{"pageNumber":"179","pageRowStart":"4450","pageSize":"25","recordCount":41062,"records":[{"id":70236046,"text":"70236046 - 2022 - Seismostratigraphic analysis of Lake Cahuilla sedimentation cycles and fault displacement history beneath the Salton Sea, California, USA","interactions":[],"lastModifiedDate":"2022-08-26T12:16:47.972733","indexId":"70236046","displayToPublicDate":"2022-06-17T07:10:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seismostratigraphic analysis of Lake Cahuilla sedimentation cycles and fault displacement history beneath the Salton Sea, California, USA","docAbstract":"<div id=\"134024229\" class=\"article-section-wrapper js-article-section js-content-section  \"><p>The Salton Trough (southeastern California, USA) is the northernmost transtensional stepover of the Gulf of California oblique-divergent plate boundary and is also where the southern terminus of the San Andreas fault occurs. Until recently, the distribution of active faults in and around the Salton Sea and their displacement histories were largely unknown. Subbottom CHIRP (compressed high-intensity radar pulse) surveys in the Salton Sea are used to develop a seismic facies model for ancient Lake Cahuilla deposits, a detailed map of submerged active faults, and reconstructed fault displacement histories during the late Holocene. We observe as many as fourteen Lake Cahuilla sequences in the Salton Sea (last ~3 k.y.) and develop a chronostratigraphic framework for the last six sequences (last ~1200 yr) by integrating CHIRP data and cone penetrometer logs with radiocarbon-dated stratigraphy at an onshore paleoseismic site. The Salton Sea contains northern and southern subbasins that appear to be separated by a tectonic hinge zone, and a subsidence signal across hinge-zone faults of 6–9 mm/yr (since ca. A.D. 940) increases toward the south to &gt;15 mm/yr. The faults mapped to the south of the hinge zone appear to accommodate transtension within the San Andreas–Imperial fault stepover. We identify 8–15 distinct growth events across hinge-zone faults, meaning growth occurred at least once every 100 yr since Lake Cahuilla sedimentation began. Several faults offset the top of the most recent Lake Cahuilla highstand deposits, and at least two faults have offset the Salton Sea flood deposits. Active faults and folds were also mapped to a limited extent within the northern subbasin and display growth, but their kinematics and rupture histories require further study. The broad distribution of active faulting suggests that strain between the San Andreas, San Jacinto, and Imperial faults is highly distributed, thus discrepancies between geologic and geodetic slip-rate estimates from these major fault systems are to be expected.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02468.1","usgsCitation":"Brothers, D., Driscoll, N.W., Kent, G., Baskin, R.L., Harding, A.J., and Kell, A., 2022, Seismostratigraphic analysis of Lake Cahuilla sedimentation cycles and fault displacement history beneath the Salton Sea, California, USA: Geosphere, v. 18, no. 4, p. 1354-1376, https://doi.org/10.1130/GES02468.1.","productDescription":"23 p.","startPage":"1354","endPage":"1376","ipdsId":"IP-138899","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":447403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02468.1","text":"Publisher Index Page"},{"id":405678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.5,\n               32.5\n            ],\n            [\n              -115,\n               32.5\n            ],\n            [\n              -115,\n              34\n            ],\n            [\n              -116.5,\n              34\n            ],\n            [\n              -116.5,\n               32.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Brothers, Daniel","contributorId":295722,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":849801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Neal W.","contributorId":63266,"corporation":false,"usgs":true,"family":"Driscoll","given":"Neal","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":849802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Graham","contributorId":7608,"corporation":false,"usgs":true,"family":"Kent","given":"Graham","affiliations":[],"preferred":false,"id":849803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baskin, Robert L. 0000-0002-2175-8502 rbaskin@usgs.gov","orcid":"https://orcid.org/0000-0002-2175-8502","contributorId":360,"corporation":false,"usgs":true,"family":"Baskin","given":"Robert","email":"rbaskin@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harding, Alistair J.","contributorId":53270,"corporation":false,"usgs":true,"family":"Harding","given":"Alistair","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":849805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kell, Annie","contributorId":68176,"corporation":false,"usgs":true,"family":"Kell","given":"Annie","affiliations":[],"preferred":false,"id":849806,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70232236,"text":"70232236 - 2022 - Enumerating plausible multifault ruptures in complex fault systems with physical constraints","interactions":[],"lastModifiedDate":"2022-08-02T14:30:33.803665","indexId":"70232236","displayToPublicDate":"2022-06-16T09:04:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Enumerating plausible multifault ruptures in complex fault systems with physical constraints","docAbstract":"We propose a new model for determining the set of plausible multifault ruptures in an interconnected fault system. We improve upon the rules used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to increase connectivity and the physical consistency of ruptures. We replace UCERF3’s simple azimuth change rules with new Coulomb favorability metrics and increase the maximum jump distance to 15 km. Although the UCERF3 rules were appropriate for faults with similar rakes, the Coulomb calculations used here inherently encode preferred orientations between faults with different rakes. Our new rules are designed to be insensitive to discretization details and are generally more permissive than their UCERF3 counterparts; they allow more than twice the connectivity compared to UCERF3, yet heavily penalize long ruptures that take multiple improbable jumps. The set of all possible multifault ruptures in the California fault system is near-infinite, but our model produces a tractable set of 326,707 ruptures (a modest 29% increase over UCERF3, despite the greatly increased connectivity). Inclusion in the rupture set does not dictate that a rupture receives a significant rate in the final model; rupture rates are subsequently determined by data constraints used in an inversion.\n\nWe describe the rupture building algorithm and its components in detail and provide comparisons with ruptures generated by a physics-based multicycle earthquake simulator. We find that greater than twice as many ruptures generated by the simulator violate the UCERF3 rules than violate our proposed model.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210322","usgsCitation":"Milner, K.R., Shaw, B.E., and Field, E.H., 2022, Enumerating plausible multifault ruptures in complex fault systems with physical constraints: Bulletin of the Seismological Society of America, v. 112, no. 4, p. 1806-1824, https://doi.org/10.1785/0120210322.","productDescription":"19 p.","startPage":"1806","endPage":"1824","ipdsId":"IP-139511","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70237243,"text":"70237243 - 2022 - A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.","interactions":[],"lastModifiedDate":"2022-10-05T13:32:04.880727","indexId":"70237243","displayToPublicDate":"2022-06-16T08:19:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.","docAbstract":"<p><span>Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5</span><span class=\"inline-formula\"><sup>∘</sup></span><span> </span><span class=\"inline-formula\">×</span><span> 0.5</span><span class=\"inline-formula\"><sup>∘</sup></span><span>&nbsp;global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.</span></p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/gmd-15-4597-2022","usgsCitation":"Golub, M., Thiery, W., Marce, R., Pierson, D., Vanderkelen, I., Mercado-Bettin, D., Woolway, R., Grant, L., Jennings, E., Kraemer, B., Schewe, J., Zhao, F., Frieler, K., Mengel, M., Bogomolov, V.Y., Bouffard, D., Cote, M., Couture, R., Debolskiy, A.V., Droppers, B., Gal, G., Guo, M., Janssen, A.B., Kirillin, G., Ladwig, R., Magee, M., Moore, T., Perroud, M., Piccolroaz, S., Raaman Vinnea, L., Schmid, M., Shatwell, T., Stepanenko, V.M., Tan, Z., Woodward, B., Yao, H., Adrian, R., Allan, M., Anneville, O., Arvola, L., Atkins, K., Boegman, L., Carey, C.C., Christianson, K., de Eyto, E., DeGasperi, C.L., Grechushnikova, M., Hejzlar, J., Joehnk, K., Jones, I.D., Laas, A., MacKay, E.B., Mammarella, I., Markensten, H., McBride, C.G., Özkundakci, D., Potes, M., Rinke, K., Robertson, D., Rusak, J.A., Salgado, R., van der Linden, L., Verburg, P., Wain, D., Ward, N.K., Wollrab, S., and Zdorovennova, G., 2022, A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.: Geoscientific Model Development, v. 15, p. 4297-4623, https://doi.org/10.5194/gmd-15-4597-2022.","productDescription":"27 p.","startPage":"4297","endPage":"4623","ipdsId":"IP-136556","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-15-4597-2022","text":"Publisher Index Page"},{"id":407955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Golub, Malgorzata","contributorId":297257,"corporation":false,"usgs":false,"family":"Golub","given":"Malgorzata","email":"","affiliations":[{"id":35850,"text":"Uppsala University, Sweden","active":true,"usgs":false}],"preferred":false,"id":853719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thiery, Wim","contributorId":223158,"corporation":false,"usgs":false,"family":"Thiery","given":"Wim","email":"","affiliations":[],"preferred":false,"id":853720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marce, Rafael","contributorId":297259,"corporation":false,"usgs":false,"family":"Marce","given":"Rafael","email":"","affiliations":[{"id":64329,"text":"Catalan Institute for Water Research (ICRA), Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":853721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pierson, Don","contributorId":194465,"corporation":false,"usgs":false,"family":"Pierson","given":"Don","email":"","affiliations":[],"preferred":false,"id":853722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderkelen, Inne","contributorId":297260,"corporation":false,"usgs":false,"family":"Vanderkelen","given":"Inne","email":"","affiliations":[{"id":64331,"text":"Vrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, Brussels, Belgium","active":true,"usgs":false}],"preferred":false,"id":853723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mercado-Bettin, Daniel","contributorId":297261,"corporation":false,"usgs":false,"family":"Mercado-Bettin","given":"Daniel","affiliations":[{"id":64329,"text":"Catalan Institute for Water Research (ICRA), Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":853724,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woolway, R. 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M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853777,"contributorType":{"id":1,"text":"Authors"},"rank":59},{"text":"Rusak, James A. 0000-0002-4939-6478","orcid":"https://orcid.org/0000-0002-4939-6478","contributorId":150301,"corporation":false,"usgs":false,"family":"Rusak","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":17970,"text":"Dorset Environmental Science Centre, Ontario Ministry of the Environment and Climate Change, Dorset, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":853778,"contributorType":{"id":1,"text":"Authors"},"rank":60},{"text":"Salgado, Rui","contributorId":297291,"corporation":false,"usgs":false,"family":"Salgado","given":"Rui","email":"","affiliations":[{"id":64351,"text":"Universidade de Évora, Instituto de Ciências da Terra, Departamento de Física, Escola de Ciências e Tecnologia, Évora, Portugal","active":true,"usgs":false}],"preferred":false,"id":853779,"contributorType":{"id":1,"text":"Authors"},"rank":61},{"text":"van der Linden, Leon","contributorId":297292,"corporation":false,"usgs":false,"family":"van der Linden","given":"Leon","email":"","affiliations":[{"id":64352,"text":"South Australian Water Corporation, Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":853780,"contributorType":{"id":1,"text":"Authors"},"rank":62},{"text":"Verburg, Piet","contributorId":150311,"corporation":false,"usgs":false,"family":"Verburg","given":"Piet","email":"","affiliations":[{"id":17985,"text":"National Institute of Water and Atmospheric Research, Hamilton, New Zealand","active":true,"usgs":false}],"preferred":false,"id":853781,"contributorType":{"id":1,"text":"Authors"},"rank":63},{"text":"Wain, Danielle","contributorId":297293,"corporation":false,"usgs":false,"family":"Wain","given":"Danielle","email":"","affiliations":[{"id":64353,"text":"7 Lakes Alliance, Belgrade Lakes, Maine, USA 04901","active":true,"usgs":false}],"preferred":false,"id":853782,"contributorType":{"id":1,"text":"Authors"},"rank":64},{"text":"Ward, Nicole K.","contributorId":297294,"corporation":false,"usgs":false,"family":"Ward","given":"Nicole","email":"","middleInitial":"K.","affiliations":[{"id":64354,"text":"Virginia Tech, Department of Biological Sciences & Forest Resources & Environmental Conservation, Blacksburg, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":853783,"contributorType":{"id":1,"text":"Authors"},"rank":65},{"text":"Wollrab, Sabine","contributorId":297295,"corporation":false,"usgs":false,"family":"Wollrab","given":"Sabine","email":"","affiliations":[{"id":64355,"text":"Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":853784,"contributorType":{"id":1,"text":"Authors"},"rank":66},{"text":"Zdorovennova, Galina","contributorId":297296,"corporation":false,"usgs":false,"family":"Zdorovennova","given":"Galina","email":"","affiliations":[{"id":64356,"text":"Northern water problems Institute Karelian Research Centre of RAS, Petrozavodsk, Russia","active":true,"usgs":false}],"preferred":false,"id":853785,"contributorType":{"id":1,"text":"Authors"},"rank":67}]}}
,{"id":70232905,"text":"70232905 - 2022 - Statistical assessment on determining local presence of rare bat species","interactions":[],"lastModifiedDate":"2022-07-13T11:50:01.124415","indexId":"70232905","displayToPublicDate":"2022-06-16T06:47:14","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":"Statistical assessment on determining local presence of rare bat species","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Surveying cryptic, sparsely distributed taxa using autonomous recording units, although cost-effective, provides imperfect knowledge about species presence. Summertime bat acoustic surveys in North America exemplify the challenges with characterizing sources of uncertainty: observation error, inability to census populations, and natural stochastic variation. Statistical uncertainty, if not considered thoroughly, hampers determining rare species presence accurately and/or estimating rangewide status and trends with suitable precision. Bat acoustic data are processed using an automated workflow in which proprietary or open-source algorithms assign a species label to each recorded high-frequency echolocation sequence. A false-negative occurs, if a species is actually present but not recorded and/or all recordings from the species are of such poor quality that a correct species identity cannot be assigned to any observation. False positives for a focal species are a direct result of the presence and incorrect identification of a recording from another species. We compare four analytical approaches in terms of parameter estimation and their resulting (in)correct decisions regarding species presence or absence using realistic data-generating scenarios for bat acoustic data within a simulation study. The current standard for deciding species presence or absence uses a multinomial likelihood-ratio test<span>&nbsp;</span><i>p</i><span>&nbsp;</span>value (maximum likelihood estimate [MLE]-metric) that accounts for known species misidentifications, but not imperfect detection and only returns a binary outcome (evidence of presence or not). We found that the MLE-metric had estimated median correct decisions less than 60% for presence and greater than 85% for absence. Alternatively, a multispecies count detection model was equivalent to or better than the MLE-metric for correct claims of rare species presence or absence using the posterior probability a species was present at a site and, importantly, provided unbiased estimates of relative activity and probability of occurrence, creating opportunities for reducing posterior uncertainty through the inclusion of meaningful covariates. Single-species occupancy models with and without false-positive detections removed were insufficient for determining local presence because of substantially biased occurrence and detection probabilities. We propose solutions to potential barriers for integrating local, short-term and rangewide, long-term acoustic surveys within a cohesive statistical framework that facilitates determining local species presence with uncertainty concurrent with estimating species–environment relationships.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4142","usgsCitation":"Irvine, K.M., Banner, K., Stratton, C., Ford, W., and Reichert, B., 2022, Statistical assessment on determining local presence of rare bat species: Ecosphere, v. 13, no. 6, e4142, 15 p., https://doi.org/10.1002/ecs2.4142.","productDescription":"e4142, 15 p.","ipdsId":"IP-133431","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":447424,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.4142","text":"External Repository"},{"id":403588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":846458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banner, Katharine M.","contributorId":244876,"corporation":false,"usgs":false,"family":"Banner","given":"Katharine M.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":846459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stratton, Christian","contributorId":265905,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":846460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":846461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":846462,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70256680,"text":"70256680 - 2022 - Adaptive problem maps (APM): Connecting data dots to build increasingly informed and defensible environmental conservation decisions","interactions":[],"lastModifiedDate":"2024-08-01T19:45:18.227588","indexId":"70256680","displayToPublicDate":"2022-06-15T14:37:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Adaptive problem maps (APM): Connecting data dots to build increasingly informed and defensible environmental conservation decisions","docAbstract":"Connecting individual datasets from different projects to each other and to decisions can help manager-researcher-administrator teams build on what is known and adapt their environmental decision-making process as new information becomes available. Throughout their careers, environmental professionals often collect data on many individual projects that address similar sets of natural resource conservation problems. Consequently, the institutions, agencies, and organizations that employ these environmental professionals accumulate a large reservoir of project-specific information. However, bigger-picture opportunities to advance broader natural resource conservation goals are lost if individual projects and datasets are not integrated. Here we illustrate how our adaptive problem mapping (APM) process provides a framing and internal structure that charts relationships among pertinent information types, germane data sets, applicable concepts, and relevant decisions. In the APM process, appropriately defined problem statements and coordinated bridging questions connect data and concepts to build a network of increasingly informed and defensible decisions. Although our APM process can be applied to many environmental problems, here we focus on examples from aquatic systems in which fish are conservation priorities. Prioritizing an initial evaluation and regular modification of the relationships among datasets and decisions using the APM process helps manager-research-administrator teams envision, track, and update what is known, unknown, learned, and needed. The resulting broader point of view advances strategic planning, evaluations of progress, assessments of opportunity costs, identification of options, and justifications of decision-related actions. \n ","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.114826","usgsCitation":"Mather, M.E., and Dettmers, J., 2022, Adaptive problem maps (APM): Connecting data dots to build increasingly informed and defensible environmental conservation decisions: Journal of Environmental Management, v. 312, 114826, 10 p., https://doi.org/10.1016/j.jenvman.2022.114826.","productDescription":"114826, 10 p.","ipdsId":"IP-138779","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"312","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dettmers, John M.","contributorId":341569,"corporation":false,"usgs":false,"family":"Dettmers","given":"John M.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":908630,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70254299,"text":"70254299 - 2022 - Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region","interactions":[],"lastModifiedDate":"2024-05-17T13:58:51.399871","indexId":"70254299","displayToPublicDate":"2022-06-15T08:54:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region","docAbstract":"<p><span>For agricultural areas facing water scarcity, sustainable water use policy relies on irrigation information that is timely and at a high resolution, but existing publicly available water use data are often insufficient for monitoring compliance or understanding the influence of policy on individual farmer decisions. This study attempts to fill this data gap by using remote sensing to map annual irrigation quantity at the field-scale within the central Ogallala aquifer region of the United States. We compiled in situ annual irrigation volume data at the field scale in the Republican River Basin of Colorado for 2015–2018 and at the Public Land Survey System (PLSS) section scale in western Kansas for 2000–2016, which served as reference data in random forest models that relied on Landsat-based actual evapotranspiration from the Operational Simplified Surface Energy Balance model (SSEBop) along with maps of irrigated area, Landsat spectral indices, climate, soils, and derived hydrologic variables. The models explained 87% of the variability in irrigation volume in Colorado and 75% in Kansas, but accuracy declined when transferring the models in spatial cross-validation (Colorado R</span><sup>2</sup><span>&nbsp;=0.81; Kansas R</span><sup>2</sup><span>&nbsp;=0.51) and temporal cross-validation (Colorado R</span><sup>2</sup><span>&nbsp;=0.82; Kansas R</span><sup>2</sup><span>&nbsp;=0.68). Predicted annual totals of irrigation volume in western Kansas had a mean absolute error of 11.9%, which was slightly higher than the average annual change of 11%. Use of predicted irrigation maps also lead to an underestimated effect size for a water use restriction policy in Kansas. These results indicate that field- and section-scale irrigation can be mapped with reasonable accuracy within a region and time period that has adequate sample data, but that methods may need to be improved for applying the models more broadly in areas that lack extensive in situ irrigation data to support further research on water use and aid in structuring policy.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2022.107764","usgsCitation":"Filippelli, S.S., Sloggy, M.R., Vogeler, J.C., Manning, D.T., Goemans, C., and Senay, G.B., 2022, Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region: Agricultural Water Management, v. 271, 107764, 15 p., https://doi.org/10.1016/j.agwat.2022.107764.","productDescription":"107764, 15 p.","ipdsId":"IP-137832","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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,{"id":70232182,"text":"ofr20221050 - 2022 - Implementation plan of the National Cooperative Geologic Mapping Program strategy — Appalachian Piedmont and Blue Ridge Provinces","interactions":[],"lastModifiedDate":"2022-09-27T13:49:52.6265","indexId":"ofr20221050","displayToPublicDate":"2022-06-14T17:20:00","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":"2022-1050","displayTitle":"Implementation Plan of the National Cooperative Geologic Mapping Program Strategy — Appalachian Piedmont and Blue Ridge Provinces","title":"Implementation plan of the National Cooperative Geologic Mapping Program strategy — Appalachian Piedmont and Blue Ridge Provinces","docAbstract":"<p>The National Cooperative Geologic Mapping Program is publishing a strategic plan titled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge.” The plan provides a vision, mission, and goals for the program for the years 2020–30:</p><ul><li>Vision: create an integrated, three-dimensional, digital geologic map of the United States.</li><li>Mission: characterize, interpret, and disseminate a national geologic framework model of the Earth through geologic mapping.</li><li>Goal: focus on geological mapping as a core function of the U.S. Geological Survey within the long-term vision of adequately mapping the Nation’s geologic framework in three dimensions.</li></ul>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221050","usgsCitation":"Merschat, A.J., Carter, M.W., and Piedmont and Blue Ridge Working Group, 2022, Implementation plan of the National Cooperative Geologic Mapping Program strategy — Appalachian 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            -76.9921875,\n              42.8115217450979\n            ],\n            [\n              -78.92578124999999,\n              42.58544425738491\n            ],\n            [\n              -81.73828125,\n              41.47566020027821\n            ],\n            [\n              -82.5732421875,\n              40.78054143186033\n            ],\n            [\n              -82.30957031249999,\n              40.245991504199026\n            ],\n            [\n              -82.8369140625,\n              39.470125122358176\n            ],\n            [\n              -83.84765625,\n              38.89103282648846\n            ],\n            [\n              -84.375,\n              39.36827914916014\n            ],\n            [\n              -85.078125,\n              39.26628442213066\n            ],\n            [\n              -85.5615234375,\n              38.685509760012\n            ],\n            [\n              -86.1328125,\n              39.198205348894795\n            ],\n            [\n              -86.748046875,\n              39.095962936305476\n            ],\n            [\n              -87.62695312499999,\n              38.47939467327645\n            ],\n            [\n              -88.5498046875,\n              38.03078569382294\n            ],\n            [\n              -89.4287109375,\n              37.61423141542417\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/florence-bascom-geoscience-center\" data-mce-href=\"https://www.usgs.gov/centers/florence-bascom-geoscience-center\">Florence Bascom Geoscience Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 21092</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Piedmont and Blue Ridge Provinces</li><li>Scientific Relevance</li><li>Societal Relevance</li><li>Scientific Objectives</li><li>Geologic Mapping Objectives</li><li>Capability Gaps</li><li>Partners</li><li>Anticipated Impacts</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-06-14","noUsgsAuthors":false,"publicationDate":"2022-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":844483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"2018 Piedmont and Blue Ridge Working Group","contributorId":292465,"corporation":true,"usgs":false,"organization":"2018 Piedmont and Blue Ridge Working Group","id":844648,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70255287,"text":"70255287 - 2022 - Bayesian inverse reinforcement learning for collective animal movement","interactions":[],"lastModifiedDate":"2024-06-14T12:14:05.470606","indexId":"70255287","displayToPublicDate":"2022-06-14T07:12:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian inverse reinforcement learning for collective animal movement","docAbstract":"<div><p id=\"ID0EF\" class=\"first\">Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori, and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long-term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the selfpropelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.</p></div>","language":"English","publisher":"Project Euclid","doi":"10.1214/21-AOAS1529","usgsCitation":"Schafer, T.L., Wikle, C., and Hooten, M., 2022, Bayesian inverse reinforcement learning for collective animal movement: Annals of Applied Statistics, v. 16, no. 2, p. 999-1013, https://doi.org/10.1214/21-AOAS1529.","productDescription":"15 p.","startPage":"999","endPage":"1013","ipdsId":"IP-122147","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":447431,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/2009.04003","text":"External Repository"},{"id":430198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schafer, Toryn L. J.","contributorId":339344,"corporation":false,"usgs":false,"family":"Schafer","given":"Toryn","email":"","middleInitial":"L. J.","affiliations":[{"id":81080,"text":"umo","active":true,"usgs":false}],"preferred":false,"id":904102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wikle, Christopher K.","contributorId":339345,"corporation":false,"usgs":false,"family":"Wikle","given":"Christopher K.","affiliations":[{"id":81080,"text":"umo","active":true,"usgs":false}],"preferred":false,"id":904103,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":904101,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70232608,"text":"70232608 - 2022 - Resist, accept, and direct responses to biological invasions: A social–ecological perspective","interactions":[],"lastModifiedDate":"2022-08-02T15:08:13.718872","indexId":"70232608","displayToPublicDate":"2022-06-14T06:38:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Resist, accept, and direct responses to biological invasions: A social–ecological perspective","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Biological invasions represent an important and unique case of ecological transformation that can strongly influence species and entire ecosystems. Challenges in managing invasions arise on multiple fronts, ranging from diverse and often divergent values associated with native and introduced species, logistical constraints, and transformation via other change agents (e.g., climate and land-use change). We address biological invasions considering the Resist-Accept-Direct (RAD) framework for addressing ecological transformation. Because RAD is focused on decisions, we address both social and ecological factors that influence preferences for decision alternatives. We address social factors first as these can constrain the range of alternatives considered in an ecological context. Next, we address ecological dynamics by modeling trajectories from RAD alternatives in a two-species scenario involving impacts of introduced brook trout (<i>Salvelinus fontinalis</i>) on native bull trout (<i>S. confluentus</i>). Results reveal that decision alternatives aligned with each of the major components of RAD can produce positive outcomes. In a management context, these findings highlight the value of investing in early engagement to fully identify decision alternatives, formalizing models of system dynamics to understand ecological trajectories, and applying this knowledge to set the stage for longer term efforts to address biological invasions.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/fme.12574","usgsCitation":"Dunham, J.B., Benjamin, J.R., Lawrence, D.J., and Clifford, K., 2022, Resist, accept, and direct responses to biological invasions: A social–ecological perspective: Fisheries Management and Ecology, v. 29, no. 4, p. 475-485, https://doi.org/10.1111/fme.12574.","productDescription":"11 p.","startPage":"475","endPage":"485","ipdsId":"IP-140669","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447432,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/51896","text":"External Repository"},{"id":403257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":846052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":846053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, David J. 0000-0002-1457-9944","orcid":"https://orcid.org/0000-0002-1457-9944","contributorId":225585,"corporation":false,"usgs":false,"family":"Lawrence","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":41167,"text":"U.S. Park Service","active":true,"usgs":false}],"preferred":false,"id":846054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clifford, Katherine","contributorId":260139,"corporation":false,"usgs":false,"family":"Clifford","given":"Katherine","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":846055,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70262179,"text":"70262179 - 2022 - A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus)","interactions":[],"lastModifiedDate":"2025-01-16T14:35:18.64688","indexId":"70262179","displayToPublicDate":"2022-06-14T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus)","docAbstract":"<p><span>Many turtle species have temperature-dependent sex determination (TSD), raising the prospect that climate change could impact population dynamics by altering sex ratios. Understanding how climate change will affect populations of animals with TSD requires a reliable and minimally invasive method of identifying the sexes of young individuals. This determination is challenging in many turtles, which often lack conspicuous external sexual dimorphism until years after hatching. Here, we explore four alternatives for sexing three age classes of captive-reared young gopher tortoises (</span><i>Gopherus polyphemus</i><span>), a terrestrial turtle of conservation concern native to the southeastern United States: (1) naive testosterone levels, (2) testosterone levels following a follicle stimulating hormone (FSH) challenge, (3) linear morphological measurements, and (4) geometric morphometrics. Unlike some other turtle species, male and female neonatal gopher tortoises have overlapping naive testosterone concentration distributions, justifying more complicated methods. We found that sex of neonates (&lt;7 days old) is best predicted by a “random forest” machine learning model with naive testosterone levels and morphological measurements (8% out-of-bag error). Sex of hatchlings (4–8 months old) was predicted with 11% error using a simple threshold on naive testosterone levels, or with 4% error using a simple threshold on post-FSH testosterone levels. Sex of juveniles (approximately 3.5 years old) was perfectly predicted using a simple threshold on naive testosterone levels. Sexing hatchlings at &gt;4 months of age is the easiest and most reliable non-surgical method for sex identification. Given access to a rearing facility and equipment to perform hormone assays, these methods have the potential to supplant laparoscopic surgery as the method of choice for sexing young gopher tortoises.</span></p>","language":"English","publisher":"PeerJ Inc","doi":"10.7717/peerj.13599","usgsCitation":"Loope, K., Rostal, D., Walden, M., Shoemaker, K., and Hunter, E.A., 2022, A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus): PeerJ, v. 10, e13599, 21 p., https://doi.org/10.7717/peerj.13599.","productDescription":"e13599, 21 p.","ipdsId":"IP-136848","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467180,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13599","text":"Publisher Index Page"},{"id":466447,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Kevin J.","contributorId":348329,"corporation":false,"usgs":false,"family":"Loope","given":"Kevin J.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rostal, David C.","contributorId":348330,"corporation":false,"usgs":false,"family":"Rostal","given":"David C.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walden, Margarete A.","contributorId":348331,"corporation":false,"usgs":false,"family":"Walden","given":"Margarete A.","affiliations":[{"id":83335,"text":"University of Nevada–Reno","active":true,"usgs":false}],"preferred":false,"id":923371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shoemaker, Kevin T.","contributorId":348332,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":83335,"text":"University of Nevada–Reno","active":true,"usgs":false}],"preferred":false,"id":923372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923373,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70232202,"text":"70232202 - 2022 - How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","interactions":[],"lastModifiedDate":"2022-06-14T13:08:00.238472","indexId":"70232202","displayToPublicDate":"2022-06-13T11:00:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","docAbstract":"<p>Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire.</p><p id=\"sp0015\">Accuracy and map agreement varied considerably among the vegetation types and across time and space (r<sup>2</sup><span>&nbsp;</span>ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r<sup>2</sup>) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas &gt;12&nbsp;km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108935","usgsCitation":"Applestein, C., and Germino, M., 2022, How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?: Ecological Indicators, v. 139, 108935, 11 p., https://doi.org/10.1016/j.ecolind.2022.108935.","productDescription":"108935, 11 p.","ipdsId":"IP-138386","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447436,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108935","text":"Publisher Index Page"},{"id":435803,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8G8XM","text":"USGS data release","linkHelpText":"Modelled functional group vegetation cover from 2016 to 2020 on the Soda Wildfire"},{"id":402095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.05883789062501,\n              42.391008609205045\n            ],\n            [\n              -114.730224609375,\n              42.391008609205045\n            ],\n            [\n              -114.730224609375,\n              45.44471679159555\n            ],\n            [\n              -119.05883789062501,\n              45.44471679159555\n            ],\n            [\n              -119.05883789062501,\n              42.391008609205045\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844583,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70232200,"text":"70232200 - 2022 - Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","interactions":[],"lastModifiedDate":"2022-09-15T14:08:12.517171","indexId":"70232200","displayToPublicDate":"2022-06-13T10:49:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","docAbstract":"<p>A robust assessment of the American eel (<i>Anguilla rostrata</i>) stock, required to guide conservation efforts, is challenged by the species’ vast range, high variability in demographic parameters and data inadequacies. Novel ideas and underutilised resources that may assist both analytic assessments and spatially oriented modelling include (1)&nbsp;species and environmental databases; (2) mining of data from scattered sources; (3)&nbsp;infilling of data gaps by spatial analysis; (4) age estimation from measurements of DNA methylation; evaluation of eel abundance by (5) larval, (6) glass-bottom boat, (7)&nbsp;net enclosure and (8) eDNA surveys; (9) accounting for dam-induced habitat increases in eel watercourse modelling; (10) spatially oriented modelling with and without temporal components; (11) geographically nested modelling of glass eel recruitment; (12)&nbsp;spawner per recruit modelling and (13) life cycle modelling to examine larval allocation effects. Eel biologists are too few to gather the required assessment data across all of the species’ range. Public posting of electrofishing and eDNA metabarcoding data sets and the use of machine learning techniques to comprehensively inventory small dams will help meet some data needs. These approaches address only a small proportion of the assessment challenges that face American eels. Worldwide collaboration amongst<span>&nbsp;</span><i>Anguilla</i><span>&nbsp;</span>scientists is a key enabler of progress towards stock assessment goals.</p>","language":"English","publisher":"Wiley","doi":"10.1111/fme.12572","usgsCitation":"Cairns, D., Benchetrit, J., Bernatchez, L., Bornarel, V., Casselman, J., Castonguay, M., Charsley, A., Dorrow, M., Drouineau, H., Frankowski, J., Haro, A., Hoyle, S., Knickle, D.C., Koops, M.A., Poirier, L.A., Thorson, J.T., Young, J.A., and Zhu, X., 2022, Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment: Fisheries Management and Ecology, v. 29, no. 5, p. 516-541, https://doi.org/10.1111/fme.12572.","productDescription":"26 p.","startPage":"516","endPage":"541","ipdsId":"IP-135604","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70232199,"text":"70232199 - 2022 - Quantifying relations between altered hydrology and fish community responses for streams in Minnesota","interactions":[],"lastModifiedDate":"2022-06-13T15:49:06.160966","indexId":"70232199","displayToPublicDate":"2022-06-13T10:45:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1460,"text":"Ecological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying relations between altered hydrology and fish community responses for streams in Minnesota","docAbstract":"Altered hydrology is a stressor on aquatic life for several streams in Minnesota, but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesota.  Best subsets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic explanatory metrics (i.e., duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and six categories of biological response metrics (i.e., composition, habitat, life history, reproductive, tolerance, trophic) computed from fish community samples, as well as fish-based indices of biotic integrity (FIBI) scores and FIBI scores normalized to the an impairment threshold of the corresponding stream class (FIBI_BCG4).  Three hydrologic datasets were used to examine rRelations between altered hydrology and fish community responses were examined at three different temporal scalesusing three hydrologic datasets that represented periods of record, long-term changes, and short-term changes to flow regimes in streams of Minnesota.","language":"English","publisher":"Springer","doi":"10.1186/s13717-022-00383-z","usgsCitation":"Ziegeweid, J.R., Johnson, G.D., Krall, A.L., Fitzpatrick, K., and Levin, S., 2022, Quantifying relations between altered hydrology and fish community responses for streams in Minnesota: Ecological Processes, v. 11, 41, 25 p., https://doi.org/10.1186/s13717-022-00383-z.","productDescription":"41, 25 p.","ipdsId":"IP-125704","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13717-022-00383-z","text":"Publisher Index Page"},{"id":402091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70232196,"text":"70232196 - 2022 - Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","interactions":[],"lastModifiedDate":"2022-06-13T15:44:24.981546","indexId":"70232196","displayToPublicDate":"2022-06-13T10:31:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0090\"><span>Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with&nbsp;harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via&nbsp;remote sensing. Although concentrations of chlorophyll-</span><i>a</i><span>&nbsp;and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics:&nbsp;Spectral Mixture Analysis&nbsp;for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear&nbsp;spectral unmixing&nbsp;models do not account for other optically active constituents such as&nbsp;suspended sediment&nbsp;and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a&nbsp;root mean square error&nbsp;(RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily&nbsp;</span><i>Aphanizomenon</i><span>, and intricate spatial patterns associated with&nbsp;gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified&nbsp;</span><span><i>Microcystis</i></span><span>&nbsp;</span>in Owasco Lake but avoided misclassifying<span>&nbsp;</span><i>Asterionella</i>, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.</p></div></div><div id=\"ab4005\" class=\"abstract graphical\" lang=\"en\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113089","usgsCitation":"Legleiter, C.J., King, T.V., Carpenter, K.D., Hall, N., Mumford, A.C., Slonecker, E.T., Graham, J.L., Stengel, V.G., Simon, N., and Rosen, B.H., 2022, Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data: Remote Sensing of Environment, v. 279, 113089, 19 p., https://doi.org/10.1016/j.rse.2022.113089.","productDescription":"113089, 19 p.","ipdsId":"IP-135126","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":447447,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113089","text":"Publisher Index Page"},{"id":435804,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P928658I","text":"USGS data release","linkHelpText":"SAS: Software Application for SMASH (Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms)"},{"id":402090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Oregon, Texas","city":"Dallas, Forth Worth","otherGeospatial":"Detroit Lake, Finger Lakes, Grapevine Lake, Owasco Lake, Santiam River, Trinity River Basin, Upper Klamath Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.47239685058594,\n              42.75306280054229\n            ],\n            [\n              -76.46106719970703,\n              42.75432327356435\n            ],\n            [\n              -76.45557403564453,\n              42.757852461640255\n            ],\n            [\n              -76.4651870727539,\n              42.76894288709011\n            ],\n            [\n          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Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":844534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Tyler V. 0000-0002-5785-3077","orcid":"https://orcid.org/0000-0002-5785-3077","contributorId":292424,"corporation":false,"usgs":true,"family":"King","given":"Tyler","middleInitial":"V.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carpenter, Kurt D. 0000-0002-6231-8335 kdcar@usgs.gov","orcid":"https://orcid.org/0000-0002-6231-8335","contributorId":127442,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt","email":"kdcar@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hall, Natalie Celeste 0000-0002-6448-162X","orcid":"https://orcid.org/0000-0002-6448-162X","contributorId":245015,"corporation":false,"usgs":true,"family":"Hall","given":"Natalie Celeste","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":844537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mumford, Adam C. 0000-0002-8082-8910 amumford@usgs.gov","orcid":"https://orcid.org/0000-0002-8082-8910","contributorId":171791,"corporation":false,"usgs":true,"family":"Mumford","given":"Adam","email":"amumford@usgs.gov","middleInitial":"C.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":844538,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":844539,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844540,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844541,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Simon, Nancy 0000-0003-2706-7611","orcid":"https://orcid.org/0000-0003-2706-7611","contributorId":202480,"corporation":false,"usgs":true,"family":"Simon","given":"Nancy","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":844542,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosen, Barry H. 0000-0002-8016-3939 brosen@usgs.gov","orcid":"https://orcid.org/0000-0002-8016-3939","contributorId":2844,"corporation":false,"usgs":true,"family":"Rosen","given":"Barry","email":"brosen@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":844543,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70247395,"text":"70247395 - 2022 - The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi","interactions":[],"lastModifiedDate":"2023-08-02T14:13:17.608115","indexId":"70247395","displayToPublicDate":"2022-06-13T08:55:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi","docAbstract":"<p><span>Habitat-suitability indices (HSI) have been employed in restoration to identify optimal sites for planting native species. Often, HSI are based on abiotic variables and do not include biotic interactions, even though similar abiotic conditions can favor both native and nonnative species. Biotic interactions such as competition may be especially important in invader-dominated habitats because invasive species often have fast growth rates and can exploit resources quickly. In this study, we test the utility of an HSI of microtopography derived from airborne LiDAR to predict post-disturbance recovery and native planting success in native shrub-dominated and nonnative, invasive grass-dominated dryland habitats in Hawaiʻi. The HSI uses high-resolution digital terrain models to classify sites' microtopography as high, medium, or low suitability, based on wind exposure and topographic position. We used a split-plot before-after-control-impact design to implement a disturbance experiment within native shrub (</span><i>Dodonaea viscosa</i><span>) and nonnative, invasive grass (</span><i>Cenchrus clandestinus</i><span>)-dominated ecosystems across three microtopography categories. In contrast to previous studies using the same HSI, we found that microtopography was a poor predictor of pre-disturbance conditions for soil nutrients, organic matter content, or foliar C:N, within both&nbsp;</span><i>Dodonaea</i><span>&nbsp;and&nbsp;</span><i>Cenchrus</i><span>&nbsp;vegetation types. In invader-dominated&nbsp;</span><i>Cenchrus</i><span>&nbsp;plots, microtopography helped predict cover, but not as expected (i.e., highest cover would be in high-suitability plots):&nbsp;</span><i>D. viscosa</i><span>&nbsp;had the greatest cover in low-suitability and&nbsp;</span><i>C. clandestinus</i><span>&nbsp;had the greatest cover in medium-suitability plots. Similarly, in native-dominated&nbsp;</span><i>Dodonaea</i><span>&nbsp;plots, microtopography was a poor predictor of&nbsp;</span><i>D. viscosa</i><span>,&nbsp;</span><i>C. clandestinus</i><span>, and total plant cover. Although we found some evidence that microtopography helped inform post-disturbance plant recovery of&nbsp;</span><i>D. viscosa</i><span>&nbsp;and total plant cover, vegetation type was a more important predictor. Important for considering the success of plantings, percent cover of&nbsp;</span><i>D. viscosa</i><span>&nbsp;decreased while percent cover of&nbsp;</span><i>C. clandestinus</i><span>&nbsp;increased within both vegetation types 20 months after disturbance. Our results are evidence that HSIs based on topographic features may prove most useful for choosing planting sites in harsh habitats or those already dominated by native species. In more productive habitats, competition from resident species may offset any benefits gained from “better” suitability sites.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2690","usgsCitation":"Yelenik, S.G., Rose, E., Cordell, S., Victoria, M., and Kellner, J.R., 2022, The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi: Ecological Applications, v. 32, e2690, 18 p., https://doi.org/10.1002/eap.2690.","productDescription":"e2690, 18 p.","ipdsId":"IP-080159","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":447453,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2690","text":"Publisher Index Page"},{"id":419497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Army Pohakuloa Training Area, Keʻamuku maneuver area, Hawaii Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.75116384910783,\n              19.925062000236125\n            ],\n            [\n              -155.7454528828605,\n              19.776779210079724\n            ],\n            [\n              -155.6101203582919,\n              19.781480563216192\n            ],\n            [\n              -155.65866357139447,\n              19.937813032695132\n            ],\n            [\n              -155.75116384910783,\n              19.925062000236125\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":879447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Eli 0000-0003-0958-9491 etrose@usgs.gov","orcid":"https://orcid.org/0000-0003-0958-9491","contributorId":194190,"corporation":false,"usgs":true,"family":"Rose","given":"Eli","email":"etrose@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":879448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cordell, Susan","contributorId":197818,"corporation":false,"usgs":false,"family":"Cordell","given":"Susan","email":"","affiliations":[],"preferred":false,"id":879449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Victoria, Michelle","contributorId":317848,"corporation":false,"usgs":false,"family":"Victoria","given":"Michelle","email":"","affiliations":[{"id":69168,"text":"Pacific Internship Programs for Exploring Science, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":879450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kellner, James R.","contributorId":172034,"corporation":false,"usgs":false,"family":"Kellner","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":879451,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70232415,"text":"70232415 - 2022 - Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019","interactions":[],"lastModifiedDate":"2022-07-01T12:26:23.085105","indexId":"70232415","displayToPublicDate":"2022-06-13T07:24:13","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":"Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019","docAbstract":"<div class=\"article-section__content en main\"><p>Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (<i>p</i>&nbsp;&lt;&nbsp;0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR031399","usgsCitation":"Petrakis, R., Soulard, C.E., Waller, E.K., and Walker, J., 2022, Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019: Water Resources Research, v. 58, no. 6, e2021WR031399, 24 p., https://doi.org/10.1029/2021WR031399.","productDescription":"e2021WR031399, 24 p.","ipdsId":"IP-129527","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447457,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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-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":"58","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric K. 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":203496,"corporation":false,"usgs":true,"family":"Waller","given":"Eric","email":"","middleInitial":"K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":845476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845477,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70234335,"text":"70234335 - 2022 - Response of riparian vegetation to short- and long-term hydrologic variation","interactions":[],"lastModifiedDate":"2022-12-01T16:01:50.778596","indexId":"70234335","displayToPublicDate":"2022-06-13T06:41:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Response of riparian vegetation to short- and long-term hydrologic variation","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Increasing demand for river water now conflicts with an increasing desire to maintain riparian ecosystems. Efficiently managing river flows for riparian vegetation requires an understanding of the time scale of flow effects, but this information is limited by the absence of long-term studies of vegetation change in response to flow variation. To investigate the influence of short- and long-term flow variability and dam operation on riparian vegetation, we determined the occurrence of 107 plant species in 133 permanent plots of known inundating discharge along the Gunnison River in Colorado on five different occasions between 1990 and 2013. Individual species moved up and down the gradient of inundating discharge coincident with increases and decreases in mean annual flow, and the correlations between flow and species occurrence were strongest when flows were weighted by time before vegetation sampling with a median half-life of 1.5 years. Some tall, rhizomatous, perennial species, however, responded to flows on a longer time scale. Logistic regression of species occurrence showed a significant relation with inundation duration for 70 out of 107 species. Plot species richness and total vegetative cover decreased in association with desiccation at low inundation durations and with fluvial disturbance at high inundation durations. Within-plot similarity in species occurrence between years decreased strongly with increasing inundation duration. Moderate inundation durations were dominated by tall, rhizomatous, perennial herbs, including invasive<span>&nbsp;</span><i>Phalaris arundinacea</i><span>&nbsp;</span>(reed canary grass). Over the 23-year study period, species richness declined, and the proportion of rhizomatous perennials increased, consistent with the hypothesis that decreases in flow peaks and increases in low flows caused by flow regulation have decreased establishment opportunities for disturbance-dependent species. In summary, annual-scale changes in vegetation were strongly influenced by flow variation, and decadal-scale changes were influenced by decreases in fluvial disturbance from upstream flow regulation beginning decades prior to the onset of this study.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/eap.2689","usgsCitation":"Friedman, J.M., Eurich, A.M., Auble, G.T., Scott, M., Shafroth, P., and Gibson, P.P., 2022, Response of riparian vegetation to short- and long-term hydrologic variation: Ecological Applications, v. 32, no. 8, e2689, 16 p., https://doi.org/10.1002/eap.2689.","productDescription":"e2689, 16 p.","ipdsId":"IP-128733","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":447464,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2689","text":"Publisher Index Page"},{"id":435807,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91BEXPC","text":"USGS data release","linkHelpText":"Occurrence of plants in plots along the Gunnison River, Colorado, 1990-2017"},{"id":404988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Gunnison River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.677001953125,\n              38.363195134453846\n            ],\n            [\n              -106.8804931640625,\n              38.363195134453846\n            ],\n            [\n              -106.8804931640625,\n              38.56964280859044\n            ],\n            [\n              -107.677001953125,\n              38.56964280859044\n            ],\n            [\n              -107.677001953125,\n              38.363195134453846\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"32","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eurich, Abigail M. 0000-0001-9891-3876","orcid":"https://orcid.org/0000-0001-9891-3876","contributorId":294681,"corporation":false,"usgs":false,"family":"Eurich","given":"Abigail","email":"","middleInitial":"M.","affiliations":[{"id":63625,"text":"Under Contract to USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":848586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Auble, Gregor T. 0000-0002-0843-2751","orcid":"https://orcid.org/0000-0002-0843-2751","contributorId":294682,"corporation":false,"usgs":false,"family":"Auble","given":"Gregor","email":"","middleInitial":"T.","affiliations":[{"id":37421,"text":"Retired U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":848587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Michael L.","contributorId":244803,"corporation":false,"usgs":false,"family":"Scott","given":"Michael L.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":848588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson, Polly P 0000-0002-9751-7895","orcid":"https://orcid.org/0000-0002-9751-7895","contributorId":294683,"corporation":false,"usgs":false,"family":"Gibson","given":"Polly","email":"","middleInitial":"P","affiliations":[{"id":63625,"text":"Under Contract to USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":848590,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70262376,"text":"70262376 - 2022 - The need to step-up monitoring of Asian bears","interactions":[],"lastModifiedDate":"2025-01-24T14:24:19.146882","indexId":"70262376","displayToPublicDate":"2022-06-12T10:13:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The need to step-up monitoring of Asian bears","docAbstract":"<p><span>Many&nbsp;wildlife species&nbsp;are threatened in Asia, including the five species of terrestrial bears (Asiatic black,&nbsp;</span><span><i>Ursus thibetanus</i></span><span>; brown,&nbsp;</span><i>U. arctos</i><span>; sloth,&nbsp;</span><span><i>Melursus ursinus</i></span><span>; sun,&nbsp;</span><span><i>Helarctos malayanus</i></span><span>; giant panda,&nbsp;</span><span><i>Ailuropoda melanoleuca</i></span><span>): many populations of these bears are thought to be declining or imperiled by small&nbsp;population size. Here our aim is to document how population assessments have been conducted for bears in Asia. We searched the literature and identified 102 studies published during 1999–2021 that investigated the status of an Asian bear population; these occurred in 24 of the 32 bear range countries in Asia. At the most basic level, 11% of studies verified presence of bears in places where they were not known to exist. The most common objective (53% of papers) was a distribution map, often derived from presence locations in a habitat-based model. Occupancy studies (15%) used temporal (time stamps on images from cameras) or spatial (transect segments) replicates, but tended to focus on “use”, so detector spacing was sometimes not appropriate for occupancy. Purported population indices, such as sign density or&nbsp;camera trap&nbsp;encounter rates, were reported in 16% of studies. One third of studies provided a population estimate, but only 10 studies in two decades used a rigorous method (e.g., mark–recapture). Sign surveys and interviews were the most common methods for determining bear presence, and local interviews were heavily relied upon for assessing population trend. Camera trapping has become increasingly prevalent, but only one study obtained a population estimate using photographs to distinguish natural individual markings. Only three studies used hair traps to obtain DNA-based population estimates, and three other studies obtained population estimates from DNA in scats. Just three studies quantitatively measured change in population size or occupancy over time, and none of these showed a decline. Unique rangewide sign surveys of giant pandas showed significant geographic expansion. The opinions of experts and local people, now heavily relied upon for population assessments, are not reliable or sensitive enough for monitoring. Quantitative population assessments are desirable to direct conservation actions toward the most perilous situations, and provide a means to gauge the effectiveness of conservation actions. This paper demonstrates the paucity of rigorous monitoring of Asian bears, and leads off a series of papers that propose improved methods for assessing distribution, occupancy, and density</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02087","usgsCitation":"Garshelis, D., Pigeon, K., Hwang, M., Proctor, M., McShea, W., Fuller, A.K., and Morin, D., 2022, The need to step-up monitoring of Asian bears: Global Ecology and Conservation, v. 35, e02087, 13 p., https://doi.org/10.1016/j.gecco.2022.e02087.","productDescription":"e02087, 13 p.","ipdsId":"IP-135711","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481082,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02087","text":"Publisher Index Page"},{"id":481003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Asia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              35.41529707232115,\n              46.71177016110596\n            ],\n            [\n              38.775261932193075,\n              32.68713053487669\n            ],\n            [\n              52.43784583457693,\n              25.45805552202023\n            ],\n            [\n              66.05615094699141,\n              24.425093270056593\n            ],\n            [\n              73.35126800461401,\n              10.197546076498313\n            ],\n            [\n              81.23886207886909,\n              4.4314691000760575\n            ],\n            [\n              91.04270936032952,\n              20.575934862650513\n            ],\n            [\n              94.1901763972345,\n              15.202352382026703\n            ],\n            [\n              97.1384405012642,\n              15.069087431430717\n            ],\n            [\n              94.6771672603644,\n              5.283116189461083\n            ],\n            [\n              102.35786027086067,\n              -7.160654452094249\n            ],\n            [\n              114.53893138055798,\n              -9.948047350337688\n            ],\n            [\n              126.0483612530345,\n              1.5862440839753162\n            ],\n            [\n              110.48966479958608,\n              11.165967994207222\n            ],\n            [\n              109.5109961667568,\n              15.596355224751335\n            ],\n            [\n              123.11341991330539,\n              27.830415332171597\n            ],\n            [\n              117.41469263762411,\n            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Forests","active":true,"usgs":false}],"preferred":false,"id":923978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hwang, Mei-hsiu","contributorId":349068,"corporation":false,"usgs":false,"family":"Hwang","given":"Mei-hsiu","affiliations":[{"id":83423,"text":"National Pingtung University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":923979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Proctor, Michael","contributorId":349069,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShea, William J.","contributorId":349070,"corporation":false,"usgs":false,"family":"McShea","given":"William J.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":923981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morin, Dana J.","contributorId":349071,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":923982,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70232212,"text":"70232212 - 2022 - Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay","interactions":[],"lastModifiedDate":"2022-08-15T13:54:09.728265","indexId":"70232212","displayToPublicDate":"2022-06-12T09:12:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay","docAbstract":"<p><span>Understanding effects of climate change on nitrogen fate and transport in the environment is critical to nutrient management. We used climate projections within a previously calibrated spatially referenced regression (SPARROW) model to predict effects of expected climate change over 1995 through 2025 on total nitrogen fluxes to Chesapeake Bay and in watershed streams. Assuming nitrogen inputs and other watershed conditions remain at 2012 levels, effects of increasing temperature, runoff, streamflow, and stream velocity expected between 1995 and 2025 will include an estimated net 6.5% decline in annual nitrogen delivery to the bay from its watershed. This predicted decline is attributable to declines in the delivery of nitrogen from upland nonpoint sources to streams due to predicted warmer temperatures. Such temperature-driven declines in the delivery of nitrogen to streams more than offset predicted increased delivery to and within streams due to increased runoff and streamflow and may be attributable to increasing rates of denitrification or ammonia volatilization or to changes in plant phenology. Predicted climate-driven declines in nitrogen flux are generally similar across the watershed but vary slightly among major nonpoint source sectors and tributary watersheds. Nitrogen contributions to the bay from point sources are not affected by temperature-driven changes in delivery from uplands and are therefore predicted to increase slightly between 1995 and 2025.</span></p>","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.13017","usgsCitation":"Ator, S., Schwarz, G.E., Sekellick, A.J., and Bhatt, G., 2022, Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay: Journal of the American Water Resources Association, v. 58, no. 4, p. 578-596, https://doi.org/10.1111/1752-1688.13017.","productDescription":"19 p.","startPage":"578","endPage":"596","ipdsId":"IP-125426","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":447466,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13017","text":"Publisher Index 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]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ator, Scott 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":215458,"corporation":false,"usgs":true,"family":"Ator","given":"Scott","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sekellick, Andrew J. 0000-0002-0440-7655","orcid":"https://orcid.org/0000-0002-0440-7655","contributorId":215462,"corporation":false,"usgs":true,"family":"Sekellick","given":"Andrew","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":844664,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70233403,"text":"70233403 - 2022 - Processes and mechanisms of coastal woody-plant mortality","interactions":[],"lastModifiedDate":"2022-09-15T14:17:06.01715","indexId":"70233403","displayToPublicDate":"2022-06-11T08:24:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Processes and mechanisms of coastal woody-plant mortality","docAbstract":"<p><span>Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO</span><sub>2</sub><span>, and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO</span><sub>2</sub><span>, drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16297","usgsCitation":"McDowell, N.G., Ball, M., Bond-Lamberty, B., Kirwan, M.L., Krauss, K., Megonigal, J.P., Mencuccini, M., Ward, N.D., Weintraub, M., and Bailey, V., 2022, Processes and mechanisms of coastal woody-plant mortality: Global Change Biology, v. 28, no. 20, p. 5881-5900, https://doi.org/10.1111/gcb.16297.","productDescription":"20 p.","startPage":"5881","endPage":"5900","ipdsId":"IP-138484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447473,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.16297","text":"External Repository"},{"id":404111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"20","noUsgsAuthors":false,"publicationDate":"2022-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"McDowell, Nate G.","contributorId":207743,"corporation":false,"usgs":false,"family":"McDowell","given":"Nate","email":"","middleInitial":"G.","affiliations":[{"id":37622,"text":"Earth Systems Science Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ball, Marilyn","contributorId":293463,"corporation":false,"usgs":false,"family":"Ball","given":"Marilyn","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":847020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bond-Lamberty, Ben","contributorId":224752,"corporation":false,"usgs":false,"family":"Bond-Lamberty","given":"Ben","email":"","affiliations":[{"id":40935,"text":"Joint Global Research Institute, Maryland","active":true,"usgs":false}],"preferred":false,"id":847021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirwan, Matthew L.","contributorId":191373,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matthew","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":847022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":221936,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":847023,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Megonigal, J. Patrick","contributorId":288317,"corporation":false,"usgs":false,"family":"Megonigal","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":847024,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mencuccini, Maurizio","contributorId":199454,"corporation":false,"usgs":false,"family":"Mencuccini","given":"Maurizio","email":"","affiliations":[],"preferred":false,"id":847025,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ward, Nicholas D.","contributorId":293465,"corporation":false,"usgs":false,"family":"Ward","given":"Nicholas","email":"","middleInitial":"D.","affiliations":[{"id":40277,"text":"U.S. Department of Energy","active":true,"usgs":false}],"preferred":false,"id":847026,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weintraub, Michael N.","contributorId":293467,"corporation":false,"usgs":false,"family":"Weintraub","given":"Michael N.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":847027,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bailey, Vanessa","contributorId":224753,"corporation":false,"usgs":false,"family":"Bailey","given":"Vanessa","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847028,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70246521,"text":"70246521 - 2022 - U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States","interactions":[],"lastModifiedDate":"2023-07-07T12:21:17.568399","indexId":"70246521","displayToPublicDate":"2022-06-10T07:18:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States","docAbstract":"<div id=\"134124394\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>Although the Atlantic continental margin of the eastern United States is an archetypal passive margin, episodes of rejuvenation following continental breakup are increasingly well documented. To better constrain this history of rejuvenation along the southern portion of this continental margin, we present zircon U-Pb (ZUPb) age, zircon fission-track (ZFT) age, apatite U-Pb (AUPb) age, and apatite fission-track (AFT) age and length data from six bedrock samples. The samples were collected along the boundary between the exposed Appalachian hinterland (Piedmont province) and the updip limit of passive margin strata (Coastal Plain province). The samples were collected from central Virginia southward to the South Carolina–Georgia border. ZUPb age distributions are generally consistent with geologic mapping in each of the sample areas. The AUPb data are highly discordant owing to high common-Pb abundances, but for two plutons at the northern and southern ends of the sample area, they define a discordia regression line that indicates substantial Permo-Triassic exhumation-driven cooling. ZFT age distributions are highly dispersed but define central values ranging from Permian to Jurassic. AFT data mostly appear to define a singular underlying cooling age, generally approximately Jurassic or Early Cretaceous. Apatite fission tracks are moderately long (mean lengths in the range of ~13.5 µm), however track lengths for one sample in central North Carolina are shorter (~12.5 µm). To interpret the post-breakup thermal history, we present inverse models of time-temperature history for the five plutonic samples. The models show a history of (1) rapid cooling (&gt;10 °C/m.y.) from deep-crustal to near-surface temperatures by the Triassic, (2) hundreds of degrees of Triassic reheating, (3) Jurassic–Early Cretaceous cooling (at rates of 1–10 °C/m.y.), and (4) slow Late Cretaceous–Cenozoic cooling (~1 °C/m.y.). An additional suite of forward models is presented to further evaluate the magnitude of maximum Triassic reheating at one sample site that is particularly well constrained by thermal maturity data. The model results and geologic reasoning suggest that the inverse models may overestimate Triassic paleotemperatures but that other aspects of the inverse modeling are robust. Overall, this thermal history can be reconciled with several aspects of the lithostratigraphy of distal parts of the continental margin, including the lack of Jurassic–earliest Cretaceous strata beneath the southern Atlantic coastal plain and Cretaceous–Cenozoic grain-size trends.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02447.1","usgsCitation":"Craddock, W.H., O'Sullivan, P., and McAleer, R.J., 2022, U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States: Geosphere, v. 18, no. 4, p. 1330-1353, https://doi.org/10.1130/GES02447.1.","productDescription":"24 p.","startPage":"1330","endPage":"1353","ipdsId":"IP-127916","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447476,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02447.1","text":"Publisher Index Page"},{"id":418744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.06589631767767,\n              40.62761013989578\n            ],\n            [\n              -76.81264623503682,\n              40.32689154960596\n            ],\n            [\n              -79.40430771293059,\n              40.02563596082908\n            ],\n            [\n              -81.51277806782672,\n              38.76941324786962\n            ],\n            [\n              -83.62124842272343,\n              36.33152035458974\n            ],\n            [\n              -84.63155713444466,\n              33.778910284254295\n            ],\n            [\n              -83.84088075135841,\n              32.7874625650833\n            ],\n            [\n              -82.1277485880051,\n              31.4481799351054\n            ],\n            [\n              -80.32676349319762,\n              31.672755039272587\n            ],\n            [\n              -77.51546968666938,\n              33.22949605942023\n            ],\n            [\n              -75.14344053741085,\n              35.02409793894682\n            ],\n            [\n              -73.12282311396812,\n              37.10613979176382\n            ],\n            [\n              -73.47423483978443,\n              39.24678773255735\n            ],\n            [\n              -76.06589631767767,\n              40.62761013989578\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":877037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Sullivan, Paul","contributorId":257903,"corporation":false,"usgs":false,"family":"O'Sullivan","given":"Paul","affiliations":[{"id":51089,"text":"Geosep Services","active":true,"usgs":false}],"preferred":false,"id":877038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":877039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70238498,"text":"70238498 - 2022 - Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models","interactions":[],"lastModifiedDate":"2022-11-28T12:19:06.34769","indexId":"70238498","displayToPublicDate":"2022-06-10T06:15:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Unprecedented conservation efforts for sagebrush (<i>Artemisia</i><span>&nbsp;</span>spp.) ecosystems across the western United States have been catalyzed by risks from escalated wildfire activity that reduces habitat for sagebrush-obligate species such as Greater Sage-Grouse (<i>Centrocercus urophasianus</i>). However, post-fire restoration is challenged by spatial variation in ecosystem processes influencing resilience to disturbance and resistance to non-native invasive species, and spatial and temporal lags between slower sagebrush recovery processes and faster demographic responses of sage-grouse to loss of important habitat. Decision-support frameworks that account for these factors can help users strategically apply restoration efforts by predicting short and long-term ecological benefits of actions. Here, we developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit sage-grouse populations through time. Specifically, we estimated sagebrush recovery following wildfire and risk of non-native annual grass invasion under four scenarios: passive recovery, grazing exclusion, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.</p></div></div>","language":"English","publisher":"Springer Nature","doi":"10.1007/s00267-022-01649-0","usgsCitation":"Roth, C.L., O’Neil, S.T., Coates, P.S., Ricca, M.A., Pyke, D.A., Aldridge, C.L., Heinrichs, J.A., Espinosa, S.P., and Delehanty, D.J., 2022, Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models: Environmental Management, v. 70, p. 288-306, https://doi.org/10.1007/s00267-022-01649-0.","productDescription":"19 p.","startPage":"288","endPage":"306","ipdsId":"IP-123159","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447481,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-022-01649-0","text":"Publisher Index Page"},{"id":435809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96K6X05","text":"USGS data release","linkHelpText":"Sagebrush Restoration Following Fire Disturbance in the Virginia Mountains, Nevada (2018)"},{"id":409662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","noUsgsAuthors":false,"publicationDate":"2022-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Roth, Cali L. 0000-0001-9077-2765 croth@usgs.gov","orcid":"https://orcid.org/0000-0001-9077-2765","contributorId":174422,"corporation":false,"usgs":true,"family":"Roth","given":"Cali","email":"croth@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":857639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857641,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857642,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":857643,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":857644,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":857645,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":857646,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":857647,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70262475,"text":"70262475 - 2022 - The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears","interactions":[],"lastModifiedDate":"2025-01-22T15:30:34.089451","indexId":"70262475","displayToPublicDate":"2022-06-10T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears","docAbstract":"<p><span>Designing a population monitoring program for Asian bears presents challenges associated with their low densities and detectability, generally large home ranges, and logistical or resource constraints. The use of an occupancy-based method to monitor bear populations can be appropriate under certain conditions given the mechanistic relationship between occupancy and abundance. The form of the occupancy–abundance relationship is dependent on species-specific characteristics such as home range size and population density, as well as study area size. To assess the statistical power of tests to detect population change of Asian bears, we conducted a study using a range of scenarios by simulating spatially explicit individual-based capture-recapture data from a demographically open model. Simulations assessed the power to detect changes in population density via changes in site-level occupancy or abundance through time, estimated using a standard occupancy model or a Royle-Nichols model, both with point detectors (representing camera traps). We used IUCN Red List criteria as a guide in selection of two population decline scenarios (20% and 50%), but we chose a shorter time horizon (10 years = 1 bear generation), meaning that declines were steeper than used for IUCN criteria (3 generations). Our simulations detected population declines of 50% with high power (&gt;0.80) and low false positive rates (FPR: incorrectly detecting a decline) (&lt;0.10) when detectors were spaced at &gt;&nbsp;0.67 times the home range diameter (home-range spacing ratio: HRSR, a measure of spatial correlation), such that bears would tend to overlap no more than two detectors. There was high (0.85) correlation between realized occupancy and N in these scenarios. The FPR increased as the HRSR decreased because of spatial correlation in the occupancy process induced when individual home ranges overlap multiple detectors. The mean statistical power to detect more gradual population declines (20% in 10 years) with HRSR &gt;&nbsp;0.67 was low for occupancy models 0.22 (maximum power 0.67) and Royle-Nichols models (0.24; maximum power 0.67), suggesting that declines of this magnitude may not be described reliably with 10 years of monitoring. Our results demonstrated that under many realistic scenarios that we explored, false positive rates were unacceptably high. We highlight that when designing occupancy studies, the spacing between point detectors be at least 0.67 times the diameter of the home range size of the larger sex (e.g., males) when the assumptions of the spatial capture-recapture model used for simulation are met.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02075","usgsCitation":"Fuller, A.K., Augustine, B., Morin, D., Pigeon, K., Boulanger, J., Lee, D., Bisi, F., and Garshelis, D., 2022, The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears: Global Ecology and Conservation, v. 35, e02075, 18 p., https://doi.org/10.1016/j.gecco.2022.e02075.","productDescription":"e02075, 18 p.","ipdsId":"IP-135705","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02075","text":"Publisher Index Page"},{"id":480920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, Ben C.","contributorId":349417,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":924298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morin, Dana J.","contributorId":349419,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":924299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pigeon, Karine","contributorId":349420,"corporation":false,"usgs":false,"family":"Pigeon","given":"Karine","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":924300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boulanger, John","contributorId":349422,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","affiliations":[{"id":83347,"text":"Integrated Ecological Research","active":true,"usgs":false}],"preferred":false,"id":924301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, David C.","contributorId":349424,"corporation":false,"usgs":false,"family":"Lee","given":"David C.","affiliations":[{"id":83348,"text":"University of South Wales","active":true,"usgs":false}],"preferred":false,"id":924302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bisi, Francesco","contributorId":349429,"corporation":false,"usgs":false,"family":"Bisi","given":"Francesco","affiliations":[{"id":83482,"text":"University of Insubria","active":true,"usgs":false}],"preferred":false,"id":924303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garshelis, David L.","contributorId":349431,"corporation":false,"usgs":false,"family":"Garshelis","given":"David L.","affiliations":[{"id":83484,"text":"IUCN SSC Bear Specialist Group.","active":true,"usgs":false}],"preferred":false,"id":924304,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70232247,"text":"70232247 - 2022 - Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland","interactions":[],"lastModifiedDate":"2022-06-17T14:15:00.422295","indexId":"70232247","displayToPublicDate":"2022-06-09T09:05:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland","docAbstract":"<p><span>Nearly all of the wetlands in the coastal zone of Lake Erie have been degraded or destroyed since the 1860s, and most of those that remain are separated from their watersheds by earthen dikes. Hydrologic isolation of these wetlands disrupts ecosystem benefits typical to Great Lakes coastal wetlands, particularly the ability to trap sediments and retain nutrients when inundated by runoff and lake water. High-frequency measurements of turbidity and discharge were taken in 2013 and 2014 to observe turbidity and water flow dynamics to estimate total phosphorus flux of a hydrologically reconnected diked wetland pool in the Crane Creek-Lake Erie wetland complex. Modeled estimates suggest the reconnected pool retained 8% of the total phosphorus loading in 2013 and 10% in 2014, which included short periods of phosphorus export to Lake Erie. Water flowing out of the wetland generally had lower turbidity than inflowing water, but flux in and out of the pool varied seasonally and was linked to changes in lake-levels, seiche dynamics, and weather conditions. More frequent storms, higher winds, and stronger seiches in the spring and fall created turbidity patterns that suggest more phosphorus retention than in summer or winter. Estimates suggest that phosphorus was released during the summer when higher lake levels and the absence of frequent storms, larger short-term seiche oscillations, and potentially soil oxygen availability were driving flux dynamics. This study demonstrated that reestablishing lake hydrology through reconnection of wetland pools can reduce loading and alter timing of delivery of total phosphorus to Lake Erie.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/w14121853","usgsCitation":"Carter, G., Kowalski, K., and Eggleston, M., 2022, Turbidity and estimated phosphorus retention in a reconnected Lake Erie coastal wetland: Water, v. 14, no. 2, 1853, 12 p., https://doi.org/10.3390/w14121853.","productDescription":"1853, 12 p.","ipdsId":"IP-110303","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":447485,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14121853","text":"Publisher Index Page"},{"id":435812,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71V5C3B","text":"USGS data release","linkHelpText":"Total phosphorus and water flux at a restored hydrologic connection at Ottawa National Wildlife Refuge in 2013 and 2014"},{"id":402325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Crane Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -83.24014663696289,\n              41.59772934193236\n            ],\n            [\n              -83.17886352539062,\n              41.59772934193236\n            ],\n            [\n              -83.17886352539062,\n              41.64764694964725\n            ],\n            [\n              -83.24014663696289,\n              41.64764694964725\n            ],\n            [\n              -83.24014663696289,\n              41.59772934193236\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Glenn 0000-0001-6630-7513","orcid":"https://orcid.org/0000-0001-6630-7513","contributorId":292490,"corporation":false,"usgs":false,"family":"Carter","given":"Glenn","email":"","affiliations":[],"preferred":false,"id":844792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":844793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Michael 0000-0003-1068-3290","orcid":"https://orcid.org/0000-0003-1068-3290","contributorId":204833,"corporation":false,"usgs":true,"family":"Eggleston","given":"Michael","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":844794,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70236280,"text":"70236280 - 2022 - Tree rings reveal unmatched 2nd century drought in the Colorado River Basin","interactions":[],"lastModifiedDate":"2022-08-31T14:13:13.360127","indexId":"70236280","displayToPublicDate":"2022-06-09T09:01:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Tree rings reveal unmatched 2nd century drought in the Colorado River Basin","docAbstract":"The ongoing 22 year drought in the Upper Colorado River Basin (UCRB) has been extremely severe, even in the context of the longest available tree-ring reconstruction of annual flow at Lees Ferry, Arizona, dating back to 762 CE. While many southwestern drought assessments have been limited to the past 1200 years, longer paleorecords of moisture variability do exist for the UCRB. Here, gridded drought-atlas data in the UCRB domain along with naturalized streamflow data from the instrumental period (1906–2021) are used in a K nearest neighbor (KNN) nonparametric algorithm to develop a streamflow reconstruction for the Lees Ferry gage starting in 1 CE. The reconstruction reveals a 2nd century drought unmatched in severity by the current drought or by well-documented medieval period droughts in the UCRB. Although data are sparse, analysis of individual long tree ring records and other paleoclimatic data also support the occurrence of an exceptional 2nd century drought.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL098781","usgsCitation":"Gangopadhyay, S., Woodhouse, C., McCabe, G.J., Routson, C.C., and Meko, D., 2022, Tree rings reveal unmatched 2nd century drought in the Colorado River Basin: Geophysical Research Letters, v. 49, no. 11, e2022GL098781, 10 p., https://doi.org/10.1029/2022GL098781.","productDescription":"e2022GL098781, 10 p.","ipdsId":"IP-139459","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":447488,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl098781","text":"Publisher Index Page"},{"id":405995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"upper Colorado River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.03857421875,\n              36.38591277287651\n            ],\n            [\n              -111.181640625,\n              36.58024660149866\n            ],\n            [\n              -110.25878906249999,\n              36.36822190085111\n            ],\n            [\n              -109.9072265625,\n              35.88905007936091\n            ],\n            [\n              -108.8525390625,\n              35.782170703266075\n            ],\n            [\n              -107.51220703125,\n              35.67514743608467\n            ],\n            [\n              -106.74316406249999,\n              36.56260003738545\n            ],\n            [\n              -106.34765625,\n              37.07271048132943\n            ],\n            [\n              -106.5673828125,\n              37.64903402157866\n            ],\n            [\n              -106.12792968749999,\n              38.048091067457236\n            ],\n            [\n              -106.083984375,\n              39.07890809706475\n            ],\n            [\n              -105.0732421875,\n              39.825413103424786\n            ],\n            [\n              -105.35888671875,\n              40.96330795307353\n            ],\n            [\n              -107.11669921875,\n              42.342305278572816\n            ],\n            [\n              -110.32470703125,\n              43.29320031385282\n            ],\n            [\n              -110.6982421875,\n              43.56447158721811\n            ],\n            [\n              -110.91796875,\n              41.75492216766298\n            ],\n            [\n              -111.1376953125,\n              41.393294288784865\n            ],\n            [\n              -111.4013671875,\n              41.09591205639546\n            ],\n            [\n              -111.884765625,\n              39.90973623453719\n            ],\n            [\n              -112.19238281249999,\n              38.66835610151506\n            ],\n            [\n              -112.3681640625,\n              37.21283151445594\n            ],\n            [\n              -112.39013671875,\n              36.65079252503471\n            ],\n            [\n              -112.03857421875,\n              36.38591277287651\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":850421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodhouse, Connie 0000-0003-0545-9753","orcid":"https://orcid.org/0000-0003-0545-9753","contributorId":296028,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":850422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":850423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Routson, Cody C. 0000-0001-8694-7809","orcid":"https://orcid.org/0000-0001-8694-7809","contributorId":187600,"corporation":false,"usgs":false,"family":"Routson","given":"Cody","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":850424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meko, David 0000-0002-5171-2724","orcid":"https://orcid.org/0000-0002-5171-2724","contributorId":296029,"corporation":false,"usgs":false,"family":"Meko","given":"David","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":850425,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
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