program Coordinator, Water Availability and Use Science Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org","doi":"10.13031/ja.14964","usgsCitation":"Douglas-Mankin, K., Rumsey, C., Sexstone, G., Ivahnenko, T.I., Houston, N., Chavarria, S., Senay, G.B., Foster, L.K., Thomas, J., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T.L., Pedraza, D.E., Lambert, P., and Johnson, M.S., 2022, Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis: Journal of the American Society of Agricultural and Biological Engineers, v. 65, no. 4, p. 881-901, https://doi.org/10.13031/ja.14964.","productDescription":"21 p.","startPage":"881","endPage":"901","ipdsId":"IP-133533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":446605,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.13031/ja.14964","text":"Publisher Index Page"},{"id":407213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.7314453125,\n 30.410781790845864\n ],\n [\n -102.21679687500001,\n 30.410781790845864\n ],\n [\n -102.21679687500001,\n 38.30718056188316\n ],\n [\n -109.7314453125,\n 38.30718056188316\n ],\n [\n -109.7314453125,\n 30.410781790845864\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":223378,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":852780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":852783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":852786,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852787,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":217874,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852788,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flickinger, Allison K. 0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852789,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852790,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852791,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852793,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":207782,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana","email":"","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852796,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lambert, Patrick M. 0000-0001-6808-2303","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":296913,"corporation":false,"usgs":false,"family":"Lambert","given":"Patrick M.","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":852794,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Michael Scott 0000-0003-2378-7144 johnsonm@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-7144","contributorId":296914,"corporation":false,"usgs":true,"family":"Johnson","given":"Michael","email":"johnsonm@usgs.gov","middleInitial":"Scott","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852795,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70236124,"text":"ofr20221044 - 2022 - Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19","interactions":[],"lastModifiedDate":"2022-09-27T13:30:35.071237","indexId":"ofr20221044","displayToPublicDate":"2022-08-30T13:38:50","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":"2022-1044","displayTitle":"Distribution and Demography of Coastal Cactus Wrens in Southern California, 2015–19","title":"Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19","docAbstract":"Surveys and monitoring for the coastal Cactus Wren (Campylorhynchus brunneicapillus) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.
We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.
Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.
A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.
Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.
Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.
Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.
Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (Sambucus mexicana spp. caerulea), cactus (Opuntia spp. and Cylindropuntia spp.), California buckwheat (Eriogonum fasciculatum), native bunch grasses, and black mustard (Brassica nigra). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.
Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.
We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.
Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3065185546875,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.52365781569917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.
This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (Rana pipiens) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.
Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.
STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.
While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236251,"text":"70236251 - 2022 - Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century","interactions":[],"lastModifiedDate":"2022-08-31T11:35:53.919041","indexId":"70236251","displayToPublicDate":"2022-08-30T06:32:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century","docAbstract":"Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long-term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period-of-record flows. We then analyzed drought trends using the Mann-Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.
The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random.
In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm2A19","collaboration":"Prepared in cooperation with North Carolina State University, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology","usgsCitation":"Rubino, M.J., McKerrow, A.J., Tarr, N.M., and Williams, S.G., 2022, Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data: U.S. Geological Survey Techniques and Methods 2-A19, 13 p., https://doi.org/10.3133/tm2A19.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124954","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":405205,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a19/images"},{"id":405206,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.xml"},{"id":405202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2A-19"},{"id":405201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a19/coverthb.jpg"},{"id":405204,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H1308B","text":"USGS data release","linkHelpText":"Black-bellied Salamander (Desmognathus quadramaculatus) aBESAx_CONUS_2001v1 Habitat Map"}],"country":"United States","state":"Alabama, Georgia, North Carolina, Tennessee, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.9580078125,\n 36.73888412439431\n ],\n [\n -82.41943359375,\n 36.73888412439431\n ],\n [\n -83.1884765625,\n 36.43896124085945\n ],\n [\n -84.04541015625,\n 36.13787471840729\n ],\n [\n -84.92431640625,\n 35.99578538642032\n ],\n [\n -85.7373046875,\n 35.38904996691167\n ],\n [\n -86.24267578125,\n 35.11990857099681\n ],\n [\n -86.220703125,\n 34.50655662164561\n ],\n [\n -85.58349609375,\n 34.361576287484176\n ],\n [\n -84.17724609375,\n 34.79576153473033\n ],\n [\n -82.81494140625,\n 35.02999636902566\n ],\n [\n -82.08984375,\n 35.28150065789119\n ],\n [\n -81.10107421874999,\n 35.44277092585766\n ],\n [\n -80.26611328125,\n 36.2265501474709\n ],\n [\n -80.068359375,\n 36.77409249464195\n ],\n [\n -79.7607421875,\n 37.37015718405753\n ],\n [\n -79.453125,\n 37.71859032558816\n ],\n [\n -79.2333984375,\n 38.09998264736481\n ],\n [\n -79.25537109375,\n 38.59970036588819\n ],\n [\n -79.65087890624999,\n 38.54816542304656\n ],\n [\n -80.04638671875,\n 38.42777351132902\n ],\n [\n -80.5517578125,\n 38.39333888832238\n ],\n [\n -80.83740234375,\n 37.996162679728116\n ],\n [\n -81.18896484375,\n 37.405073750176925\n ],\n [\n -81.49658203125,\n 36.914764288955936\n ],\n [\n -81.9580078125,\n 36.73888412439431\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Core Science Analytics and Synthesis
U.S. Geological Survey
Box 25046, Mail Stop 302
Denver, CO 80225
Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.
Features:
The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
","language":"English","publisher":"Chapman and Hall/CRC","doi":"10.1201/9781351018784","usgsCitation":"Qian, S.S., Dufour, M.R., and Alameddine, I., 2022, Bayesian applications in environmental and ecological studies with R and Stan, 415 p., https://doi.org/10.1201/9781351018784.","productDescription":"415 p.","ipdsId":"IP-134860","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":407695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S.","contributorId":198934,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":842388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alameddine, Ibrahim","contributorId":244836,"corporation":false,"usgs":false,"family":"Alameddine","given":"Ibrahim","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":842389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236495,"text":"70236495 - 2022 - Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","interactions":[],"lastModifiedDate":"2022-09-09T13:17:21.375872","indexId":"70236495","displayToPublicDate":"2022-08-27T08:10:01","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","docAbstract":"Using images from an updated and expanded three-dimensional electrical conductivity synthesis model for the contiguous United States (CONUS), we highlight the key continent-scale geoelectric structures that are associated with the Precambrian assembly of southern Laurentia. Conductivity anomalies are associated with the Trans-Hudson orogen, the Penokean suture, the ca. 1.8–1.7 Ga Cheyenne belt and Spirit Lake tectonic zone, and the Grenville suture zone; the geophysical characteristics of these structures indicate that the associated accretionary events involved the closure of ancient ocean basins along discrete, large-scale structures. In contrast, we observe no large-scale conductivity anomalies through the portion of southern Laurentia that is generally viewed as composed of late Paleoproterozoic–early Mesoproterozoic accretionary crust. The lack of through-going conductors places constraints on the structure, petrology, and geodynamic history of crustal growth in southern Laurentia during that time period. Overall, our model highlights the enigmatic nature of the concealed Precambrian basement of much of southern Laurentia, as it in some places supports and in other places challenges prevailing models of Laurentian assembly. The revised CONUS electrical conductivity model thus provides important constraints for testing new models of Precambrian tectonism in this region.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Laurentia: Turning points in the evolution of a continent","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.1220(13)","usgsCitation":"Murphy, B.S., Bedrosian, P.A., and Kelbert, A., 2022, Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia, chap. of Laurentia: Turning points in the evolution of a continent, v. 220, 18 p., https://doi.org/10.1130/2022.1220(13).","productDescription":"18 p.","ipdsId":"IP-131709","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":446629,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.1220(13)","text":"Publisher Index Page"},{"id":406446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"continental 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":"220","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":851252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":851254,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236273,"text":"70236273 - 2022 - Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines","interactions":[],"lastModifiedDate":"2022-08-31T12:24:53.280063","indexId":"70236273","displayToPublicDate":"2022-08-26T07:24:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines","docAbstract":"In the United States, national chemical water quality criteria for the protection of aquatic life assume that aquatic ecosystems have sufficient resiliency to recover from criteria exceedences occurring up to once every 3 years. This resiliency assumption was critically reviewed through two approaches: 1) synthesis of case studies and 2) population modeling. The population modeling examined differences in recovery of species with widely different life histories. One invertebrate (Hyalella azteca) and four fish species were modeled (fathead minnow, brook trout, lake trout, and shortnose sturgeon) with various disturbance magnitudes and intervals. The synthesis of ecosystem case studies showed generally faster recoveries for insect communities rather than fish, and recoveries from pulse (acute) disturbances were often faster than recoveries from press (chronic) disturbances. When the recovery dataset excluded severe disturbances that seemed unrepresentative of common facility discharge upsets that might cause criteria exceedences, the median recovery time was 1 year, 81% of the cases were considered recovered within 3 years, and 95% were considered recovered within 10 years. The modeling projected that short-lived fish species with high recovery times could thrive despite enduring 50% mortality disturbances every other year. However, long-lived fish species had longer recovery times and declined under the 1 disturbance every 3 years scenario. Overall, the analyses did not refute the long-standing judgements that 3 years is generally sufficient for recovery from non-repetitive, moderate intensity disturbances of a magnitude up to 2X the chronic criteria in waters without other pollution sources or stresses. However, these constraints may not always be met and if long-lived fish species are a concern, longer return intervals such as 5 to 10 years could be indicated.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5471","usgsCitation":"Mebane, C.A., 2022, The capacity of freshwater ecosystems to recover from exceedances of aquatic life criteria: Environmental Toxicology and Chemistry, v. 41, no. 12, p. 2887-2910, https://doi.org/10.1002/etc.5471.","productDescription":"24 p.","startPage":"2887","endPage":"2910","ipdsId":"IP-125552","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":446640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5471","text":"Publisher Index Page"},{"id":406944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852244,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236338,"text":"70236338 - 2022 - Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska","interactions":[],"lastModifiedDate":"2022-10-17T16:08:48.61057","indexId":"70236338","displayToPublicDate":"2022-08-25T09:39:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska","docAbstract":"Although polar bears (Ursus maritimus) of the Southern Beaufort Sea (SBS) subpopulation have commonly created maternal dens on sea ice in the past, maternal dens on land have become increasingly prevalent as sea ice declines. This trend creates conditions for increased human–bear interactions associated with local communities and industrial activity. Maternal denning is a vulnerable period in the polar bear life cycle, and den disturbance could lead to den abandonment, cub mortality, and negative population impacts. We used published long-term data to parameterize a Bayesian hierarchical model of annual land den abundance during 2000–2015, in 4 regions of northern Alaska, USA, with current or potential future oil and gas activity. We also estimated long-term (1982–2015) shifts in the spatial distribution of land dens within and among regions using kernel density estimation and assessed the influence of local and regional sea ice and snow conditions on den site selection using a random forest resource selection function. Our objectives were to quantify current den distribution and abundance, test for distributional shifts over time, and investigate if those shifts could be attributed to environmental variables related to den habitat. We estimated that between 2000 and 2015, the SBS contained a median 123 dens in a typical year, of which 68 occurred on land. The region between the Colville and Canning rivers, where most current oil and gas activity occurred, also contained the largest fraction of land dens. Overall, land dens were disproportionately concentrated on barrier islands and on land within 30 km of the coast. The probability of dens occurring on land varied from 1982–1999 to 2000–2015 in all regions, and the overall distribution of land dens shifted west between those periods. This regional-scale change in den distribution was predictable based on spatial and temporal heterogeneity in snow and sea ice conditions within 50 km of individual den locations. Land denning is likely to become increasingly common with continued sea ice loss, and our results and modeling framework could be used to design additional mitigation strategies for reducing the risk of incidental take due to den disturbance.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.22302","usgsCitation":"Patil, V.P., Durner, G.M., Douglas, D.C., and Atwood, T.C., 2022, Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska: Journal of Wildlife Management, v. 86, no. 8, e22302, 22 p., https://doi.org/10.1002/jwmg.22302.","productDescription":"e22302, 22 p.","ipdsId":"IP-134179","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":446643,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22302","text":"Publisher Index Page"},{"id":435714,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZNG8JT","text":"USGS data release","linkHelpText":"Code for analysis of polar bear maternal den abundance and distribution in four regions of northern Alaska and Canada within the Southern Beaufort Sea subpopulation boundary (1982-2015)"},{"id":406139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canadian, United States","state":"Alaska","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -172.6171875,\n 67.7427590666639\n ],\n [\n -122.51953124999999,\n 67.7427590666639\n ],\n [\n -122.51953124999999,\n 77.5041191797399\n ],\n [\n -172.6171875,\n 77.5041191797399\n ],\n [\n -172.6171875,\n 67.7427590666639\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":850654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":850655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":850656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":850657,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255104,"text":"70255104 - 2022 - Wildfire influences individual growth and breeding dispersal, but not survival and recruitment in a montane amphibian","interactions":[],"lastModifiedDate":"2024-06-17T14:07:46.883503","indexId":"70255104","displayToPublicDate":"2022-08-25T09:02:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire influences individual growth and breeding dispersal, but not survival and recruitment in a montane amphibian","docAbstract":"Global wildfire regimes are changing rapidly, with widespread increases in the size, frequency, duration, and severity of wildfires. Whereas the effects of wildfire on ecological state variables such as occupancy, abundance, and species diversity are relatively well documented, changes in population vital rates (e.g., survival, recruitment) and individual responses (e.g., growth, movement) to wildfire are more limited because of the detailed information needed on the same individuals both pre- and post-fire. We capitalized on the 2018 Roosevelt wildfire, which occurred during our 6-year (2015–2020) capture–mark–recapture study of boreal toads (Anaxyrus boreas boreas; n = 1415) in the Bridger-Teton National Forest, USA, to evaluate the responses of population vital rates and individual metrics to wildfire. We employed robust design capture–recapture models to compare the growth, dispersal, survival, and recruitment of adult boreal toads pre- and post-fire at burned versus unburned sites. At burned locations, growth increased 2 years post-fire compared with the year directly following wildfire and was higher 2 years post-fire than any other interval during our study period. Boreal toads dispersed to alternative breeding patches more at burned sites than unburned sites and dispersal increased 2 years post-fire compared with the year directly following wildfire. Annual survival and recruitment neither differed between pre- and post-fire years nor among pre-fire years, the year following wildfire, and 2 years post-fire. We demonstrate that, in certain contexts, dispersal can play a major role in changes to state variables (e.g., abundance) after wildfire, as opposed to other vital rates such as survival and recruitment. Our study represents an important step toward understanding the biological processes that underlie observed patterns in state variables following wildfire, which ultimately will be critical for the effective management of species in landscapes experiencing shifts in fire activity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4212","usgsCitation":"Barrile, G., Chalfoun, A.D., Estes-Zumpf, W.A., and Walters, A.W., 2022, Wildfire influences individual growth and breeding dispersal, but not survival and recruitment in a montane amphibian: Ecosphere, v. 13, no. 8, e4212, 18 p., https://doi.org/10.1002/ecs2.4212.","productDescription":"e4212, 18 p.","ipdsId":"IP-134283","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446650,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4212","text":"Publisher Index Page"},{"id":430272,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.6,\n 43.32\n ],\n [\n -110.6,\n 42.9\n ],\n [\n -109.8,\n 42.9\n ],\n [\n -109.8,\n 43.32\n ],\n [\n -110.6,\n 43.32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":338642,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Estes-Zumpf, Wendy A.","contributorId":338643,"corporation":false,"usgs":false,"family":"Estes-Zumpf","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":903417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903414,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255280,"text":"70255280 - 2022 - Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","interactions":[],"lastModifiedDate":"2024-06-17T13:49:07.864148","indexId":"70255280","displayToPublicDate":"2022-08-25T08:43:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","docAbstract":"Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking.
We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units.
We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA).
We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance.
Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01510-z","usgsCitation":"Bauder, J.M., Chandler, H.C., Elmore, M., and Jenkins, C.L., 2022, Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake: Landscape Ecology, v. 37, https://doi.org/10.1007/s10980-022-01510-z.","productDescription":"15 p.","startPage":"2533","ipdsId":"IP-137585","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467167,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/666095","text":"External Repository"},{"id":430270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6589463070196,\n 30.27164925540076\n ],\n [\n -87.4471664851995,\n 30.273991912380097\n ],\n [\n -87.243062061103,\n 30.25688691395483\n ],\n [\n -86.504722900934,\n 30.288280599826876\n ],\n [\n -85.8821985085937,\n 30.127920150613292\n ],\n [\n -85.45848132490356,\n 29.678163497359677\n ],\n [\n -85.18012336836138,\n 29.52298729685313\n ],\n [\n -84.431494016169,\n 29.8435460527122\n ],\n [\n -84.27785036727468,\n 29.98120848065058\n ],\n [\n -83.89226362456542,\n 29.960791456317352\n ],\n [\n -83.47317674842255,\n 29.509171366635385\n ],\n [\n -83.08833538532544,\n 29.1434267271204\n ],\n [\n -82.82094989668451,\n 29.0956993019621\n ],\n [\n -82.67858009554807,\n 28.666898644234394\n ],\n [\n -82.84220852464915,\n 27.892907277971844\n ],\n [\n -81.9139493773393,\n 26.026944742093463\n ],\n [\n -81.3608497502143,\n 25.677695288573233\n ],\n [\n -81.20240783558897,\n 25.110332177132477\n ],\n [\n -80.5049186418957,\n 25.163605849744997\n ],\n [\n -81.39546331741505,\n 24.792875468134326\n ],\n [\n -81.84017458395616,\n 24.611130518564238\n ],\n [\n -81.49095266413781,\n 24.52625187147568\n ],\n [\n -80.30964454840066,\n 25.01219197845387\n ],\n [\n -80.28167788112444,\n 25.441483917895624\n ],\n [\n -80.07107634394508,\n 25.88314239044695\n ],\n [\n -80.01314206035136,\n 26.51492387439309\n ],\n [\n -80.110778674335,\n 27.072566624838785\n ],\n [\n -80.54296836001978,\n 28.439325701479135\n ],\n [\n -80.92465488637467,\n 29.033168269780845\n ],\n [\n -81.40440957377771,\n 30.780806652102896\n ],\n [\n -80.97885233616921,\n 32.04045515787071\n ],\n [\n -81.28694625263563,\n 32.58791480990446\n ],\n [\n -81.99407467041935,\n 32.551271523349655\n ],\n [\n -87.68936380825409,\n 31.462681042444245\n ],\n [\n -87.8537521440345,\n 31.229156643229587\n ],\n [\n -87.97481408751028,\n 30.53533344322976\n ],\n [\n -87.6589463070196,\n 30.27164925540076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","edition":"2519","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":904088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, H. C.","contributorId":339318,"corporation":false,"usgs":false,"family":"Chandler","given":"H.","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":904089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elmore, M.","contributorId":339320,"corporation":false,"usgs":false,"family":"Elmore","given":"M.","email":"","affiliations":[{"id":81289,"text":"Georgia Ecological Services","active":true,"usgs":false}],"preferred":false,"id":904090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, C. L.","contributorId":339321,"corporation":false,"usgs":false,"family":"Jenkins","given":"C.","email":"","middleInitial":"L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":904091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}