{"pageNumber":"101","pageRowStart":"2500","pageSize":"25","recordCount":40783,"records":[{"id":70259400,"text":"70259400 - 2023 - A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework","interactions":[],"lastModifiedDate":"2024-10-07T15:11:26.345198","indexId":"70259400","displayToPublicDate":"2023-12-01T10:02:12","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework","docAbstract":"

This document serves to fulfill the Coregonine Threats Assessment Science Team’s charge of providing a written recommendation for a methodology to conduct threats assessments for Great Lakes coregonines within the Coregonine Restoration Framework (CRF). Through a series of team meetings that included presentations by experts on five candidate threats assessment frameworks followed by structured deliberations, we came to consensus to recommend the threats assessment framework used by Fisheries and Oceans Canada under Canada’s Species at Risk Act, with three modifications: (1) a conceptual modeling step, (2) the use of a “point spreading” approach to incorporate uncertainty when scoring threats, and (3) the use of a modified Delphi or “estimate-talk-estimate” approach when scoring key elements in the assessment. We recommend that this approach be applied to the spatial units delineated by the CRF Resolve Taxonomy and Gap Analysis science teams. In brief, the assessment process includes providing background information on the spatial unit and threats under assessment, constructing a conceptual model linking threats to key processes and vital rates, and scoring or ranking threats across six elements: likelihood of occurrence, level of impact, strength of evidence, unit-level threat occurrence, unit-level threat frequency, and unit-level threat extent. We provide detailed instructions for completing each step of the assessment and generating associated results, with particular attention paid to our suggested modifications.

The Coregonine Threats Assessment Science Team also conducted two test runs to assess the applicability and effectiveness of our recommended framework for Great Lakes coregonine populations and their threats. We conducted these test runs on two examples of Great Lakes coregonines that represented two extremes of data availability, as well as two different management contexts. We chose Kiyi (Coregonus kiyi) in Lake Ontario as an example of a data-poor, extirpated population, and we chose Cisco (Coregonus artedi) in Lake Superior as an example of a data-rich, extant population. We provide the results of these test runs in Appendices 1-2. We also describe the lessons we learned from these test runs throughout this document and highlighted them in the “Recommendations for avoiding challenges during application” section.

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Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225

","tableOfContents":"","publishedDate":"2023-12-01","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sweetkind, Donald S. 0000-0003-0892-4796","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":210808,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":889030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zellman, Kristine L. 0000-0002-7088-429X kzellman@usgs.gov","orcid":"https://orcid.org/0000-0002-7088-429X","contributorId":4849,"corporation":false,"usgs":true,"family":"Zellman","given":"Kristine","email":"kzellman@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":889031,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271309,"text":"70271309 - 2023 - Hidden system identification: Basin modeling as a tool for examining sedimentary geothermal resource potential","interactions":[],"lastModifiedDate":"2025-09-08T14:58:24.487775","indexId":"70271309","displayToPublicDate":"2023-12-01T09:50:13","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Hidden system identification: Basin modeling as a tool for examining sedimentary geothermal resource potential","docAbstract":"

Three-dimensional (3D) geologic and temperature models have been developed for the onshore U.S. Gulf Coast. The results from these models identify areas of moderate- to high-temperature (90°-150°C and >150°C; respectively) geothermal resources at depths <6 km. This modeling study addresses the fundamental challenge of predicting where opportune temperature and lithology coincide. Unlike traditional geothermal systems with surface expressions of hydrothermal circulation (e.g., hot springs, fumaroles, sinter), sedimentary geothermal systems (SGS) are generally hidden. Historically, simplified efforts to predict subsurface temperatures in sedimentary basins have focused on linear temperature extrapolation that does not consider the variable thermal properties of different lithologies or lithologic changes with depth (e.g., compaction, lithification). Therefore, the need to understand basin architecture and predict temperatures in 3D within SGS is paramount to identifying geothermal resources and determining economic feasibility. Basin modeling software has long been used to characterize the subsurface conditions of sedimentary basins, including temperature, in the pursuit of finding hydrocarbons. This tool can also be adapted to evaluate the potential of geothermal resources in a sedimentary basin by predicting the confluence of desirable temperatures and reservoir lithologies. In this work, PetroMod basin modeling software was used to create a regional geologic model of the onshore U.S. Gulf Coast, covering over 500,000 km2 calibrated to temperature data from wells. Inputs include structural surfaces from commercial databases, lithology information derived from published literature, and corrected bottom-hole temperatures (BHT) from over 6,000 wells. The resulting 3D geologic model can be used to predict temperatures throughout the basin. Maps were exported showing the depth, depositional unit, and reservoir lithology at which temperatures of 90°C and 150°C were reached, revealing over 400,000 km2 of moderate- to high-temperature resources at depths <6 km. These maps function as a first-order screening tool to identify areas where low-, moderate-, or high-grade resource potential may exist, based on temperature and if optimal reservoir lithologies or depositional units of interest are present. Depending on the success criteria of a project, the same maps can be exported for any isotherm or incorporate other 1407 Gardner and Birdwell subsurface properties. The methodology employed in this work can be applied in any sedimentary basin with available subsurface data. Further calibration incorporating other data, including pressure and porosity, can expand the utility of basin modeling for geothermal evaluations. Basin modeling is a powerful but underutilized tool for identifying prospective geothermal resources in sedimentary basins.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geothermal Rising Conference","conferenceDate":"October 1-3, 2023","conferenceLocation":"Reno, NV","language":"English","publisher":"Geothermal Rising","usgsCitation":"Gardner, R., and Birdwell, J.E., 2023, Hidden system identification: Basin modeling as a tool for examining sedimentary geothermal resource potential, in Using the Earth to save the Earth, v. 47, Reno, NV, October 1-3, 2023, p. 1407-1414.","productDescription":"8 p.","startPage":"1407","endPage":"1414","ipdsId":"IP-155363","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":495157,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034802","linkFileType":{"id":5,"text":"html"}},{"id":495216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, Rand 0000-0001-8711-5334","orcid":"https://orcid.org/0000-0001-8711-5334","contributorId":316831,"corporation":false,"usgs":true,"family":"Gardner","given":"Rand","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257118,"text":"70257118 - 2023 - USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling","interactions":[],"lastModifiedDate":"2024-08-12T14:44:46.763484","indexId":"70257118","displayToPublicDate":"2023-12-01T09:41:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling","docAbstract":"

No abstract available.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Monitoring and Response Plan","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee (invasivecarp.us)","usgsCitation":"Brey, M.K., Jackson, P.R., Stanton, J.C., and Fritts, A.K., 2023, USGS Telemetry Project: Real-Time Telemetry and Multi-State Modeling, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-151295","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":432459,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}},{"id":432485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261284,"text":"70261284 - 2023 - Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan","interactions":[],"lastModifiedDate":"2024-12-04T15:39:20.098701","indexId":"70261284","displayToPublicDate":"2023-12-01T09:33:40","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan","docAbstract":"

The Great Lakes Tectonic Zone (GLTZ) forms the boundary between the Wawa-Abitibi subprovince (north side) and Minnesota River Valley subprovince (south side) within the Archean Superior Province. The GLTZ is concealed for all of its 1100 km length, except south of Marquette in the central Upper Peninsula of Michigan (Sims, 1991; Sims and Day, 1993). Near KI Sawyer, it is exposed as a NW-striking, 2.3 km wide mylonite zone along a strike length of about 11 km, with a mylonitic foliation that dips steeply to the SW (Sims, 1993). The location extent of the GLTZ is unknown to the east where it is concealed beneath Paleozoic sedimentary rocks. We use legacy aeromagnetic data (Daniels et al., 2009) in combination with modern aeromagnetic data (Drenth and Brown, 2020) and ground gravity data to geophysically characterize the GLTZ and map its eastward extent under cover and map additional nearby covered Precambrian tectonic elements.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Institute on Lake Superior Geology proceedings, 69th annual meeting, Eau Claire, Wisconsin, part 1 - Abstracts and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Drenth, B.J., and Cannon, W.F., 2023, Geophysical mapping of the Great Lakes Tectonic Zone and surrounding Precambrian geology in the central Upper Peninsula, Michigan, in Institute on Lake Superior Geology proceedings, 69th annual meeting, Eau Claire, Wisconsin, part 1 - Abstracts and proceedings, p. 27-28.","productDescription":"2 p.","startPage":"27","endPage":"28","ipdsId":"IP-151526","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":464752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464741,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcollections.lakeheadu.ca/exhibits/show/ilsg/item/8207"}],"country":"United States","state":"Michigan","otherGeospatial":"Upper Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6,\n 46.5\n ],\n [\n -87.6,\n 46\n ],\n [\n -86.5,\n 46\n ],\n [\n -86.5,\n 46.5\n ],\n [\n -87.6,\n 46.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":920217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":920218,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264392,"text":"70264392 - 2023 - A management-focused population viability analysis for North Atlantic right whales","interactions":[],"lastModifiedDate":"2025-03-14T14:18:14.086582","indexId":"70264392","displayToPublicDate":"2023-12-01T09:10:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NMFS-NEFSC 307","title":"A management-focused population viability analysis for North Atlantic right whales","docAbstract":"

The North Atlantic right whale (Eubalaena glacialis) is among the most endangered whale species in the world and has been in decline since 2010. Considerable effort is directed toward its recovery by striving to remove threats. In this report, we describe the development of a population viability analysis for right whales that is designed to assess the current status, evaluate the contributions of various threats, and explore the management interventions needed to achieve recovery. The individual-based model that underlies this analysis accounts for age- and stagespecific survival and reproductive rates, the effects of severe injury from entanglement or vessel strike, and future changes in prey availability and accessibility. Several new or updated empirical analyses supplied parameter estimates, and parametric uncertainty was carefully incorporated into the model results.

We find that under the status quo conditions of 2019, prior to the enactment of new regulations by the U.S. and Canada after 2020, the North Atlantic right whale population would be expected to continue to fall, with a median decline of 75% in 100 years (95% projection interval, –98% to +9% change) and a probability of falling below 50 proven females of 0.934 in 100 years. If the recently enacted regulations reduce entanglement risk by 25%, however, the population would be expected to decrease by 42% over 100 years (95% projection interval –92% to +154% change), with a risk of falling below 50 proven females in 100 years of 0.705. If, instead, the recently enacted regulations reduce entanglement risk by 50%, the population would be expected to increase by 52% in 100 years (95% projection interval –83% to +497% change), with a probability of falling below 50 proven females of 0.349.

Of the 3 primary threats explored in this analysis, the risk of entanglement contributes the most to the long-term risk of quasi-extinction, followed closely by the risk of vessel strike, and much more distantly by a decrease in prey availability. In hypothetical scenarios that fully remove one threat at a time, removal of the entanglement threat alone reduces the probability of falling below 50 proven females in 100 years from 0.934 to 0.053; removal of the vessel strike threat alone reduces it to 0.343; and a return to higher prey conditions, but with both human-related threats still in place, reduces it to 0.875.

We explored a wide range of management intervention scenarios that changed the rate of entanglement risk (e.g., endline reductions, closures, implementation of ropeless/on-demand gear); the effect of entanglement (through use of weak rope technology); the rate of vessel traffic increase over time; and the severity of vessel strike risk through speed restrictions. We found, for example, that reducing entanglement risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.705; reducing vessel strike risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.846; but the combination of reducing both entanglement risk and vessel strike risk by 25% reduces the risk of quasi-extinction to 0.528.

This model and the results it produced are meant to represent an assessment of the current status of North Atlantic right whales using the best available scientific and commercial data and state-of-the-art analytical tools. Our knowledge of the future of the right whale population, however, has limitations. We have endeavored to fully incorporate uncertainty into this model, but there are many areas for continued improvement. We view this model as a living tool that can be improved, adapted, and extended as new data, new methods, and new questions arise.

","language":"English","publisher":"National Oceanic and Atmospheric Administration","doi":"10.25923/dqp2-2r71","usgsCitation":"Runge, M.C., Linden, D., Hostetler, J.A., Borggaard, D., Garrison, L.P., Knowlton, A., Lesage, V., Williams, R., and Pace, R., 2023, A management-focused population viability analysis for North Atlantic right whales: NOAA Technical Memorandum NMFS-NEFSC 307, 93 p., https://doi.org/10.25923/dqp2-2r71.","productDescription":"93 p.","ipdsId":"IP-144310","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linden, Daniel W.","contributorId":229525,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":930630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":930631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borggaard, Diane L","contributorId":352275,"corporation":false,"usgs":false,"family":"Borggaard","given":"Diane L","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrison, Lance P.","contributorId":296893,"corporation":false,"usgs":false,"family":"Garrison","given":"Lance","email":"","middleInitial":"P.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":930633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowlton, Amy R.","contributorId":352046,"corporation":false,"usgs":false,"family":"Knowlton","given":"Amy R.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":930634,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lesage, Véronique","contributorId":352276,"corporation":false,"usgs":false,"family":"Lesage","given":"Véronique","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":930635,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams, Robert A. 0000-0002-2973-8493","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":203802,"corporation":false,"usgs":false,"family":"Williams","given":"Robert A.","affiliations":[{"id":36721,"text":"USGS-Emeritus","active":true,"usgs":false}],"preferred":false,"id":930636,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pace, Richard M III","contributorId":352277,"corporation":false,"usgs":false,"family":"Pace","given":"Richard M","suffix":"III","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930637,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267462,"text":"70267462 - 2023 - Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria","interactions":[],"lastModifiedDate":"2025-05-23T14:09:15.876291","indexId":"70267462","displayToPublicDate":"2023-12-01T09:08:54","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria","docAbstract":"Identifying and analyzing closed depressions in karst areas is important for sinkhole hazard evaluation and land management. We created a sinkhole inventory in the karst region of northwest Puerto Rico using a lidar-derived elevation model acquired in 2018 approximately eleven months after Hurricane Maria. The goal of this project is to develop a geodatabase of sinkhole feature classes (polygons and points), relevant geometric attributes of each feature, and a density raster to portray areas of greater clustering of sinkholes as an input for future sinkhole susceptibility assessment. We used ArcGIS Pro® v3.0 to create closed depression polygons using two semi-automated extraction methods. A fill-difference method was used to capture depressions nine square meters and larger, and a contour tree method was used to capture nested depressions larger than one hundred square meters. Quality checks were conducted to eliminate non-karst depressions, such as human-made depressions and those resulting as artifacts from the automated methods. Geospatial data of land cover, soils, and geology helped to refine the results and improve quality control. The most challenging aspect of this effort was determining a true karst sinkhole from other depressions extracted from the lidar-derived elevation model. Limitations of this semi-automated method include false-positive depressions in the automated results and the exclusion of sinkholes in conducting large-scale eliminations based on landscape attributes. We approached this challenge by combining layers of other geospatial information to evaluate the type of process that could result in a closed depression. This project will help develop an efficient method to visualize karst hazards utilizing lidar-derived elevation models and sinkhole geomorphic expressions. The resulting geodatabase can be used to efficiently identify sinkhole susceptibility and support land management decision-making in karst areas.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th multidisciplinary conference on sinkholes and the engineering and environmental impacts of karst","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"National Cave and Karst Research Institute","usgsCitation":"Smith, L., Doctor, D.H., and Cox, C., 2023, Mapping closed depressions in the karst region of northwest Puerto Rico using lidar-derived elevation data obtained in 2018 after Hurricane Maria, in Proceedings of the 17th multidisciplinary conference on sinkholes and the engineering and environmental impacts of karst, v. 17, p. 239-248.","productDescription":"10 p.","startPage":"239","endPage":"248","ipdsId":"IP-148086","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":486487,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcommons.usf.edu/sinkhole_2022/ProceedingswithProgram/"},{"id":486498,"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 \"coordinates\": [\n [\n [\n -67.30291697689199,\n 18.559923243339426\n ],\n [\n -67.30291697689199,\n 18.330982046375794\n ],\n [\n -66.86489408952299,\n 18.228131112250153\n ],\n [\n -66.11901997545601,\n 18.330982046375794\n ],\n [\n -66.11901997545601,\n 18.559923243339426\n ],\n [\n -67.30291697689199,\n 18.559923243339426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Lillian G.","contributorId":355868,"corporation":false,"usgs":false,"family":"Smith","given":"Lillian G.","affiliations":[{"id":36213,"text":"University of Redlands","active":true,"usgs":false}],"preferred":false,"id":938308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":938309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cox, Cheyenne L.","contributorId":355869,"corporation":false,"usgs":false,"family":"Cox","given":"Cheyenne L.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":938310,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265060,"text":"70265060 - 2023 - Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023","interactions":[],"lastModifiedDate":"2025-04-01T14:07:00.224393","indexId":"70265060","displayToPublicDate":"2023-12-01T09:02:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023","docAbstract":"

Red Knots (Calidris canutus rufa) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). We conducted a mark-recapture-resight investigation to estimate the passage population of Red Knots at Delaware Bay in 2023. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The 2023 passage population size was estimated at 39,361 (95% credible interval: 33,724–47,556). Although there is broad overlap in the credible intervals for population estimates from 2020–2023, the population estimate for 2023 was below 40,000 birds for only the second time since 2011. Horseshoe crabs have been harvested for use as bait in eel (Anguilla rostrata) and whelk (Busycon) fisheries since at least 1990. In the late 1990s and early 2000s, the number of Red Knots counted during aerial surveys at Delaware Bay declined from ~50,000 to ~13,000 and some avian conservation biologists hypothesized that horseshoe crab harvest levels in the 1990s prevented sufficient refueling for successful migration to the Arctic breeding grounds, reproduction, and survival for the remainder of the annual cycle. Since 2013, the harvest of horseshoe crabs in the Delaware Bay region has been managed using an Adaptive Resource Management (ARM) framework. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat for Red Knots and other migrating shorebirds. For annual harvest recommendations, the ARM framework requires annual estimates of horseshoe crab population size and the Red Knot stopover population size. The 2023 population size estimate will inform harvest recommendations in the next management cycle for decision making by the Atlantic States Marine Fisheries Commission.

","language":"English","publisher":"Delaware Division of Fish and Wildlife","usgsCitation":"Lyons, J.E., 2023, Red Knot stopover population size and migration ecology at Delaware Bay, USA, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-159297","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":484058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484043,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.alpha.delaware.gov/fish-wildlife/conservation/shorebirds/research/"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.08367028503963,\n 38.719590525784895\n ],\n [\n -74.91863610562206,\n 38.97012711441013\n ],\n [\n -74.8543007475435,\n 39.15256976333487\n ],\n [\n -75.3829695595777,\n 39.44695257658836\n ],\n [\n -75.51443746521609,\n 39.64539134375613\n ],\n [\n -75.65429693929858,\n 39.619540213656876\n ],\n [\n -75.56198968640427,\n 39.36698574058866\n ],\n [\n -75.42772459128453,\n 39.14606207448017\n ],\n [\n -75.3745779911329,\n 38.9679523390096\n ],\n [\n -75.08367028503963,\n 38.719590525784895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":932440,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250953,"text":"70250953 - 2023 - Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies","interactions":[],"lastModifiedDate":"2024-01-13T14:57:56.409648","indexId":"70250953","displayToPublicDate":"2023-12-01T08:55:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies","docAbstract":"

The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.

","language":"English","publisher":"Springer","doi":"10.1007/s10584-023-03623-z","usgsCitation":"Fortini, L., Kaiser, L.R., Frazier, A.G., and Giambelluca, T.W., 2023, Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies: Climatic Change, v. 176, https://doi.org/10.1007/s10584-023-03623-z.","productDescription":"169, 21 p.","startPage":"169","ipdsId":"IP-136355","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":441499,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10584-023-03623-z","text":"Publisher Index Page"},{"id":435113,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94IHW4X","text":"USGS data release","linkHelpText":"Hawaiian Islands downscaled climate projections for baseline (1983-2012), mid- (2040-2059), and late-century (2060-2079) 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\"}}]}","volume":"176","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Lauren R.","contributorId":200422,"corporation":false,"usgs":false,"family":"Kaiser","given":"Lauren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":892397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":892398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giambelluca, Thomas W","contributorId":296956,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas","email":"","middleInitial":"W","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":892399,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261199,"text":"70261199 - 2023 - New high resolution airborne geophysical surveys in Nevada And California for geothermal and mineral resource studies","interactions":[],"lastModifiedDate":"2024-11-29T14:54:09.70735","indexId":"70261199","displayToPublicDate":"2023-12-01T08:53:31","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"New high resolution airborne geophysical surveys in Nevada And California for geothermal and mineral resource studies","docAbstract":"The U.S. Geological Survey (USGS) and the Department of Energy (DOE) are collaborating to acquire high-resolution airborne magnetic and radiometric data to support geologic and geophysical mapping and modeling that will assist geothermal and critical mineral studies. Coordinated with these efforts are programs supporting geologic mapping and airborne LiDAR (light detection and ranging) surveys that yield detailed surface topographic models of the terrain over the same regions spanned by the geophysical surveys. The collaboration leverages resources from the USGS and DOE to acquire large regional datasets that will provide fundamental data necessary to map surface and subsurface geology and structure to benefit mineral and resource program objectives of both agencies. Such regionally uniform datasets are important for geothermal research to assist in identifying geologically favorable settings and as invaluable inputs in predictive models targeting undiscovered resources that use knowledge-driven (e.g., play fairway analysis) or data-driven approaches (e.g., machine-learning methods) to reduce risk associated with resource exploration. These data will also serve a wide range of other related activities from hazard (earthquake, volcano, landslide, environmental) and resource (water, mineral, energy) studies, to mapping and land management.\n\nSurveys were conducted in two areas that were selected because they host substantial geothermal and mineral potential in California and Nevada. The data will aid several ongoing USGS and DOE projects aimed at characterizing geothermal and mineral systems, understanding the factors controlling their occurrence, and improving future national resource assessments. The first of these surveys (referred to as GeoDAWN) was collected over northern and western Nevada and eastern California and spans areas of major resource potential associated with the Walker Lane and western Great Basin. This includes Clayton Valley, which hosts substantial lithium brine and clay resources, and the Humboldt Mafic Complex, which constitutes a potentially important resource of critical minerals (including cobalt, rare earth elements, platinum group elements, iron, chromium, nickel, and copper). The second survey area (referred to as GeoFlight) is focused over\n\nthe Salton Trough in southern California that contains some of the largest and hottest known hydrothermal systems in the world, as well as a substantial lithium brine resource that could potentially meet the nation’s lithium demand for electric vehicles. Data from both surveys will be made publicly available through USGS publications and online data repositories. Future efforts under this collaboration are presently being evaluated and may involve acquisition of other data sets such as airborne gravity, electromagnetic or hyperspectral data to address research targets.","language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Glen, J.M., and Earney, T.E., 2023, New high resolution airborne geophysical surveys in Nevada And California for geothermal and mineral resource studies, v. 47, p. 1738-1762.","productDescription":"25 p.","startPage":"1738","endPage":"1762","ipdsId":"IP-156123","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":464588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464580,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034804","linkFileType":{"id":5,"text":"html"}}],"volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":919603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":919604,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250443,"text":"70250443 - 2023 - Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska","interactions":[],"lastModifiedDate":"2023-12-09T14:53:33.90405","indexId":"70250443","displayToPublicDate":"2023-12-01T08:48:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska","docAbstract":"

This map of fractures, scarps, faults, and landslides was completed to identify areas in Glacier Bay National Park and Preserve that may present a landslide-generated tsunami hazard. To address the potential of landslide and tsunami hazards in the park, the National Park Service (NPS) and the US Geological Survey (USGS) partnered to conduct a multi-year hazard assessment of Glacier Bay National Park and Preserve. To produce the map described in this report, we used the newly acquired (2019-2020) light detection and ranging (LiDAR) 0.5 to 1.0 m digital elevation models (DEMs) that cover all the coastal areas of the park and extend up to the ridgetops in places with steep slopes. A bare earth DEM was used to identify and map areas of incipient landslides (i.e., fractures and scarps), fault scarps, and areas where landslides have clearly occurred in the past (i.e., areas where scars and deposits are clearly visible). This map provides a baseline data set that can be used to aid forecasts of where landslides are most likely to occur in the future.

","language":"English","publisher":"National Park Service","doi":"10.36967/2300706","collaboration":"National Park Service","usgsCitation":"Hults, C., Coe, J.A., and Avdievitch, N.N., 2023, Fractures, scarps, faults, and landslides mapped using LiDAR, Glacier Bay National Park and Preserve, Alaska, iv, 14 p., https://doi.org/10.36967/2300706.","productDescription":"iv, 14 p.","ipdsId":"IP-147660","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":423385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -139.23040794383448,\n 59.9305550708161\n ],\n [\n -139.23040794383448,\n 57.252640525398476\n ],\n [\n -134.22064231883454,\n 57.252640525398476\n ],\n [\n -134.22064231883454,\n 59.9305550708161\n ],\n [\n -139.23040794383448,\n 59.9305550708161\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hults, Chad","contributorId":332290,"corporation":false,"usgs":false,"family":"Hults","given":"Chad","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":889926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":889927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avdievitch, Nikita N. 0000-0002-2507-2962","orcid":"https://orcid.org/0000-0002-2507-2962","contributorId":225492,"corporation":false,"usgs":true,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889928,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257372,"text":"70257372 - 2023 - Simulation modeling to assess line transect distance sampling under a range of translocation scenarios","interactions":[],"lastModifiedDate":"2024-09-04T15:33:06.278948","indexId":"70257372","displayToPublicDate":"2023-12-01T08:35:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Simulation modeling to assess line transect distance sampling under a range of translocation scenarios","docAbstract":"

The accuracy of posttranslocation population monitoring methods is critical to assessing long-term success in translocation programs. Translocation can produce unique challenges to monitoring efforts; therefore, it is important to understand the flexibility and robustness of commonly used monitoring methods. In Florida, USA, thousands of gopher tortoises Gopherus polyphemus have been, and continue to be, translocated from development sites to permitted recipient sites. These recipient sites create a broad range of potential monitoring scenarios due to variability in soft-release strategies, habitat conditions, and population demographics. Line transect distance sampling is an effective method for monitoring natural tortoise populations, but it is currently untested for translocated populations. We therefore produced 3,024 individual-based, spatially explicit scenarios of translocated tortoise populations that differed in recipient site and tortoise population properties, based on real-world examples, literature review, and expert opinion. We virtually sampled simulated tortoise populations by using line transect distance sampling methods and built a Bayesian hierarchical model to estimate the population density for each simulation, which incorporated individual-level covariates (i.e., burrow width and burrow occupancy). Line transect distance sampling was largely appropriate for the conditions that typify gopher tortoise recipient sites, particularly when detection probability on the transect lines was greater than or equal to 0.85. Designing the layout of transects relative to the orientation of soft-release pens, to avoid possible sampling biases that lead to extreme outliers in estimates of tortoise densities, resulted in more accurate population estimates. We also suggest that use of individual-level covariates, applied using a Bayesian framework as demonstrated in our study, may improve the applicability of line transect distance sampling surveys in a variety of contexts and that simulation can be a powerful tool for assessing survey design in complex sampling situations.

","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-23-029","usgsCitation":"Jones, M.D., Smith, L., Gentry Richardson, K., DeSha, J., Castellón, T., Hipes, D., Kalfin, A., Halstead, N.T., and Hunter, E.A., 2023, Simulation modeling to assess line transect distance sampling under a range of translocation scenarios: Journal of Fish and Wildlife Management, v. 14, no. 2, p. 385-399, https://doi.org/10.3996/JFWM-23-029.","productDescription":"15 p.","startPage":"385","endPage":"399","ipdsId":"IP-151978","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-029","text":"Publisher Index Page"},{"id":433450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Extreme water-level recurrence estimates for a complex estuary using a high-resolution 2D model and a new method for estimating remotely generated sea level anomalies (SLAs) at the model boundary have been developed. The hydrodynamic model accurately resolves the dominant physical processes contributing to extreme water levels across the Washington State waters of the Salish Sea, including the relative contribution of remote SLA and other non-tidal residual processes that drive extreme water levels above the predicted tide. The model’s predictions have errors of less than 15 cm (<5% of 3–4 m tidal range) at eight tide gauge locations across the model domain. The influence of remote SLAs at the seaward boundary of the model was implemented using a multivariate regression of readily available and locally relevant wind, sea surface temperature, and pressure anomaly data, combined with El Niño Index data (R2 = 0.76). The hydrodynamic model simulations using the remote SLA predictor compared well with simulations using the widely used data-assimilative global ocean model HYCOM SLA data (root mean square difference of 5.5 cm). Extreme water-level recurrence estimates with and without remote SLA show that remote forcing accounts for 50–60% of the total water level anomaly observed along Salish Sea shorelines. The resulting model simulations across decadal timescales provide estimates of extreme water level recurrence across the Salish Sea, capturing climate variability important to long-term coastal hazard planning. This approach has widespread applications for other complex estuarine systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w15234167","usgsCitation":"Grossman, E.E., Tehranirad, B., Nederhoff, K., Crosby, S., Stevens, A.W., VanArendonk, N.R., Nowacki, D.J., Erikson, L.H., and Barnard, P.L., 2023, Modeling extreme water levels in the Salish Sea: The importance of including remote sea level anomalies for application in hydrodynamic simulations: Water, v. 15, no. 23, 4167, 24 p., https://doi.org/10.3390/w15234167.","productDescription":"4167, 24 p.","ipdsId":"IP-146065","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15234167","text":"Publisher Index Page"},{"id":425601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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The diffuse attenuation coefficient of photosynthetically active radiation (KdPAR) is commonly used to predict light attenuation in aquatic productivity models, but obtaining measurements of PAR to compute KdPAR is difficult. In situ calculations of KdPAR require multiple measurements of PAR through the water column, and these measurements are infeasible for real-time recording. Instead, predictive models using surface-water measurements may be used. Traditional KdPAR models are based on open-ocean habitats and rely on chlorophyll—as a proxy measurement for phytoplankton abundance—as the main predictive parameter. However, elevated suspended sediments and dissolved organic materials may also affect KdPAR values of inland water bodies and estuaries. In this study, we leverage KdPAR calculations derived from in situ light measurements collected along with surface-water-quality parameters across the Sacramento-San Joaquin River Delta in California, USA (the Delta). Sampling occurred between January of 2013 and May of 2014. We also explored regional and seasonal effects, but these did not clearly affect the model. Ultimately, the best-performing model included surface-level turbidity only (R2 = 0.91). The simplicity of the model facilitates use of KdPAR estimates for a variety of purposes throughout the Delta, including euphotic depth calculations, and as inputs to primary-productivity and habitat-suitability models. We demonstrate the model’s usability with two open-sources data sets (one spatially dense, and one temporally dense), and estimate KdPAR, euphotic depth, and primary productivity within the Delta. We provide calculations for each estimation, allowing users to easily adopt these models and apply them to their own data or with open-sourced data, which are abundant.

","language":"English","publisher":"University of California","doi":"10.15447/sfews.2023v21iss4art5","usgsCitation":"Richardson, E.T., Bouma-Gregson, K., O’Donnell, K., and Bergamaschi, B.A., 2023, A simple approach to modeling light attenuation in the Sacramento-San Joaquin Delta using commonly available data: San Francisco Estuary and Watershed Sciences, v. 21, no. 4, 5, 15 p., https://doi.org/10.15447/sfews.2023v21iss4art5.","productDescription":"5, 15 p.","ipdsId":"IP-149629","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2023v21iss4art5","text":"Publisher Index Page"},{"id":423859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.26086948103463,\n 38.478699734733226\n ],\n [\n -122.26086948103463,\n 37.77218409560264\n ],\n [\n -121.28033969587833,\n 37.77218409560264\n ],\n [\n -121.28033969587833,\n 38.478699734733226\n ],\n [\n -122.26086948103463,\n 38.478699734733226\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Katy 0000-0003-2323-8970 kodonnell@usgs.gov","orcid":"https://orcid.org/0000-0003-2323-8970","contributorId":5640,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Katy","email":"kodonnell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":890902,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250597,"text":"70250597 - 2023 - Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling","interactions":[],"lastModifiedDate":"2023-12-19T12:40:36.280538","indexId":"70250597","displayToPublicDate":"2023-12-01T06:38:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3824,"text":"Letters in Biomathematics","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling","docAbstract":"Mathematical biology is a wide field of study with many venues that undergraduate students can access through research. However, the topics of study for these students can be overwhelming, and many topics of study yield either only trivial results or abstract outcomes that are nonintuitive and diffcult to understand. We have used natural resource modeling, and more specifically, a partnership between academic researchers and federal scientists, as a bridge between undergraduate research and mathematical biology. Our collaboration is an interdisciplinary team that combines biology, mathematics, and statistics professors with government research scientists. As a team, we have mentored students through opportunities such as a Research Experiences for Undergraduates and other projects. In this article, we provide an overview of how we develop questions for undergraduates and outline two case studies, both of which resulted in peer reviewed journal articles. Last, we describe how we also transfer the results from these undergraduate projects to resource managers so the results may be applied to real world problems.","language":"English","publisher":"Intercollegiate Biomathematics Alliance","usgsCitation":"Erickson, R.A., Baumann, D., Bennie, B., Bungula, W., Cupp, A.R., Diffendorfer, J., Eager, E., Haro, R.J., Jankowski, K.J., Larson, D.M., Sandland, G., Van Appledorn, M., and Peirce, J.P., 2023, Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling: Letters in Biomathematics, v. 10, no. 1, p. 185-191.","productDescription":"7 p.","startPage":"185","endPage":"191","ipdsId":"IP-147034","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890506,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":890507,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eager, Eric A.","contributorId":140447,"corporation":false,"usgs":false,"family":"Eager","given":"Eric A.","affiliations":[{"id":13504,"text":"Department of Mathematics, University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":890508,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":890509,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences 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Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890513,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peirce, James P 0000-0002-7147-3695","orcid":"https://orcid.org/0000-0002-7147-3695","contributorId":316559,"corporation":false,"usgs":false,"family":"Peirce","given":"James","email":"","middleInitial":"P","affiliations":[{"id":47908,"text":"University of Wisconsin - La Crosse","active":true,"usgs":false}],"preferred":false,"id":890514,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70257366,"text":"70257366 - 2023 - A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes","interactions":[],"lastModifiedDate":"2024-08-23T16:23:27.893177","indexId":"70257366","displayToPublicDate":"2023-12-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal 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A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad-scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a (Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.

","language":"English","publisher":"Wiley","doi":"10.1002/lno.12457","usgsCitation":"Lapierre, J., Webster, K.E., Hanks, E., Wagner, T., Soranno, P.A., McCullough, I., Reinl, K.L., Domka, M., and Lotting, N.R., 2023, A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes: Limnology and Oceanography, v. 69, no. 12, p. 2759-2773, https://doi.org/10.1002/lno.12457.","productDescription":"15 p.","startPage":"2759","endPage":"2773","ipdsId":"IP-145952","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.12457","text":"Publisher Index Page"},{"id":433110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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Introduction

In Minnesota, high-quality elevation data are essential for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, coastal zone management, and many other business uses. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

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\"}}]}","contact":"

Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192

Email: 3DEP@usgs.gov

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Introduction

Montana, America’s fourth largest State with an area of 147,040 square miles, is defined by its diverse terrain. The western two-fifths of the State falls within the Rocky Mountains and the eastern three-fifths is in the Great Plains. Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

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\"}}]}","contact":"

Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192

Email: 3DEP@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-11-30","noUsgsAuthors":false,"publicationDate":"2023-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Tom 0000-0002-5682-8988","orcid":"https://orcid.org/0000-0002-5682-8988","contributorId":304658,"corporation":false,"usgs":true,"family":"Carlson","given":"Tom","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":888663,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250366,"text":"70250366 - 2023 - Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui","interactions":[],"lastModifiedDate":"2023-12-05T13:13:51.187375","indexId":"70250366","displayToPublicDate":"2023-11-30T07:05:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui","docAbstract":"

Haleakalā National Park and montane areas on east Maui, Hawaiian Archipelago, support critical nesting habitat for endangered ‘ua‘u Hawaiian petrel Pterodroma sandwichensis. Habitat loss, non-native predators, and damage by feral ungulates are limiting factors for ground-nesting petrels at Haleakalā and throughout Hawai‘i. Because nesting habitats differ among the Hawaiian Islands, habitat distribution modeling for Hawaiian petrel has been island specific. Based on 2453 known nest site locations, we provide the first landscape-scale predictive model describing relative abundance and habitat available for nesting petrels throughout upper Haleakalā (1830 to 3055 m). We evaluated (principal components analyses and Pearson’s correlation) 13 spatial landscape and climate predictor variables associated with nest sites and the background landscape followed by random forest modeling to predict nest site density. Six variables (elevation, slope, topographic position index at 2 scales, heat load index, presence-absence ash/cinder, and presence-absence vegetation) indicated nest sites occurred non-randomly throughout the central part of the summit and crater; greatest concentrations were predicted along the crater rim and a ridgeline extending southwest from the summit. Moderately high predicted density occurred in the northeastern and northern crater. Lower elevations to the north, west, and south flanks of Haleakalā had relatively fewer predicted nest sites. Although we focused on higher elevations on Haleakalā, there is no reason to suspect that conservation efforts would not be successful at lower elevations, provided nesting petrels were protected from invasive predators, grazing ungulates, and significant land alteration.

","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01280","usgsCitation":"Adams, J., Felis, J., Klinger, R.C., Kelsey, E.C., Tamayose, J., Kaholoa’a, R., Bailey, C.N., Penniman, J.F., Learned, J., Ganter, C., Medeiros, J., and Chen, H., 2023, Predicted distribution of ‘ua‘u (Hawaiian petrel Pterodroma sandwichensis) nest sites on Haleakalā, Maui: Endangered Species Research, v. 52, p. 231-246, https://doi.org/10.3354/esr01280.","productDescription":"16 p.","startPage":"231","endPage":"246","ipdsId":"IP-148421","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441525,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01280","text":"Publisher Index Page"},{"id":423239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Haleakalā, 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Ciara","contributorId":332156,"corporation":false,"usgs":false,"family":"Ganter","given":"Ciara","email":"","affiliations":[{"id":79397,"text":"Hawai'i State of Dep. of Land and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":889579,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Medeiros, John","contributorId":167591,"corporation":false,"usgs":false,"family":"Medeiros","given":"John","email":"","affiliations":[{"id":24766,"text":"4. State of Hawaii, Division of Forestry and Wildlife-Maui, 54 South High Street # 101, Wailuku, HI 96793.","active":true,"usgs":false}],"preferred":false,"id":889580,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chen, Huisheng","contributorId":332157,"corporation":false,"usgs":false,"family":"Chen","given":"Huisheng","email":"","affiliations":[{"id":79398,"text":"NPS; University of Hawai'i","active":true,"usgs":false}],"preferred":false,"id":889581,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70250805,"text":"70250805 - 2023 - Practical guide to measuring wetland carbon pools and fluxes","interactions":[],"lastModifiedDate":"2024-01-08T17:36:42.174027","indexId":"70250805","displayToPublicDate":"2023-11-28T07:39:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Practical guide to measuring wetland carbon pools and fluxes","docAbstract":"

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

","language":"English","publisher":"Springer","doi":"10.1007/s13157-023-01722-2","usgsCitation":"Bansal, S., Creed, I., Tangen, B., Bridgham, S.D., Desai, A.R., Krauss, K., Neubauer, S.C., Noe, G.E., Rosenberry, D.O., Trettin, C.C., Wickland, K., Allen, S.T., Arias-Ortiz, A., Armitage, A.R., Baldocchi, D., Banerjee, K., Bastviken, D., Berg, P., Bogard, M.J., Chow, A.T., Conner, W.H., Craft, C., Creamer, C., Delsontro, T., Duberstein, J., Eagle, M.J., Fennessey, M.S., Finkelstein, S.A., Goeckede, M., Grunwald, S., Halibisky, M., Herbert, E.R., Jahangir, M., Johnson, O., Jones, M.C., Kelleway, J., Knox, S., Kroeger, K.D., Kuehn, K., Lobb, D., Loder, A., Ma, S., Maher, D., McNicol, G., Meier, J., Middleton, B.A., Mills, C.T., Mistry, P., Mitra, A., Mobilian, C., Nahlik, A.M., Newman, S., O’Connell, J., Oikawa, P., Post van der Burg, M., Schutte, C.A., Song, C., Stagg, C.L., Turner, J., Vargas, R., Waldrop, M., Wallin, M., Wang, Z.A., Ward, E., Willard, D., Yarwood, S.A., and Zhu, X., 2023, Practical guide to measuring wetland carbon pools and fluxes: Wetlands, v. 43, 105, 169 p., https://doi.org/10.1007/s13157-023-01722-2.","productDescription":"105, 169 p.","ipdsId":"IP-138829","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a longstanding challenge and a particular impediment to automated analysis. This paper demonstrates that sinuosity can vary with the magnitude of units at which it is measured (measurement scales). The paper derives a scale-specific sinuosity (S3) metric intended to measure stream bend geometry across a range of measurement scales. The metric is warranted for analysis and modeling at measurement scales equal or similar to the spatial process and landscape conditions under investigation. Derived from the Richardson (1961) plot, the S3 metric quantifies a frequency signature of planform bend sizes spanning a range of measurement units, enabling visual and quantitative analysis of bend geometry in linear stream features. Derivation of the metric makes evident a systematic relationship between stream bend size, sinuosity, and measurement scale, formalizing a relationship between sinuosity and fractal dimension. The paper shows the utility of the S3 metric in examining bend patterns for synthetic and real-world linear stream data.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2023.108734","usgsCitation":"Stanislawski, L., Kronenfeld, B.J., Buttenfield, B.P., and Shavers, E.J., 2023, At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution: Geomorphology, v. 436, 108734, 11 p., https://doi.org/10.1016/j.geomorph.2023.108734.","productDescription":"108734, 11 p.","ipdsId":"IP-147902","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":441543,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2023.108734","text":"Publisher Index Page"},{"id":499546,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13VXT4Z","text":"USGS data release","linkHelpText":"Scale_Specific_Sinuosity"},{"id":423008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"436","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":888907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":888908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888909,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250191,"text":"70250191 - 2023 - Understanding the drivers of volcano deformation through geodetic model verification and validation","interactions":[],"lastModifiedDate":"2023-11-28T12:51:48.239575","indexId":"70250191","displayToPublicDate":"2023-11-28T06:44:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the drivers of volcano deformation through geodetic model verification and validation","docAbstract":"

Volcano geodesy often involves the use of models to explain observed surface deformation. A variety of forward models are used, from analytical point sources to numerical simulations that consider complex magma system geometries, topography, and material properties. Various inversion methods can then be used to relate observed volcano data to models. Ideally, forward models should be verified through intercomparison, to check for implementation errors and quantify the error induced by any approximations used. Additionally, forward models and inversion methods should be validated through tests with synthetic and/or real data, to determine their ability to match data and estimate parameter values within uncertainty. However, to date, there have not been comprehensive verification and validation efforts in volcano geodesy. Here, we report on the first phase of the Drivers of Volcano Deformation (DVD) exercises, which were designed to build community involvement through web-based exercises involving calculations of static elastic displacement around pressurized magma reservoirs. The forward model exercises begin with a spherical reservoir in a homogeneous half space, then introduce topography, heterogeneous elastic properties, and spheroidal geometries. The inversion exercises provide synthetic noisy surface displacement data for a spherical reservoir in a homogeneous half space and assess consistency in estimates of reservoir location and volume/pressure change. There is variability in the results from both forward modeling and inversions, which highlights the strengths and limitations of different forward models, as well as the importance of inversion method choice and uncertainty quantification. This first phase of the DVD exercises serves as a community resource and will facilitate further efforts to develop standards of reproducibility.

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Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.

","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ad0993","usgsCitation":"Garcia-Tigreros, F., Elder, C.D., Kurek, M.R., Miller, B.L., Xu, X., Wickland, K., Czimczik, C.I., Dornblaser, M.M., Striegl, R.G., Kyzivat, E.D., Smith, L., Spencer, R., Miller, C.E., and Butman, D., 2023, Arctic-boreal lakes of interior Alaska dominated by contemporary carbon: Environmental Research Letters, v. 18, no. 12, 124024, 11 p., https://doi.org/10.1088/1748-9326/ad0993.","productDescription":"124024, 11 p.","ipdsId":"IP-148398","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":441558,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0993","text":"Publisher Index Page"},{"id":422881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Boot Lake, Canvasback Lake, Greenpepper Lake, Shack Lake, Thumb Lake, Twelvemile Lake, Twin Lake, West Crazy Lake, YF18 Lake, Yukon Flats National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.32495243819622,\n 65.09586209258472\n ],\n [\n -149.03799309568313,\n 64.59463623905711\n ],\n [\n -143.9761424480567,\n 65.63219990838482\n ],\n [\n -141.00690728046249,\n 66.43516910407672\n ],\n [\n -140.9727162057442,\n 68.73213031511489\n ],\n [\n -148.19579980608407,\n 67.27696483824582\n ],\n [\n -153.60488278174276,\n 67.04326208119708\n ],\n [\n -152.32495243819622,\n 65.09586209258472\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Tigreros, Fenix 0000-0001-8694-9046","orcid":"https://orcid.org/0000-0001-8694-9046","contributorId":194744,"corporation":false,"usgs":false,"family":"Garcia-Tigreros","given":"Fenix","email":"","affiliations":[],"preferred":false,"id":888627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elder, Clayton D.","contributorId":201542,"corporation":false,"usgs":false,"family":"Elder","given":"Clayton","email":"","middleInitial":"D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":888628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurek, Martin R.","contributorId":300567,"corporation":false,"usgs":false,"family":"Kurek","given":"Martin","email":"","middleInitial":"R.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":888629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Benjamin L.","contributorId":331727,"corporation":false,"usgs":false,"family":"Miller","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":888630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":888631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - 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Washington","active":true,"usgs":false}],"preferred":false,"id":888640,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70250773,"text":"70250773 - 2023 - Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths","interactions":[],"lastModifiedDate":"2024-01-04T13:09:31.702227","indexId":"70250773","displayToPublicDate":"2023-11-23T07:06:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths","docAbstract":"

Groundwater discharge to streams is a nonpoint source of nitrogen (N) that confounds N mitigation efforts and represents a significant portion of the annual N loading to watersheds. However, we lack an understanding of where and how much groundwater N enters streams and watersheds. Nitrogen concentrations at the end of groundwater flowpaths are the culmination of biogeochemical and physical processes from the contributing land area where groundwater recharges, within the aquifer system, and in the near-stream riparian area where groundwater discharges to streams. Our research objectives were to quantify the spatial distribution of N concentrations at groundwater discharges throughout a mixed land-use watershed and to evaluate how relationships among contributing and riparian land cover, modeled aquifer characteristics, and groundwater discharge biogeochemistry explain the spatial variation in groundwater discharge N concentrations. We accomplished this by integrating high-resolution thermal infrared surveys to locate groundwater discharge, biogeochemical sampling of groundwater, and a particle tracking model that links groundwater discharge locations to their contributing area land cover. Groundwater N loading from groundwater discharges within the watershed varied substantially between and within streambank groundwater discharge features. Groundwater nitrate concentrations were spatially heterogeneous ranging from below 0.03–11.45 mg-N/L, varying up to 20-fold within meters. When combined with the particle tracking model results and land cover metrics, we found that groundwater discharge nitrate concentrations were best predicted by a linear mixed-effect model that explained over 60% of the variation in nitrate concentrations, including aquifer chemistry (dissolved oxygen, Cl−, SO42−), riparian area forested land cover, and modeled physical aquifer characteristics (discharge, Euclidean distance). Our work highlights the significant spatial variability in groundwater discharge nitrate concentrations within mixed land-use watersheds and the need to understand groundwater N processing across the many spatiotemporal scales within groundwater cycling.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ad0c86","usgsCitation":"Moore, E.M., Barclay, J.R., Haynes, A.B., Jackson, K.E., Bisson, A.M., Briggs, M., and Helton, A.M., 2023, Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths: Environmental Research Letters, v. 18, 124039, 12 p., https://doi.org/10.1088/1748-9326/ad0c86.","productDescription":"124039, 12 p.","ipdsId":"IP-150250","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":441560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0c86","text":"Publisher Index Page"},{"id":424111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut","otherGeospatial":"Farmington River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.18499513799182,\n 42.06150696284237\n ],\n [\n -73.18499513799182,\n 41.43446844111949\n ],\n [\n -72.30059572392948,\n 41.43446844111949\n ],\n [\n -72.30059572392948,\n 42.06150696284237\n ],\n [\n -73.18499513799182,\n 42.06150696284237\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Eric M. 0000-0002-2479-2677","orcid":"https://orcid.org/0000-0002-2479-2677","contributorId":332922,"corporation":false,"usgs":false,"family":"Moore","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":79689,"text":"University of Connecticut, Department Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":891365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haynes, Adam B.","contributorId":332923,"corporation":false,"usgs":false,"family":"Haynes","given":"Adam","email":"","middleInitial":"B.","affiliations":[{"id":79690,"text":"University of California Santa Cruz, Earth & Planetary Sciences Department","active":true,"usgs":false}],"preferred":false,"id":891367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Kevin E. 0000-0002-2156-5073","orcid":"https://orcid.org/0000-0002-2156-5073","contributorId":332924,"corporation":false,"usgs":false,"family":"Jackson","given":"Kevin","email":"","middleInitial":"E.","affiliations":[{"id":79691,"text":"University of Maryland, Center for Envirnomental Science","active":true,"usgs":false}],"preferred":false,"id":891368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bisson, Alaina M.","contributorId":332925,"corporation":false,"usgs":false,"family":"Bisson","given":"Alaina","email":"","middleInitial":"M.","affiliations":[{"id":79692,"text":"Harvard University Arboretum","active":true,"usgs":false}],"preferred":false,"id":891369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - 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This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4686","usgsCitation":"Dietze, M.C., Thomas, R.Q., Peters, J., Boettiger, C., Koren, G., Shiklomanov, A.N., and Ashander, J., 2023, A community convention for ecological forecasting: Output files and metadata version 1.0: Ecosphere, v. 14, no. 11, e4686, 23 p., https://doi.org/10.1002/ecs2.4686.","productDescription":"e4686, 23 p.","ipdsId":"IP-146045","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":441562,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4686","text":"Publisher Index Page"},{"id":423087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietze, Michael C.","contributorId":266096,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":54896,"text":"Department of Earth & Environment, Boston University, Boston, MA 02215, USA","active":true,"usgs":false}],"preferred":false,"id":889240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":889241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peters, Jody","contributorId":216790,"corporation":false,"usgs":false,"family":"Peters","given":"Jody","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":889242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boettiger, Carl 0000-0002-1642-628X","orcid":"https://orcid.org/0000-0002-1642-628X","contributorId":332018,"corporation":false,"usgs":false,"family":"Boettiger","given":"Carl","email":"","affiliations":[{"id":79359,"text":"Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":889243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koren, Gerband","contributorId":332019,"corporation":false,"usgs":false,"family":"Koren","given":"Gerband","email":"","affiliations":[{"id":79360,"text":"Copernicus Institute of Sustainable Development, Utrecht University, Netherlands","active":true,"usgs":false}],"preferred":false,"id":889244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shiklomanov, Alexy N.","contributorId":332020,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexy","email":"","middleInitial":"N.","affiliations":[{"id":79361,"text":"NASA Goddard Space Flight Center, Greenbelt, MD, USA","active":true,"usgs":false}],"preferred":false,"id":889245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ashander, Jaime 0000-0002-1841-4768","orcid":"https://orcid.org/0000-0002-1841-4768","contributorId":294949,"corporation":false,"usgs":true,"family":"Ashander","given":"Jaime","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":889246,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}