Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119343","usgsCitation":"Pascual, A., Giardina, C.P., Selmants, P., Laramee, L.J., and Asner, G.P., 2021, A new remote sensing-based Carbon Sequestration Potential Index (CSPI): A tool to support land carbon management: Forest Ecology and Management, v. 494, 119343, 10 p., https://doi.org/10.1016/j.foreco.2021.119343.","productDescription":"119343, 10 p.","ipdsId":"IP-125868","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":452211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119343","text":"Publisher Index Page"},{"id":386868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.45654296875,\n 18.8335153964335\n ],\n [\n -154.632568359375,\n 19.580493479202527\n ],\n [\n -155.928955078125,\n 20.910134481692683\n ],\n [\n -156.895751953125,\n 21.320080964008206\n ],\n [\n -157.96142578124997,\n 21.790107059807873\n ],\n [\n -159.59838867187497,\n 22.370396344320053\n ],\n [\n -159.9169921875,\n 22.085639901650328\n ],\n [\n -158.192138671875,\n 21.135745255030603\n ],\n [\n -157.005615234375,\n 20.64306554672647\n ],\n [\n -156.478271484375,\n 19.921712747556207\n ],\n [\n -156.07177734375,\n 18.8335153964335\n ],\n [\n -155.45654296875,\n 18.8335153964335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"494","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pascual, Adrian","contributorId":260677,"corporation":false,"usgs":false,"family":"Pascual","given":"Adrian","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":818464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":818465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul C. 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","middleInitial":"C.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laramee, Leah J","contributorId":260678,"corporation":false,"usgs":false,"family":"Laramee","given":"Leah","email":"","middleInitial":"J","affiliations":[{"id":52640,"text":"Dept. of Land and Natural Resources, State of Hawaii","active":true,"usgs":false}],"preferred":false,"id":818467,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":818468,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263925,"text":"70263925 - 2021 - Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","interactions":[],"lastModifiedDate":"2025-02-28T16:13:52.073178","indexId":"70263925","displayToPublicDate":"2021-05-18T10:10:47","publicationYear":"2021","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":"Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","docAbstract":"Earthquake early warning (EEW) systems can quickly identify the beginning of a significant earthquake rupture, but the first seconds of seismic data have not been found to predict the final rupture length. We present two approaches for estimating probabilities of rupture length given the rupture initiation from an EEW system. In the first approach, bends and steps on the fault are interpreted as physical mechanisms for rupture arrest. Arrest probability relations are developed from empirical observations and depend on bend angle and step size. Probability of arrest compounds serially with increasing rupture length as bends or steps are encountered. In the second approach, time‐independent rates among ruptures from the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), are interpreted to apply to the time‐dependent condition in which rupture grows from a known starting point. Length probabilities from a Gutenberg–Richter magnitude–frequency relation provide a reference of comparison. We illustrate the new approach using the discretized fault model for California developed for UCERF3. For the case of rupture initiating on the southeast end of the San Andreas fault we find the geometric complexity of the Mill Creek section impedes most ruptures, and only ∼5% are predicted to reach to San Bernardino on the eastern edge of the greater Los Angeles region. Conditional probabilities of length can be precompiled in this manner for any initiation point on the fault system and thus are of potential value in seismic hazard and EEW applications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200370","usgsCitation":"Biasi, G., and Wesnousky, S.G., 2021, Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning: Bulletin of the Seismological Society of America, v. 111, no. 4, p. 2235-2247, https://doi.org/10.1785/0120200370.","productDescription":"13 p.","startPage":"2235","endPage":"2247","ipdsId":"IP-116890","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wesnousky, Steven G.","contributorId":193416,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":33746,"text":"Center for Neotectonic Studies, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224285,"text":"70224285 - 2021 - Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","interactions":[],"lastModifiedDate":"2021-09-20T12:59:52.651404","indexId":"70224285","displayToPublicDate":"2021-05-18T07:57:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","docAbstract":"Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk-based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current-use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure–activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals—atrazine, N,N-diethyltoluamide, di(2-ethylhexyl)phthalate, dl-menthol, galaxolide, p-tert-octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants—had toxicity quotients >0.1 or EARs for individual chemicals >10–3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10–3. To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165–2182. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
A pilot-scale expanded target assessment of mixtures of inorganic and organic contaminants in point-of-consumption drinking water (tapwater, TW) was conducted in Puerto Rico (PR) to continue to inform TW exposures and corresponding estimations of cumulative human-health risks across the US. In August 2018, a spatial synoptic pilot assessment of than 524 organic, 37 inorganic, and select microbiological contaminant indicators was conducted in 14 locations (7 home; 7 commercial) across PR. A follow-up 3-day temporal assessment of TW variability was conducted in December 2018 at two of the synoptic locations (1 home, 1 commercial) and included daily pre- and post-flush samples. Concentrations of regulated and unregulated TW contaminants were used to calculate cumulative in vitro bioactivity ratios and Hazard Indices (HI) based on existing human-health benchmarks. Synoptic results confirmed that human exposures to inorganic and organic contaminant mixtures, which are rarely monitored together in drinking water at the point of consumption, occurred across PR and consisted of elevated concentrations of inorganic contaminants (e.g., lead, copper), disinfection byproducts (DBP), and to a lesser extent per/polyfluoroalkyl substances (PFAS) and phthalates. Exceedances of human-health benchmarks in every synoptic TW sample support further investigation of the potential cumulative risk to vulnerable populations in PR and emphasize the importance of continued broad characterization of drinking-water exposures at the tap with analytical capabilities that better represent the complexity of both inorganic and organic contaminant mixtures known to occur in ambient source waters. Such health-based monitoring data are essential to support public engagement in source water sustainability and treatment and to inform consumer point-of-use treatment decision making in PR and throughout the US.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.147721","usgsCitation":"Bradley, P., Padilla, I.Y., Romanok, K., Smalling, K., Focazio, M.J., Breitmeyer, S.E., Cardon, M.C., Conley, J.M., Evans, N., Givens, C.E., Gray, J., Gray, L., Hartig, P.C., Hladik, M.L., Higgins, C.P., Iwanowicz, L., Lane, R.F., Loftin, K.A., McCleskey, R., McDonough, C.A., Medlock-Kakaley, E., Meppelink, S.M., Weis, C.P., and Wilson, V.S., 2021, Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA: Environment International, v. 788, 147721, 14 p., https://doi.org/10.1016/j.scitotenv.2021.147721.","productDescription":"147721, 14 p.","ipdsId":"IP-110491","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":452219,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://europepmc.org/pmc/articles/PMC8504685","text":"Publisher Index Page"},{"id":436359,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EQS5CS","text":"USGS data release","linkHelpText":"Target-Chemical Concentration Results of Mixed-Organic/Inorganic Chemical Exposures in Puerto Rico Tapwater, 2017 to 2018"},{"id":385835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3681640625,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 17.727758609852284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"788","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Padilla, Ingrid Y. 0000-0001-8460-1679","orcid":"https://orcid.org/0000-0001-8460-1679","contributorId":258259,"corporation":false,"usgs":false,"family":"Padilla","given":"Ingrid","email":"","middleInitial":"Y.","affiliations":[{"id":52264,"text":"University of Puerto Rico-Mayaguez","active":true,"usgs":false}],"preferred":false,"id":816178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816177,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Focazio, Michael J. 0000-0003-0967-5576 mfocazio@usgs.gov","orcid":"https://orcid.org/0000-0003-0967-5576","contributorId":1276,"corporation":false,"usgs":true,"family":"Focazio","given":"Michael","email":"mfocazio@usgs.gov","middleInitial":"J.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":816179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":37464,"text":"WMA - 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Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":816185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Gray, L. 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The geochronology of this coastal system is based on uranium series, radiocarbon, amino acid racemization (AAR), and optically stimulated luminescence (OSL) methods. We report over 600 mollusk AAR results from 93 sites between northeastern North Carolina and the central New Jersey shelf, representing samples from both onshore cores or outcrops, sub-barrier and offshore cores, and transported shells from barrier island beaches. AAR age estimates are constrained by paired 14C analyses on specific shells and associated U-series coral ages from onshore sites. AAR data from offshore cores are interpreted in the context of detailed seismic stratigraphy. The distribution of Pleistocene-age shells on the island beaches is linked to the distribution of inner shelf or sub-barrier source units. Age mixing over a range of time-scales (~1 ka to ~100 ka) is identified by AAR results from onshore, beach, and shelf collections, often contributing insights into the processes forming individual barrier islands. The regional aminostratigraphic framework identifies a widespread late Pleistocene (Marine Isotope Stage 5) aminozone, with isolated records of middle and early Pleistocene deposition. AAR results provide age estimates for the timing of formation of the three major paleochannels that underlie the Delmarva Peninsula: Persimmon Point paleochannel ≥800 ka; Exmore paleochannel ~400–500 ka (MIS 12); and Eastville paleochannel > 125 ka (MIS 6). The results demonstrate the value of synthesizing abundant AAR chronologic data across various coastal environments, integrating multiple distinct geologic studies. The ages and elevations of the Quaternary units are important for current hypotheses about relative sea-level history and crustal dynamics in the region, which was likely influenced by the Laurentide ice sheet, the margin just ~400 km to the north.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2021.101177","usgsCitation":"Wehmiller, J., Brothers, L.L., Ramsey, K., Foster, D.S., Mattheus, C., Hein, C., and Shawler, J.L., 2021, Molluscan aminostratigraphy of the US Mid-Atlantic Quaternary coastal system: Implications for onshore-offshore correlation, paleochannel and barrier island evolution, and local late Quaternary sea-level history: Quaternary Geochronology, v. 66, 101177, 34 p., https://doi.org/10.1016/j.quageo.2021.101177.","productDescription":"101177, 34 p.","ipdsId":"IP-122893","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":452222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2021.101177","text":"Publisher Index Page"},{"id":386336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.365966796875,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 36.914764288955936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wehmiller, John","contributorId":260088,"corporation":false,"usgs":false,"family":"Wehmiller","given":"John","affiliations":[{"id":52500,"text":"University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Kelvin","contributorId":260089,"corporation":false,"usgs":false,"family":"Ramsey","given":"Kelvin","email":"","affiliations":[{"id":52502,"text":"Geological Survey, University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mattheus, C.R.","contributorId":260090,"corporation":false,"usgs":false,"family":"Mattheus","given":"C.R.","email":"","affiliations":[{"id":52504,"text":"Illinois Geological Survey, DGS","active":true,"usgs":false}],"preferred":false,"id":817219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hein, Christopher","contributorId":214093,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","affiliations":[{"id":18865,"text":"VIMS","active":true,"usgs":false}],"preferred":false,"id":817220,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":817221,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230080,"text":"70230080 - 2021 - Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","interactions":[],"lastModifiedDate":"2022-03-28T11:44:20.391333","indexId":"70230080","displayToPublicDate":"2021-05-18T06:40:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","docAbstract":"An important aspect of volcanic hazard assessment is determination of the level and character of background activity at a volcano so that deviations from background (called unrest) can be identified. Here, we compile the instrumentally recorded eruptive and noneruptive activity for 161 US volcanoes between 1978 and 2020. We combine monitoring data from four techniques: seismicity, ground deformation, degassing, and thermal emissions. To previous work, we add the first comprehensive survey of US volcanoes using medium-spatial resolution satellite thermal observations, newly available field surveys of degassing, and new compilations of seismic and deformation data. We report previously undocumented thermal activity at 30 volcanoes using data from the spaceborne ASTER sensor during 2000–2020. To facilitate comparison of activity levels for all US volcanoes, we assign a numerical classification of the Activity Intensity Level for each monitoring technique, with the highest ranking corresponding to an eruption. There are 96 US volcanoes (59%) with at least one type of detected activity, but this represents a lower bound: For example, there are 12 volcanoes where degassing has been observed but has not yet been quantified. We identify dozens of volcanoes where volcanic activity is only measured by satellite (45% of all thermal observations), and other volcanoes where only ground-based sensors have detected activity (e.g., all seismic and 62% of measured degassing observations). Our compilation provides a baseline against which future measurements can be compared, demonstrates the need for both ground-based and remote observations, and serves as a guide for prioritizing future monitoring efforts.
We used a 12-yr data set of benthic cover (2005–2017), spanning two bleaching events, to assess changes in benthic cover and coral community composition along 21 islands within Gulf of Mannar (GoM), southeast India. Overall, between 2005 and 2017 reefs had a simultaneous decrease in relative coral cover (avg. = − 36%) and increase in algal cover (avg. = + 45%). Changes in benthic cover were not consistent among islands, ranging from − 34 to + 5% for coral cover and from − 0.3 to + 50% for algae. There was a spatial gradient in coral mortality, which increased among islands from west to east. However, there was a disconnect between coral loss and subsequent increases in algae. Algal cover increased more on islands in west GoM where coral loss was minimal. Environmental co-factors (coral cover, percent bleaching, degree heating weeks, fish densities, Chl-a, pollution) explained > 50% of the benthic cover responses to successive bleaching. Coral survival was favored on islands with higher fish densities and chlorophyll-a levels, and increases in algal cover were associated with higher measures of pollution from terrestrial runoff. Coral morphotypes differed in their response following successive bleaching resulting in changes in the relative abundance of different coral morphotypes. Existing climate projections (RCP8.5) indicate a 22-yr gap in the onset of annual severe bleaching (ASB) for reefs in the east versus west GoM, and ASB was ameliorated for all reefs under the RCP4.5 projections. There is limited knowledge of the resilience of GoM reefs, and this study identifies coral morphotypes and reefs that are most likely to recover or decline from successive bleaching, in the context of forecasts of the frequency of future bleaching events in GoM.
Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.
Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. We present a framework to map and monitor two deciduous cover types, upland and riparian, occupying a small fraction of an expansive, mountainous landscape in north-central Wyoming. Initially, we developed broad-scale predictions of predominant woody vegetation types by integrating Landsat data into species distribution models and combining subsequent outputs into a synthesis map. Then, we evaluated a 35-year Landsat time series (1985–2019) using the Mann-Kendall test to identify significant trends in the condition of upland and riparian deciduous vegetation and assessed the rate and direction of change using the Theil-Sen estimator. Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition. The synthesis map had an overall correct classification rate of 87% and field data indicated deciduous vegetation within 45 m of coniferous forest faces increased pressure of conifer expansion. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12 years followed by a 9-year browning trend, before switching back to a greening trend for the last 13 years of the study. Our results indicate trends are driven by the climate of the measurement period at the landscape scale. Although we did not find conclusive evidence to establish a strong link between browse pressure and satellite data, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. This framework is transferable to other understudied riparian environments throughout western North America to provide insight on ecohydrological processes and assess global and local stressors across broad spatiotemporal scales.
Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researchers may report that a disease was detected or not detected within geopolitical units. Often, the spatial aggregation process obscures the values of response variables, spatial covariates, and locations of each individual, which makes recovering individual-level inference difficult. We show that applying a series of transformations, including a change of support, to a bivariate point process model allows researchers to recover individual-level inference for spatial covariates from spatially aggregated binary data. The series of transformations preserves the convenient interpretation of desirable binary regression models that are commonly applied to individual-level data. Using a simulation experiment, we compare the performance of our proposed method under varying types of spatial aggregation against the performance of standard approaches using the original individual-level data. We illustrate our method by modeling individual-level probability of infection using a data set that has been aggregated to protect an at-risk and endangered species of bats. Our simulation experiment and data illustration demonstrate the utility of the proposed method when access to original non-aggregated data is impractical or prohibited.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2021.100514","usgsCitation":"Walker, N., Hefley, T.J., Ballmann, A., Russell, R., and Walsh, D.P., 2021, Recovering individual-level spatial inference from aggregated binary data: Spatial Statistics, v. 44, 100514, 14 p.; Data release, https://doi.org/10.1016/j.spasta.2021.100514.","productDescription":"100514, 14 p.; Data release","ipdsId":"IP-118748","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":452237,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://arxiv.org/abs/2004.12013","text":"Publisher Index Page"},{"id":386279,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418318,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUPDIB","text":"USGS data release","description":"USGS data release","linkHelpText":"Pseudogymnoascus destructans detections by US county (2008-2012)"}],"country":"United States","otherGeospatial":"Northeast and Midwest United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.20703125,\n 36.87962060502676\n ],\n [\n -66.26953125,\n 36.87962060502676\n ],\n [\n -66.26953125,\n 49.15296965617042\n ],\n [\n -97.20703125,\n 49.15296965617042\n ],\n [\n -97.20703125,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Nelson","contributorId":259320,"corporation":false,"usgs":false,"family":"Walker","given":"Nelson","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":817117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":817118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220612,"text":"70220612 - 2021 - Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","interactions":[],"lastModifiedDate":"2021-12-10T16:26:48.54244","indexId":"70220612","displayToPublicDate":"2021-05-17T06:49:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","docAbstract":"Recent studies have shown the oxygen isotopic composition (δ18O) of modern terrestrial gastropod shells is determined largely by the δ18O of precipitation. This implies that fossil shells could be used to reconstruct the δ18O of paleo-precipitation as long as the isotopic system, including the hydrologic pathways of the local watershed and the gastropod systematics, is well understood. In this study, we measured the δ18O values of 456 individual gastropod shells collected from paleowetland deposits in the San Pedro Valley, Arizona that range in age from ca. 29.1 to 9.8 ka. Isotopic differences of up to 2‰ were identified among the four taxa analyzed (Succineidae, Pupilla hebes, Gastrocopta tappaniana, and Vallonia gracilicosta), with Succineidae shells yielding the highest values and V. gracilicosta shells exhibiting the lowest values. We used these data to construct a composite isotopic record that incorporates these taxonomic offsets, and found shell δ18O values increased by ~4‰ between the last glacial maximum and early Holocene, which is similar to the magnitude, direction, and rate of isotopic change recorded by speleothems in the region. These results suggest the terrestrial gastropods analyzed here may be used as a proxy for past climate in a manner that is complementary to speleothems, but potentially with much greater spatial coverage.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2021.18","usgsCitation":"Rech, J.A., Pigati, J.S., Springer, K.B., Bosch, S., Nekola, J.C., and Yanes, Y., 2021, Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest: Quaternary Research, v. 104, p. 43-53, https://doi.org/10.1017/qua.2021.18.","productDescription":"11 p.","startPage":"43","endPage":"53","onlineOnly":"N","ipdsId":"IP-122769","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":436362,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EISWFZ","text":"USGS data release","linkHelpText":"Data release for Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest"},{"id":385834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1904296875,\n 42.032974332441405\n ],\n [\n -119.92675781249999,\n 39.16414104768742\n ],\n [\n -114.9169921875,\n 35.35321610123823\n ],\n [\n -114.9609375,\n 32.731840896865684\n ],\n [\n -111.005859375,\n 31.240985378021307\n ],\n [\n -108.19335937499999,\n 31.466153715024294\n ],\n [\n -108.1494140625,\n 31.80289258670676\n ],\n [\n -106.34765625,\n 31.728167146023935\n ],\n [\n -103.1396484375,\n 31.952162238024975\n ],\n [\n -103.095703125,\n 36.94989178681327\n ],\n [\n -102.041015625,\n 36.98500309285596\n ],\n [\n -102.0849609375,\n 41.0130657870063\n ],\n [\n -110.9619140625,\n 40.91351257612758\n ],\n [\n -110.9619140625,\n 42.00032514831621\n ],\n [\n -120.1904296875,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"104","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rech, Jason A.","contributorId":117323,"corporation":false,"usgs":false,"family":"Rech","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bosch, Stephanie","contributorId":258260,"corporation":false,"usgs":false,"family":"Bosch","given":"Stephanie","email":"","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":816202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nekola, Jeffrey C.","contributorId":26214,"corporation":false,"usgs":false,"family":"Nekola","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":816203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yanes, Yurena","contributorId":197219,"corporation":false,"usgs":false,"family":"Yanes","given":"Yurena","email":"","affiliations":[],"preferred":false,"id":816204,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229404,"text":"70229404 - 2021 - Moose habitat selection and fitness consequences during two critical winter tick life stages in Vermont, United States","interactions":[],"lastModifiedDate":"2022-03-07T12:54:30.254398","indexId":"70229404","displayToPublicDate":"2021-05-17T06:41:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Moose habitat selection and fitness consequences during two critical winter tick life stages in Vermont, United States","docAbstract":"The moose (Alces alces) is a charismatic species in decline across much of their southern distribution in North America. In the northeastern United States, much of the reduction has been attributed to winter tick (Dermacentor albipictus) infestations. Winter ticks are fairly immobile throughout all life stages, and therefore their distribution patterns at any given time are shaped largely by the occurrence of moose across the landscape during the peak of two critical time periods: fall questing (when ticks latch onto moose) and spring drop-off (when engorged female ticks detach from moose). We used recent land cover and lidar data within a dynamic occupancy modeling framework to estimate first-order habitat selection (use vs. non-use) of female moose (n = 74) during the tick questing and drop-off periods. Patch extinction and colonization rates between the fall questing and spring drop-off periods were strongly influenced by habitat and elevation, but these effects were diminished during the fall questing period when moose were more active across the landscape. From the fall questing period to the spring drop-off period, patches where colonization was high and extinction was low had higher proportions of young (shrub/forage) mixed forest at higher elevations. Further, we evaluated the fitness consequences of habitat selection by adult females during the fall questing period, when females and their calves acquire ticks. We compared Resource Selection Functions (RSF) for five females that successfully reared a calf to age 1 with five females whose calves perished due to ticks. Adult female moose whose offspring perished selected habitats in the fall that spatially coincided with areas of high occupancy probability during the spring tick drop-off period. In contrast, adult female moose whose offspring survived selected areas where the probability of occupancy during the spring drop-off was low; at present, natural selection may favor female adults who do not select the same habitats in fall as in spring. Our model coefficients and mapped results define “hotspots” that are likely encouraging the deleterious effects of the tick-moose cycle. These findings fill knowledge gaps about moose habitat selection that may improve the effectiveness of management aimed at reversing declining population trends.
Dissolved calcium concentration [Ca2+] is thought to be a major factor limiting the establishment and thus the spread of invasive bivalves such as zebra (Dreissena polymorpha) and quagga (Dreissena bugensis) mussels. We measured [Ca2+] in 168 water samples collected along ~100 river-km of the lower Columbia River, USA, between June 2018 and March 2020. We found [Ca2+] to range from 13 to 18 mg L−1 during summer/fall and 5 to 22 mg L−1 during the winter/spring. Previous research indicates that [Ca2+] < 12 mg L−1 are likely to limit the establishment and spread of invasive bivalves. Thus, our results indicate that there is sufficient Ca2+ in most locations in the lower Columbia River to support the establishment of invasive dreissenid mussels, which could join the already widespread and abundant Asian clam (Corbicula fluminea) as the newest invader to an already heavily invaded Columbia River ecosystem. These new data provide important measurements from a heretofore undersampled region of the Columbia River and have important implications for the spread of invasive bivalves and, by extension, the conservation and management of native species and ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3804","usgsCitation":"Bollens, S.M., Harrison, J., Kramer, M.G., Rollwagen-Bollens, G., Counihan, T., Robb-Chavez, S.B., and Nolan, S.T., 2021, Calcium concentrations in the lower Columbia River, USA, are generally sufficient to support invasive bivalve spread: River Research and Applications, v. 37, no. 6, p. 889-894, https://doi.org/10.1002/rra.3804.","productDescription":"6 p.","startPage":"889","endPage":"894","ipdsId":"IP-126009","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":387455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington, Oregon","otherGeospatial":"southern Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.96972656249999,\n 45.336701909968134\n ],\n [\n -117.8173828125,\n 45.336701909968134\n ],\n [\n -117.8173828125,\n 46.9502622421856\n ],\n [\n -123.96972656249999,\n 46.9502622421856\n ],\n [\n -123.96972656249999,\n 45.336701909968134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bollens, Stephen M. 0000-0001-9214-9037","orcid":"https://orcid.org/0000-0001-9214-9037","contributorId":148958,"corporation":false,"usgs":false,"family":"Bollens","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":819945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, John A.","contributorId":261389,"corporation":false,"usgs":false,"family":"Harrison","given":"John A.","affiliations":[{"id":52831,"text":"Washington State University - Vancouver, School of the Environment","active":true,"usgs":false}],"preferred":false,"id":819946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kramer, Marc G.","contributorId":261390,"corporation":false,"usgs":false,"family":"Kramer","given":"Marc","email":"","middleInitial":"G.","affiliations":[{"id":52831,"text":"Washington State University - Vancouver, School of the Environment","active":true,"usgs":false}],"preferred":false,"id":819947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rollwagen-Bollens, Gretchen","contributorId":190162,"corporation":false,"usgs":false,"family":"Rollwagen-Bollens","given":"Gretchen","email":"","affiliations":[],"preferred":false,"id":819948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":819949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robb-Chavez, Salvador B.","contributorId":261391,"corporation":false,"usgs":false,"family":"Robb-Chavez","given":"Salvador","email":"","middleInitial":"B.","affiliations":[{"id":52831,"text":"Washington State University - Vancouver, School of the Environment","active":true,"usgs":false}],"preferred":false,"id":819950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nolan, Sean T.","contributorId":261392,"corporation":false,"usgs":false,"family":"Nolan","given":"Sean","email":"","middleInitial":"T.","affiliations":[{"id":52831,"text":"Washington State University - Vancouver, School of the Environment","active":true,"usgs":false}],"preferred":false,"id":819951,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227731,"text":"70227731 - 2021 - The effect of group size on reproduction in cooperatively breeding gray wolves depends on density","interactions":[],"lastModifiedDate":"2022-01-27T12:42:29.807496","indexId":"70227731","displayToPublicDate":"2021-05-16T06:39:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The effect of group size on reproduction in cooperatively breeding gray wolves depends on density","docAbstract":"In cooperatively breeding species, large group size is often positively related to reproductive success and group persistence. We have a poor understanding, however, of how group sizes within a population affect reproduction particularly as density varies. We hypothesized that at low densities, wolves in both small and large groups would have similar reproductive rates. At high densities, however, wolves in small groups would have lower reproductive rates compared to those in large groups. Using empirical data from radio-collared wolves in Idaho and Yellowstone National Park, WY, USA (1996–2012), we compared reproductive rates (i.e. proportion reproducing, litter size, pup survival) among small and large groups of wolves as density fluctuated within the populations. Reproductive rates were generally lower for individuals in small groups compared to those in large groups, particularly as density increased. Pup survival, however, was slightly higher for wolves in small groups compared to large groups except at very high densities. Polygamy increased with density regardless of group size, suggesting a polygamy threshold for wolves. Large group size resulted in less parturition failure, more breeding females per group, larger litter sizes, and ultimately more pups recruited per group. Large group size appears advantageous for several, but not all, aspects of reproduction particularly when population density is high.
Disease shapes community composition by removing species with strong interactions. To test whether the absence of keystone predation due to disease produced changes to the species composition of rocky intertidal communities, we leverage a natural experiment involving mass mortality of the keystone predator Pisaster ochraceus from Sea Star Wasting Syndrome. Over four years, we measured dimensions of mussel beds, sizes of Mytilus californianus, mussel recruitment, and species composition on vertical rock walls at six rocky intertidal sites on the central California coast. We also assessed the relationship between changes in mussel cover and changes in sea star density across 33 sites along the North American Pacific coast using data from long-term monitoring. After four years, the lower boundary of the central California mussel beds shifted downward toward the water 18.7 ± 15.8 cm (SD) on the rock and 11.7 ± 11.0 cm in elevation, while the upper boundary remained unchanged. In central California, downward expansion and total area of the mussel bed were positively correlated with mussel recruitment but were not correlated with pre-disease sea star density or biomass. At a multi-region scale, changes in mussel percent cover were positively correlated with pre-disease sea star densities but not change in densities. Species composition of primary substrate holders and epibionts below the mussel bed remained similar across years. Extirpation of the community below the bed did not occur. Instead, this community became limited to a smaller spatial extent while the mussel bed expanded.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marenvres.2021.105363","usgsCitation":"Moritsch, M.M., 2021, Expansion of intertidal mussel beds following disease-driven reduction of a keystone predator: Marine Environmental Research, v. 169, 105363, 10 p., https://doi.org/10.1016/j.marenvres.2021.105363.","productDescription":"105363, 10 p.","ipdsId":"IP-126316","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":452258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marenvres.2021.105363","text":"Publisher Index Page"},{"id":385892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"California, Oregon","otherGeospatial":"British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -129.990234375,\n 50.064191736659104\n ],\n [\n -125.33203125,\n 47.754097979680026\n ],\n [\n -120.76171875,\n 49.095452162534826\n ],\n [\n -128.232421875,\n 55.677584411089526\n ],\n [\n -134.912109375,\n 55.27911529201561\n ],\n [\n -129.990234375,\n 50.064191736659104\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.15625000000001,\n 42.09822241118974\n ],\n [\n -123.31054687499999,\n 42.09822241118974\n ],\n [\n -123.31054687499999,\n 46.49839225859763\n ],\n [\n -125.15625000000001,\n 46.49839225859763\n ],\n [\n -125.15625000000001,\n 42.09822241118974\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3984375,\n 41.83682786072714\n ],\n [\n -125.15625000000001,\n 41.83682786072714\n ],\n [\n -125.5078125,\n 39.977120098439634\n ],\n [\n -122.87109375,\n 35.67514743608467\n ],\n [\n -118.65234374999999,\n 32.32427558887655\n ],\n [\n -116.27929687499999,\n 32.39851580247402\n ],\n [\n -116.71874999999999,\n 34.08906131584994\n ],\n [\n -121.9921875,\n 37.78808138412046\n ],\n [\n -123.3984375,\n 41.83682786072714\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"169","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":816354,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220504,"text":"70220504 - 2021 - Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","interactions":[],"lastModifiedDate":"2025-05-13T16:07:15.741437","indexId":"70220504","displayToPublicDate":"2021-05-14T07:25:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8601,"text":"Estuarine, Coastal, and Shelf Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","title":"Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","docAbstract":"Grand Bay estuary in coastal Mississippi and Alabama (USA) has undergone significant geomorphic changes over the last few centuries as a result of anthropogenic (bridge, road, and hardened shoreline construction) and climatic (extreme storm events) processes, which reduce freshwater input, sediment supply, and degrade barrier islands. To investigate how geomorphic changes may have altered the Grand Bay estuary, sediment push cores were collected for foraminiferal, sedimentological (organic matter content, grain-size distribution), and radiochemical (210Pb,137Cs, and 7Be) analyses. Clay normalized geochronologies were determined with a constant rate of supply model. Based on downcore age-depth relationships, select intervals were analyzed for foraminifera in order to assess alterations in the microfossil assemblage in Grand Bay estuary over the 20th Century. All estuarine samples were low diversity (species richness: 1–10; Fisher's alpha diversity: 0.14–1.75); two species, Ammotium salsum and Paratrochammina simplissima, dominated all downcore assemblages. Paratrochammina simplissima increased in abundance up-core from a minor subsidiary species (median = 4.7% at 19–20 cm) to dominant or co-dominant with A. salsum over the 20th and early 21st Centuries in six cores, comprising up to 60.7% of a single sample. The emerging dominance of P. simplissima since ~1950 along with the reduction of brackish-estuarine taxa and introduction of calcareous species signifies increased salinity and less marsh organic matter preserved in the sediments. While seasonal dissolution limits our ability to chronologically constrain the introduction of calcareous species, P. simplissima, a species not referenced in taxonomic data from the northern Gulf of Mexico until 2012, is well constrained, following its first occurrence in the 1930s.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2021.107312","usgsCitation":"Ellis, A.M., and Smith, C., 2021, Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes: Estuarine, Coastal, and Shelf Science, v. 255, 107312, 15 p., https://doi.org/10.1016/j.ecss.2021.107312.","productDescription":"107312, 15 p.","ipdsId":"IP-123715","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":385701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.52484130859375,\n 29.99062347853047\n ],\n [\n -88.363037109375,\n 29.99062347853047\n ],\n [\n -88.363037109375,\n 30.38709188778112\n ],\n [\n -89.52484130859375,\n 30.38709188778112\n ],\n [\n -89.52484130859375,\n 29.99062347853047\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"255","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ellis, Alisha M. 0000-0002-1785-020X aellis@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-020X","contributorId":192957,"corporation":false,"usgs":true,"family":"Ellis","given":"Alisha","email":"aellis@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815846,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231206,"text":"70231206 - 2021 - The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US","interactions":[],"lastModifiedDate":"2022-05-03T11:58:19.866725","indexId":"70231206","displayToPublicDate":"2021-05-14T06:51:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US","docAbstract":"The U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous U.S. NSHM includes significant changes to the underlying ground motion models (GMMs), most of which are necessary to enable the new multi-period response spectra (MPRS) requirements of seismic design regulations that use hazard results for 22 spectral periods and eight site classes. This article focuses on the GMMs used in the western United States (WUS) and is a companion to a recent article on the GMMs used in the central and eastern United States (CEUS). In the WUS, for crustal and subduction earthquakes, two models used in previous versions of the NSHM are excluded to provide consistency over all considered periods and site classes. To more accurately estimate ground motions at long periods in the vicinity of Los Angeles, San Francisco, Salt Lake City, and Seattle, the 2018 NSHM incorporates deep sedimentary basin depth from local seismic velocity models. The subduction GMMs considered lack basin depth terms and are modified to include an additional scale factor to account for this. This article documents the WUS GMMs used in the 2018 NSHM update and provides detail on the changes to GMM medians, aleatory variability, epistemic uncertainty, and site-effect models. It compares each of these components with those considered in prior NSHMs and discusses their total effect on hazard.
Transcriptomic data has demonstrated utility to advance the study of physiological diversity and organisms’ responses to environmental stressors. However, a lack of genomic resources and challenges associated with collecting high-quality RNA can limit its application for many wild populations. Minimally invasive blood sampling combined with de novo transcriptomic approaches has great potential to alleviate these barriers. Here, we advance these goals for marine turtles by generating high quality de novo blood transcriptome assemblies to characterize functional diversity and compare global transcriptional profiles between tissues, species, and foraging aggregations.
We generated high quality blood transcriptome assemblies for hawksbill (Eretmochelys imbricata), loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) turtles. The functional diversity in assembled blood transcriptomes was comparable to those from more traditionally sampled tissues. A total of 31.3% of orthogroups identified were present in all four species, representing a core set of conserved genes expressed in blood and shared across marine turtle species. We observed strong species-specific expression of these genes, as well as distinct transcriptomic profiles between green turtle foraging aggregations that inhabit areas of greater or lesser anthropogenic disturbance.
Obtaining global gene expression data through non-lethal, minimally invasive sampling can greatly expand the applications of RNA-sequencing in protected long-lived species such as marine turtles. The distinct differences in gene expression signatures between species and foraging aggregations provide insight into the functional genomics underlying the diversity in this ancient vertebrate lineage. The transcriptomic resources generated here can be used in further studies examining the evolutionary ecology and anthropogenic impacts on marine turtles.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s12864-021-07656-5","usgsCitation":"Banjeree, S.M., Adkins Stoll, J., Allen, C.D., Lynch, J., Harris, H.S., Kenyon, L., Connon, R.E., Sterling, E.J., Naro-Maciel, E., McFadden, K., Lamont, M., Benge, J., Fernandez, N.B., Seminoff, J.A., Benson, S., Lewison, R.L., Eguchi, T., Summers, T.M., Hapdei, J.R., Rice, M.R., Martin, S., Jones, T., Dutton, P., Balazs, G., and Komoroske, L.M., 2021, Species and population specific gene expression in blood transcriptomes of marine turtles: BMC Genomics, v. 22, 346, 16 p., https://doi.org/10.1186/s12864-021-07656-5.","productDescription":"346, 16 p.","ipdsId":"IP-122805","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452271,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12864-021-07656-5","text":"Publisher Index Page"},{"id":396706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationDate":"2021-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Banjeree, Shreya M.","contributorId":287647,"corporation":false,"usgs":false,"family":"Banjeree","given":"Shreya","email":"","middleInitial":"M.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":836978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adkins Stoll, Jamie","contributorId":287648,"corporation":false,"usgs":false,"family":"Adkins Stoll","given":"Jamie","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":836979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Camryn D.","contributorId":287649,"corporation":false,"usgs":false,"family":"Allen","given":"Camryn","email":"","middleInitial":"D.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":836980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynch, Jennifer M.","contributorId":270074,"corporation":false,"usgs":false,"family":"Lynch","given":"Jennifer M.","affiliations":[{"id":47720,"text":"NIST","active":true,"usgs":false}],"preferred":false,"id":836981,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, Heather S.","contributorId":220297,"corporation":false,"usgs":false,"family":"Harris","given":"Heather","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":836982,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kenyon, Lauren","contributorId":287650,"corporation":false,"usgs":false,"family":"Kenyon","given":"Lauren","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":836983,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Connon, Richard E.","contributorId":287651,"corporation":false,"usgs":false,"family":"Connon","given":"Richard","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":836984,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sterling, Eleanor J.","contributorId":145439,"corporation":false,"usgs":false,"family":"Sterling","given":"Eleanor","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":836985,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Naro-Maciel, Eugenia","contributorId":138902,"corporation":false,"usgs":false,"family":"Naro-Maciel","given":"Eugenia","email":"","affiliations":[{"id":12576,"text":"College of Staten Island, Staten Island, New York","active":true,"usgs":false}],"preferred":false,"id":836986,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McFadden, Kathryn","contributorId":287652,"corporation":false,"usgs":false,"family":"McFadden","given":"Kathryn","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":836987,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":836988,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Benge, James","contributorId":287653,"corporation":false,"usgs":false,"family":"Benge","given":"James","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":836989,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Fernandez, Nadia B.","contributorId":175100,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nadia","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":836990,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Seminoff, Jeffrey A.","contributorId":209798,"corporation":false,"usgs":false,"family":"Seminoff","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[{"id":37993,"text":"NOAA, National Marine Fisheries Service, Southwest Fisheries Science Center, La Jolla, CA, USA 92037","active":true,"usgs":false}],"preferred":false,"id":836991,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Benson, Scott R.","contributorId":287658,"corporation":false,"usgs":false,"family":"Benson","given":"Scott R.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":836992,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lewison, Rebecca L.","contributorId":194537,"corporation":false,"usgs":false,"family":"Lewison","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":836993,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Eguchi, Tomoharu","contributorId":167037,"corporation":false,"usgs":false,"family":"Eguchi","given":"Tomoharu","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":836994,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Summers, Tammy M.","contributorId":150150,"corporation":false,"usgs":false,"family":"Summers","given":"Tammy","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":836995,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Hapdei, Jessy R.","contributorId":150151,"corporation":false,"usgs":false,"family":"Hapdei","given":"Jessy","email":"","middleInitial":"R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":836996,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Rice, Marc R.","contributorId":287664,"corporation":false,"usgs":false,"family":"Rice","given":"Marc","email":"","middleInitial":"R.","affiliations":[{"id":61622,"text":"Hawai’i Preparatory Academy","active":true,"usgs":false}],"preferred":false,"id":836997,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Martin, Summer","contributorId":287666,"corporation":false,"usgs":false,"family":"Martin","given":"Summer","email":"","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":836998,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Jones, T. Todd","contributorId":270072,"corporation":false,"usgs":false,"family":"Jones","given":"T. Todd","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":836999,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Dutton, Peter H.","contributorId":256741,"corporation":false,"usgs":false,"family":"Dutton","given":"Peter H.","affiliations":[{"id":51846,"text":"NOAA Fisheries, Southwest Fisheries Science Center, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":837000,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Balazs, George H.","contributorId":270071,"corporation":false,"usgs":false,"family":"Balazs","given":"George H.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":837001,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Komoroske, Lisa M.","contributorId":287670,"corporation":false,"usgs":false,"family":"Komoroske","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":837002,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70230618,"text":"70230618 - 2021 - Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning","interactions":[],"lastModifiedDate":"2022-04-19T14:58:45.389206","indexId":"70230618","displayToPublicDate":"2021-05-13T09:53:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7751,"text":"AGU Advances","active":true,"publicationSubtype":{"id":10}},"title":"Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning","docAbstract":"Exotic annual grasses (EAG) are one of the most damaging agents of change in western North America. Despite known socio-environmental effects of EAG there remains a need to enhance monitoring capabilities for better informing conservation and management practices. Here, we integrate field observations, remote sensing and climate data with machine-learning techniques to estimate and assess patterns of historical (1985–2019; R2 = 0.86 ± 0.05; MAE = 6.7 ± 1.4%), present (2020), and future (2025–2040) EAG abundance (30-m) across much of the western United States. Trend analysis revealed that ∼8% and 1% of the landscape experienced significant rises and declines in historical EAG cover, respectively, with hotspots of invasion generally occurring near roads and along low-to-mid elevation gradients with warmer and drier conditions. Accurate simulations of the response of EAG to changing environmental conditions, disturbances and management treatments indicate that ecosystem resistance to invasion is largely controlled by long-term EAG abundance (surrogate for seed bank), time since and frequency of wildfire, and plant community interactions. Ecological thresholds associated with enhanced probabilities of wildfire occurrence and invasion rates indicate that relatively little (10%) EAG cover is needed to heighten these risks. Climate change is expected to push 8% of the landscape across invasion thresholds by 2040, impacting 6% of existing sage-grouse habitat, and we identify where fuel breaks may be placed to reduce wildfire risks and invasion. Spatially detailed, timely, and accurate depictions of past, present, and future EAG abundance are vital for the protection of life and property and the continued stewardship of sagebrush ecosystems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020AV000298","usgsCitation":"Pastick, N., Wylie, B., Rigge, M.B., Dahal, D., Boyte, S., Jones, M.O., Allred, B.W., Parajuli, S., and Wu, Z., 2021, Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning: AGU Advances, v. 2, no. 2, e2020AV000298, 22 p., https://doi.org/10.1029/2020AV000298.","productDescription":"e2020AV000298, 22 p.","ipdsId":"IP-121974","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452276,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020av000298","text":"Publisher Index Page"},{"id":436365,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZN7BN8","text":"USGS data release","linkHelpText":"Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019)"},{"id":436364,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z85VET","text":"USGS data release","linkHelpText":"Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040)"},{"id":399087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Idaho, Nevada, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.150390625,\n 41.376808565702355\n ],\n [\n -103.88671875,\n 44.96479793033101\n ],\n [\n -110.74218749999999,\n 44.902577996288876\n ],\n [\n -112.8515625,\n 44.465151013519616\n ],\n [\n -114.2578125,\n 45.644768217751924\n ],\n [\n 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Franke College of Forestry and Conservation University of Montana, Missoula","active":true,"usgs":false}],"preferred":false,"id":840915,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":222684,"corporation":false,"usgs":true,"family":"Parajuli","given":"Sujan","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":840916,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":840917,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70220443,"text":"70220443 - 2021 - Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species","interactions":[],"lastModifiedDate":"2021-08-03T16:11:08.337078","indexId":"70220443","displayToPublicDate":"2021-05-13T08:05:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species","docAbstract":"Ecosystem-based management relies on understanding how perturbations influence ecosystem structure and function (e.g., invasive species, exploitation, abiotic changes). However, data on unimpacted systems are scarce; therefore, we often rely on impacted systems to make inferences about ‘natural states.’ Among the Laurentian Great Lakes, Lake Superior provides a unique case study to address non-native species impacts because the food web is dominated by native species. Additionally, Lake Superior is both vertically (benthic versus pelagic) and horizontally (nearshore versus offshore) structured by depth, providing an opportunity to compare the function of these sub-food webs. We developed an updated Lake Superior EcoPath model using data from the 2005/2006 lake-wide multi-agency surveys covering multiple trophic levels. We then compared trophic transfer efficiency (TTE) to previously published EcoPath models. Finally, we compared ecosystem function of the 2005/2006 ecosystem to that with non-native linkages removed and compared native versus non-native species-specific approximations of TTE and trophic flow. Lake Superior was relatively efficient (TTE = 0.14) compared to systems reported in a global review (average TTE = 0.09), and the microbial loop was highly efficient (TTE > 0.20). Non-native species represented a very small proportion (<0.01%) of total biomass and were generally more efficient and had higher trophic flow compared to native species. Our results provide valuable insight into the importance of the microbial loop and represent a baseline estimate of non-native species impacts on Lake Superior. Finally, this work is a starting point for further model development to predict future changes in the Lake Superior ecosystem.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.010","usgsCitation":"Mathias, B.G., Hrabik, T.R., Hoffman, J.C., Gorman, O., Seider, M., Sierszen, M.E., Vinson, M., Yule, D.L., and Yurista, P.M., 2021, Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species: Journal of Great Lakes Research, v. 47, no. 4, p. 1146-1158, https://doi.org/10.1016/j.jglr.2021.04.010.","productDescription":"13 p.","startPage":"1146","endPage":"1158","ipdsId":"IP-115192","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452278,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9067395","text":"External Repository"},{"id":436366,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W93YXH","text":"USGS data release","linkHelpText":"Compilation of Data for Parameterization of an Ecopath Model of Lake Superior at the Beginning of the 21st Century (2001-2016)"},{"id":385642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.63671874999997,\n 46.195042108660154\n ],\n [\n -83.84765624999997,\n 46.195042108660154\n ],\n [\n -83.84765624999997,\n 49.83798245308484\n ],\n [\n -92.63671874999997,\n 49.83798245308484\n ],\n [\n -92.63671874999997,\n 46.195042108660154\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mathias, Bryan G.","contributorId":240743,"corporation":false,"usgs":false,"family":"Mathias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":815547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrabik, Thomas R.","contributorId":35614,"corporation":false,"usgs":false,"family":"Hrabik","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":6915,"text":"University of Minnesota - 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2021 - Biogeography and ecology of Ostracoda in the U.S. northern Bering, Chukchi, and Beaufort Seas","interactions":[],"lastModifiedDate":"2021-05-17T12:47:37.844807","indexId":"70220492","displayToPublicDate":"2021-05-13T07:39:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography and ecology of Ostracoda in the U.S. northern Bering, Chukchi, and Beaufort Seas","docAbstract":"Ostracoda (bivalved Crustacea) comprise a significant part of the benthic meiofauna in the Pacific-Arctic region, including more than 50 species, many with identifiable ecological tolerances. These species hold potential as useful indicators of past and future ecosystem changes. In this study, we examined benthic ostracodes from nearly 300 surface sediment samples, >34,000 specimens, from three regions—the northern Bering, Chukchi and Beaufort Seas—to establish species’ ecology and distribution. Samples were collected during various sampling programs from 1970 through 2018 on the continental shelves at 20 to ~100m water depth. Ordination analyses using species’ relative frequencies identified six species, Normanicythere leioderma, Sarsicytheridea bradii, Paracyprideis pseudopunctillata, Semicytherura complanata, Schizocythere ikeyai, and Munseyella mananensis, as having diagnostic habitat ranges in bottom water temperatures, salinities, sediment substrates and/or food sources. Species relative abundances and distributions can be used to infer past bottom environmental conditions in sediment archives for paleo-reconstructions and to characterize potential changes in Pacific-Arctic ecosystems in future sampling studies. Statistical analyses further showed ostracode assemblages grouped by the summer water masses influencing the area. Offshore-to-nearshore transects of samples across different water masses showed that complex water mass characteristics, such as bottom temperature, productivity, as well as sediment texture, influenced the relative frequencies of ostracode species over small spatial scales. On the larger biogeographic scale, synoptic ordination analyses showed dominant species—N. leioderma (Bering Sea), P. pseudopunctillata (offshore Chukchi and Beaufort Seas), and S. bradii (all regions)—remained fairly constant over recent decades. However, during 2013–2018, northern Pacific species M. mananensis and S. ikeyai increased in abundance by small but significant proportions in the Chukchi Sea region compared to earlier years. It is yet unclear if these assemblage changes signify a meiofaunal response to changing water mass properties and if this trend will continue in the future. Our new ecological data on ostracode species and biogeography suggest these hypotheses can be tested with future benthic monitoring efforts.
Cyclic climatic and glacial fluctuations of the Late Quaternary produced a dynamic biogeographic history for high latitudes. To refine our understanding of this history in northwestern North America, we explored geographic structure in a wide-ranging carnivore, the wolverine (Gulo gulo). We examined genetic variation in populations across mainland Alaska, coastal Southeast Alaska, and mainland western Canada using nuclear microsatellite genotypes and sequence data from the mitochondrial DNA (mtDNA) control region and Cytochrome b (Cytb) gene. Data from maternally inherited mtDNA reflect stable populations in Northwest Alaska, suggesting the region harbored wolverine populations since at least the Last Glacial Maximum (LGM; 21 Kya), consistent with their persistence in the fossil record of Beringia. Populations in Southeast Alaska are characterized by minimal divergence, with no genetic signature of long-term refugial persistence (consistent with the lack of pre-Holocene fossil records there). The Kenai Peninsula population exhibits mixed signatures depending on marker type: mtDNA data indicate stability (i.e., historical persistence) and include a private haplotype, whereas biparentally inherited microsatellites exhibit relatively low variation and a lack of private alleles consistent with a more recent Holocene colonization of the peninsula. Our genetic work is largely consistent with the early 20th century taxonomic hypothesis that wolverines on the Kenai Peninsula belong to a distinct subspecies. Our finding of significant genetic differentiation of wolverines inhabiting the Kenai Peninsula, coupled with the peninsula’s burgeoning human population and the wolverine’s known sensitivity to anthropogenic impacts, provides valuable foundational data that can be used to inform conservation and management prescriptions for wolverines inhabiting these landscapes.
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ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":814570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jung, Thomas S","contributorId":257552,"corporation":false,"usgs":false,"family":"Jung","given":"Thomas","email":"","middleInitial":"S","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":814571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Magoun, Audrey J","contributorId":257553,"corporation":false,"usgs":false,"family":"Magoun","given":"Audrey J","affiliations":[{"id":7058,"text":"Alaska Department of Fish and 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Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range","docAbstract":"Invasive species are a global threat to ecosystem biodiversity and function; non-native grass invasion has been particularly problematic in sparsely vegetated ecosystems such as open dunes. Native plant population responses to invasion, however, are infrequently translated to landscape scales, limiting the effectiveness of these data for addressing conservation issues. We quantified population density, total population size, and age class distribution of the federally-endangered plant species Antioch Dunes evening primrose (Oenothera deltoides subsp. howellii), at sites along a non-native grass invasion gradient in California, USA. We then scaled relationships between invasion and plant density across the species’ range using spatial models and remote sensing data. Adult and juvenile O. deltoides subsp. howellii densities were more than 10 times higher in non-invaded areas (grids with 10% total plant cover) when compared to highly-invaded areas (grids with 80% total plant cover). The ratio of O. deltoides subsp. howellii juveniles to adults decreased to less than 1 at 54% total cover, highlighting sensitivity of the regeneration niche to invasion. Spatial models mapped hotspots of O. deltoides subsp. howellii abundance and population structure across the landscape at sub-meter scales. Scaling the impacts of increasing invasion on plant species of conservation concern holds promise when coupled with remote sensing approaches, especially in naturally low-cover ecosystems where readily available metrics (e.g., Normalized Difference Vegetation Index) can be used to quantify invasion. These spatial models inform how future invasive species management may influence population size and spatial distribution of species of conservation concern.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-021-02516-5","usgsCitation":"Jones, S., Kennedy, A., Freeman, C.M., and Thorne, K., 2021, Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range: Biological Invasions, v. 23, p. 2451-2471, https://doi.org/10.1007/s10530-021-02516-5.","productDescription":"21 p.","startPage":"2451","endPage":"2471","ipdsId":"IP-126563","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436368,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PRVA0M","text":"USGS data release","linkHelpText":"Antioch Dunes evening primrose (Oenothera deltoides subsp. howellii) juvenile and adult abundance across the known range, California, USA (2019)"},{"id":386030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Antioch","otherGeospatial":"San Francisco Bay-Delta region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.92729949951172,\n 37.998597644907385\n ],\n [\n -121.6691207885742,\n 37.998597644907385\n ],\n [\n -121.6691207885742,\n 38.089174937729794\n ],\n [\n -121.92729949951172,\n 38.089174937729794\n ],\n [\n -121.92729949951172,\n 37.998597644907385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2021-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennedy, Anna 0000-0002-6530-7498","orcid":"https://orcid.org/0000-0002-6530-7498","contributorId":259164,"corporation":false,"usgs":true,"family":"Kennedy","given":"Anna","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Chase M. 0000-0003-4211-6709 cfreeman@usgs.gov","orcid":"https://orcid.org/0000-0003-4211-6709","contributorId":150052,"corporation":false,"usgs":true,"family":"Freeman","given":"Chase","email":"cfreeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816669,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221566,"text":"70221566 - 2021 - Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program","interactions":[],"lastModifiedDate":"2021-06-23T12:34:24.043043","indexId":"70221566","displayToPublicDate":"2021-05-12T07:10:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program","title":"Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program","docAbstract":"Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13101895","usgsCitation":"Brooks, C.N., Weinstein, C.B., Poley, A.F., Grimm, A.G., Marion, N.P., Bourgeau-Chavez, L., Hansen, D., and Kowalski, K., 2021, Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program: Remote Sensing, v. 13, no. 10, 1895, 21 p., https://doi.org/10.3390/rs13101895.","productDescription":"1895, 21 p.","ipdsId":"IP-124814","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452292,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13101895","text":"Publisher Index Page"},{"id":436371,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91NICZD","text":"USGS data release","linkHelpText":"Land cover classifications and associated data from treatment areas enrolled in the Phragmites 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B.","contributorId":260518,"corporation":false,"usgs":false,"family":"Weinstein","given":"Charlotte","email":"","middleInitial":"B.","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":818069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poley, Andrew F.","contributorId":260520,"corporation":false,"usgs":false,"family":"Poley","given":"Andrew","email":"","middleInitial":"F.","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":818070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grimm, Amanda G.","contributorId":150482,"corporation":false,"usgs":false,"family":"Grimm","given":"Amanda","email":"","middleInitial":"G.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":818071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marion, 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