Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.
","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/JSTARS.2022.3177025","usgsCitation":"Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S.B., Li, W., Nguyen, H., Qiu, T., Tran, S., and Shen, C., 2022, Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 4349-4370, https://doi.org/10.1109/JSTARS.2022.3177025.","productDescription":"23 p.","startPage":"4349","endPage":"4370","ipdsId":"IP-137285","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2022.3177025","text":"Publisher Index Page"},{"id":401292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagendra, Savinay","contributorId":292084,"corporation":false,"usgs":false,"family":"Nagendra","given":"Savinay","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kifer, Daniel","contributorId":292085,"corporation":false,"usgs":false,"family":"Kifer","given":"Daniel","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":843803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pei, Te","contributorId":292087,"corporation":false,"usgs":false,"family":"Pei","given":"Te","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lawson, Kathryn","contributorId":292089,"corporation":false,"usgs":false,"family":"Lawson","given":"Kathryn","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manjunatha, Srikanth Banagere","contributorId":292090,"corporation":false,"usgs":false,"family":"Manjunatha","given":"Srikanth","email":"","middleInitial":"Banagere","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weixin","contributorId":292093,"corporation":false,"usgs":false,"family":"Li","given":"Weixin","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nguyen, Hien","contributorId":292096,"corporation":false,"usgs":false,"family":"Nguyen","given":"Hien","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Qiu, Tong","contributorId":292099,"corporation":false,"usgs":false,"family":"Qiu","given":"Tong","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tran, Sarah","contributorId":292102,"corporation":false,"usgs":false,"family":"Tran","given":"Sarah","email":"","affiliations":[{"id":37314,"text":"Google Inc.","active":true,"usgs":false}],"preferred":false,"id":843810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70232104,"text":"70232104 - 2022 - #TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","interactions":[],"lastModifiedDate":"2022-06-06T11:51:38.884858","indexId":"70232104","displayToPublicDate":"2022-05-27T06:48:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"#TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","docAbstract":"Studies from a variety of disciplines reveal that humor can be a useful method to reduce stress and increase compassion, connection, and empathy between agencies and people they serve during times of crisis. Despite this growing evidence base, humor's use during a geohazard (earthquake, volcanoes, landslides, and tsunami) to aid scientific agencies' crisis communication response has been rarely studied. A broad literature review of humor in crisis and an exploratory examination of several case studies reveal that scientific organizations, specifically those that respond to geohazards, can harness the power of humor to help create connection and empathy with the publics they seek to serve. We find evidence that supports our argument that the use of humor acknowledges a shared human experience, reducing the barriers between public officials, scientists, and the people most impacted by crisis. Public statements made by scientists and public officials during the U.S. Geological Survey (USGS) response to the Kīlauea eruption in 2018 in Hawai'i, United States, and GNS Science/GeoNet (GeoNet) response to the M7.8 Kaikōura/North Hurunui earthquake in 2016 in Aotearoa New Zealand, are used to inform the development of this conceptual model. We then posit a conceptual model which unifies concepts from the literature with our case studies to provide potential guidelines for those crisis communicators working for science agencies on how best to use humor to help people cope during times of crisis. This model can be further tested for future research to determine its effectiveness and utility for scientific agencies responding to geological crises.
Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.
In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.
Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":502376,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113075.htm","linkFileType":{"id":5,"text":"html"}},{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
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U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Anthropogenic barriers to main-stem and tributary passage are one of the primary threats associated with declining populations of Pacific Lamprey (Entosphenus tridentatus) in the Columbia River Basin. Juvenile lamprey are of special interest because their downstream migration to the ocean may be affected by barriers such as dams or water diversions. Telemetry studies that describe the movement and passage of juvenile lamprey have not been possible until the recent development of a micro-transmitter specifically for use in juvenile lamprey and eels. Through a collaborative research approach, we used these prototype transmitters and acoustic monitoring arrays installed for a juvenile salmon (Oncorhynchus spp.) migration study to evaluate juvenile lamprey movements in the Yakima River (river kilometer 179 to the river mouth) in 2019 and 2020. We tagged and released 152 juvenile lamprey from April 30 to June 5, 2019, and on June 9, 2020. Lamprey were released 6.9 kilometers (km) upstream from Wapato Dam, 1.2 km upstream from Prosser Dam, and into the canal and tailrace at Prosser Dam. Most tagged lamprey did not initiate downstream movements within the 18 days of tag life, as evidenced by our detections of lamprey in the highest numbers at the first monitoring site downstream from their release site, with limited or no detections at sites farther downstream. There was no evidence of missed detections (lamprey detected at a downstream site without corresponding detections upstream). Overall detections of tagged lamprey were low: 27.0 percent in 2019 and 48.0 percent in 2020. River flows were less than the 10-year average during the monitoring period and water temperatures were variable. Lamprey arrived at detections sites predominantly during periods of darkness (85.3–96.6 percent) following daytime releases. Travel rates through the study area ranged from 0.2 to 45.3 kilometers per day, and lamprey generally remained at each detection station for less than about 20 minutes. Groups of lamprey released together generally had similar travel rates with a small number of fish that moved more quickly or slowly than the remainder of the group. In addition to monitoring the migration and behavior of juvenile lamprey, we also assessed some assumptions of survival models (determining downstream drift of purposely killed fish and empirically measuring transmitter operating life) to benefit future evaluations focused on migration survival of juvenile lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221052","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, McNary Fisheries Compensation Committee, Bonneville Power Administration, and the Pacific Northwest National Laboratory","usgsCitation":"Liedtke, T.L., Lampman, R.T., Monk, P., Hansen, A.C., Kock, T.J., Beals, T.E., Deng, D.Z., and Porter, M.S., 2022, Monitoring the movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima River, Washington, using acoustic telemetry, 2019–20: U.S. Geological Survey Open-File Report 2022–1052, 28 p., https://doi.org/10.3133/ofr20221052.","productDescription":"Report: viii, 28 p.; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-133893","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401158,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1052/images"},{"id":401157,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://app.streamnet.org/files/822/","text":"Pacific States Marine Fisheries Commission, StreamNet—Fish Data for the Northwest data files"},{"id":401156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1052/ofr20221052.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1052"},{"id":401155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1052/covrthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.684814453125,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.01985337287631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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6505 NE 65th Street
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The U.S. Geological Survey (USGS), in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs, assessed four groundwater-withdrawal scenarios and their potential effects on the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area in western Oklahoma. Increases in industrial and public water supply needs have led to increased development of water resources within the Rush Springs aquifer. As new areas within the aquifer are developed, increased water withdrawals may result in decreases in available groundwater resources and conflicts among water users.
For this study, a previously published numerical groundwater-flow model of the Rush Springs aquifer was modified to simulate the potential effects of four groundwater withdrawal scenarios. For the previously published calibrated model, groundwater flow was simulated from 1979 through 2015. In this study, groundwater flow simulations were extended through 2035. The period from 2016 through 2035 is referred to as the “20-year projection.” Four groundwater withdrawal scenarios starting in 2007 and continuing through 2035 were evaluated. Scenario 1 simulated no groundwater withdrawals; scenario 2 simulated no withdrawals allocated for out-of-basin water-use transfers; scenario 3 simulated withdrawals based on reported withdrawals during the 2007–15 simulation period and compounded annual increases in groundwater use during the subsequent 20-year projection; and scenario 4 simulated maximum permitted withdrawals for allocation to out-of-basin water-use transfers. Out-of-basin water transfers were classified as withdrawals that are not returned back to the aquifer.
At the springs of interest, changes in water-level altitudes in response to different groundwater withdrawal scenarios were simulated by comparing the results from different model cells. Between 2007 and 2015, scenarios 2–4 yielded similar simulated water-level altitudes in the model cells containing springs of interest, with water-level altitudes decreasing to below the land surface altitude at 13 of the total 25 springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude. For the 20-year projection, water-level altitudes at springs simulated in model cells in scenarios 2–4 decreased to below land surface altitude for 13 of the total 25 model cells containing springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude.
The potential effects of groundwater withdrawals were evaluated by comparing changes in groundwater storage between the four scenarios. The 2007–15 groundwater withdrawal scenarios were used to simulate the potential effects of groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. The simulated groundwater storage change in the Rush Springs aquifer ranged from an increase of 2.8 percent for scenario 1 to an increase of 1.0 percent for scenario 4. Projected 20-year groundwater withdrawal scenarios were used to simulate the potential effects of selected groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. Simulated groundwater storage changes ranged from a decrease of 0.5 percent for scenario 1 to a decrease of 0.7 percent for scenario 4.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225044","collaboration":"Prepared in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs","usgsCitation":"Labriola, L.G., Russell, C.A., and Ellis, J.H., 2022, Potential effects of out-of-basin groundwater transfers on spring discharge, base flow, and groundwater storage pertaining to the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma: U.S. Geological Survey Scientific Investigations Report 2022–5044, 32 p., https://doi.org/10.3133/sir20225044.","productDescription":"Report: vii, 32 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-128617","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":400914,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92DYE98","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate the potential effects of out-of-basin transfers for the Rush Springs aquifer in the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma"},{"id":400911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5044"},{"id":400910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5044/coverthb.jpg"},{"id":400915,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":502399,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113068.htm","linkFileType":{"id":5,"text":"html"}},{"id":401055,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400913,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5044/images"},{"id":400912,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.XML"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Rush Springs Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 34.45221847282654\n ],\n [\n -98.5693359375,\n 36.491973470593685\n ],\n [\n -99.66796875,\n 35.817813158696616\n ],\n [\n -99.0966796875,\n 35.137879119634185\n ],\n [\n -98.61328125,\n 34.488447837809304\n ],\n [\n -97.6904296875,\n 34.34343606848294\n ],\n [\n -96.767578125,\n 34.45221847282654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Climate change will continue to be an important consideration for conservation practitioners. However, uncertainty in identifying appropriate management strategies, particularly for understudied species and regions, constrains the implementation of science-based solutions and adaptation strategies. Here, we share a decision-path approach to reduce uncertainty in climate change responses of inland fishes to inform conservation and adaptation planning. With the Fish and Climate Change database (FiCli), a comprehensive, online, public database of peer-reviewed literature on documented and projected climate impacts to inland fishes, users can identify relevant studies and associated management recommendations via geographic regions, response types (i.e., fish assemblage dynamics, demographic, distributional, evolutionary, phenological), fish taxa, and traits (e.g., thermal guilds, feeding type, parental care, habitat type) and use a suite of summary tools to make more informed decisions. For both data-rich and data-poor scenarios, we demonstrate that this approach can reduce uncertainty in understanding climate change responses. Using thermal sensitivity as an example, we also establish the utility of FiCli database to address other user-defined, management-relevant questions via supplementary analyses. This decision-path approach can be applied to rapid assessments, management decisions, and policy development and may serve as a model for other conservation decision-making processes.
","language":"English","publisher":"Wiley","doi":"10.1111/csp2.12724","usgsCitation":"Lynch, A., Myers, B., Wong, J.P., Chu, C., Tingley, R.W., Falke, J.A., Kwak, T.J., Paukert, C.P., and Krabbenhoft, T.J., 2022, Reducing uncertainty in climate change responses of inland fishes: A decision-path approach: Conservation Science and Practice, v. 4, no. 7, e12724, 15 p., https://doi.org/10.1111/csp2.12724.","productDescription":"e12724, 15 p.","ipdsId":"IP-123065","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":447669,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12724","text":"Publisher Index Page"},{"id":435839,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F6HA3M","text":"USGS data release","linkHelpText":"FiCli: Fish and Climate Change Database (2021 Update)"},{"id":403898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":846721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Myers, Bonnie 0000-0002-3170-2633","orcid":"https://orcid.org/0000-0002-3170-2633","contributorId":219702,"corporation":false,"usgs":true,"family":"Myers","given":"Bonnie","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":846722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wong, Jesse P.","contributorId":264850,"corporation":false,"usgs":false,"family":"Wong","given":"Jesse","email":"","middleInitial":"P.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":846723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chu, Cindy","contributorId":176496,"corporation":false,"usgs":false,"family":"Chu","given":"Cindy","email":"","affiliations":[],"preferred":false,"id":846724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tingley, Ralph W. III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":846725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":846726,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":846727,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":846728,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krabbenhoft, Trevor J.","contributorId":176498,"corporation":false,"usgs":false,"family":"Krabbenhoft","given":"Trevor","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":846729,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240114,"text":"70240114 - 2022 - Association of antler asymmetry with hoof disease in elk","interactions":[],"lastModifiedDate":"2023-01-27T13:18:18.965479","indexId":"70240114","displayToPublicDate":"2022-05-25T07:16:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Association of antler asymmetry with hoof disease in elk","docAbstract":"Treponeme-associated hoof disease (TAHD) is an emergent disease of elk (Cervus canadensis) in the Pacific West of the United States. Although lesions are usually restricted to the feet, anecdotal reports suggested increased prevalence of abnormal antlers in affected elk. We used hunter harvest reports for 1,688 adult male elk harvested in southwestern Washington, USA, during 2016-2018, to evaluate anecdotal reports. We used Akaike's Information Criterion to compare 18 logistic regression models describing the prevalence of asymmetrical antlers, indicated by unequal antler point counts. Our leading model (84% of model weight) described additive effects of TAHD (odds ratio = 1.91; 95% CI = [1.49, 2.44]) and maximum number of antler points. Confidence intervals overlapped zero for all other parameters, which described ecotypic, geographic, and age-related effects. Effects of physical injury on antler development have been described elsewhere; however, injuries leading to instances of antler deformity do not have population-level management implications. In contrast, we describe effects of a transmissible disease that was reported by hunters in >35% of adult male elk and was associated with an increase of ≥16 percentage points in the prevalence of gross asymmetry. Unequal point counts are quite common in elk with otherwise typical antlers and seem unlikely to attract public notice or be attributed to hoof lesions; thus, we suspect our results and anecdotal reports reflect more prominent deformities that are important to stakeholders who enjoy hunting and wildlife viewing.
Ribbed mussels, Geukensia demissa, are marsh fauna that are used in coastal management and restoration due to the ecosystem services they provide. Ribbed mussel restoration efforts may be improved with a greater understanding of the environmental drivers of ribbed mussel distribution at multiple spatial scales to predict areas where restoration could be successful. This study sought to estimate the effects of within-marsh (4 m) and landscape (500 m) factors on ribbed mussel distribution. Ribbed mussel densities were surveyed at 11 sites along the coast of Georgia, USA, and overlaid with spatial data for within-marsh factors (elevation, distance to marsh features, slope) as well as landscape factors (percent cover by subtidal creek, forest, and development within a 500-m radius). The distribution model was then validated using three previously unsurveyed marshes and explained 55% of the variance in ribbed mussel abundance. Ribbed mussel abundances and occupancy were most sensitive to changes in within-marsh factors (elevation and distance to subtidal creeks, bodies of water inundated during the full tidal cycle) but were also sensitive to landscape features (percent landcover of forests and development). The highest ribbed mussel densities were found in mid-elevation areas (~ 0.7 m NAVD88), far from subtidal creeks, and in marshes surrounded with forest and development. These results contrast with distributions in the northeastern USA, where ribbed mussels are distributed along subtidal creek banks. This work suggests that restoration may be most effective when focused on appropriate elevations and at locations away from the marsh-creek ecotone.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-022-01090-w","usgsCitation":"Annis, W., Hunter, E.A., and Carroll, J., 2022, Within-marsh and landscape features structure ribbed mussel distribution in Georgia, USA, marshes: Estuaries and Coasts, v. 45, p. 2660-2674, https://doi.org/10.1007/s12237-022-01090-w.","productDescription":"15 p.","startPage":"2660","endPage":"2674","ipdsId":"IP-132397","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s12237-022-01090-w","text":"External Repository"},{"id":480947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.27652817835495,\n 32.30818582903797\n ],\n [\n -82.27652817835495,\n 31.035143348707706\n ],\n [\n -80.80318017426605,\n 31.035143348707706\n ],\n [\n -80.80318017426605,\n 32.30818582903797\n ],\n [\n -82.27652817835495,\n 32.30818582903797\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Annis, William K.","contributorId":348800,"corporation":false,"usgs":false,"family":"Annis","given":"William K.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carroll, John M.","contributorId":348801,"corporation":false,"usgs":false,"family":"Carroll","given":"John M.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923782,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231675,"text":"fs20223020 - 2022 - New model of the Barry Arm landslide in Alaska reveals potential tsunami wave heights of 2 meters, values much lower than previously estimated","interactions":[],"lastModifiedDate":"2026-03-24T21:13:43.326319","indexId":"fs20223020","displayToPublicDate":"2022-05-24T12:10:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3020","displayTitle":"New Model of the Barry Arm Landslide in Alaska Reveals Potential Tsunami Wave Heights of 2 Meters, Values Much Lower Than Previously Estimated","title":"New model of the Barry Arm landslide in Alaska reveals potential tsunami wave heights of 2 meters, values much lower than previously estimated","docAbstract":"The retreat of Barry Glacier has contributed to the destabilization of slopes in Barry Arm, creating the possibility that a landslide could rapidly enter the fjord and trigger a tsunami.
The U.S. Geological Survey (USGS) recently released a report documenting potential tsunami wave heights in the event of a large, fast-moving landslide at the Barry Arm fiord near Prince William Sound, Alaska (Barnhart and others, 2021). This new work shows that the largest plausible wave height is smaller than initial estimates published in Dai and others (2020), but waves still represent a substantial hazard to the people who live, work, and recreate in Prince William Sound. Thus, it is important that residents and visitors remain informed about this hazard and prepare accordingly.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223020","usgsCitation":"Macías, M.A., Barnhart, K.R., Staley, D.M., 2022, New model of the Barry Arm landslide in Alaska reveals potential tsunami wave heights of 2 meters, values much lower than previously estimated: U.S. Geological Survey Fact Sheet 2022–3020, 2 p., https://doi.org/10.3133/fs20223020.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-132586","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":501486,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113066.htm","linkFileType":{"id":5,"text":"html"}},{"id":400821,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3020/images"},{"id":400820,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3020/fs20223020.xml"},{"id":400817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3020/coverthb.jpg"},{"id":400818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3020/fs20223020.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3020"},{"id":400819,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211071","linkHelpText":"Preliminary Assessment of the Wave Generating Potential from Landslides at Barry Arm, Prince William Sound, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.27423095703125,\n 61.09348761017874\n ],\n [\n -148.08883666992188,\n 61.09348761017874\n ],\n [\n -148.08883666992188,\n 61.194228075714236\n ],\n [\n -148.27423095703125,\n 61.194228075714236\n ],\n [\n -148.27423095703125,\n 61.09348761017874\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
P.O. Box 25046, MS 966
Denver, CO 80225–0046
The Everglades in south Florida supply fresh drinking water for more than 7 million people, host a National Park, and are classified as a Ramsar wetland of international distinction. Predicting trajectories of water flow and water storage changes in the future is important to managing the Congressionally authorized restoration of the Everglades. Here we describe the needed data sources and analysis approaches to build the inputs for biophysically based modeling that can protect water and ecological resources in the face of changing water management and climate conditions. A biophysical approach to modeling overland flow in the Everglades can help predict future outcomes for ecological habitat, water storage during droughts, and water conveyance during floods. The needed data include measurements of vegetation stem architecture, microtopography, and landscape pattern metrics. Stem architecture measurements present the opportunity to estimate flow roughness of distinct vegetation communities based on hydraulic principles. At a larger scale, the microtopography and the connectivity of the sloughs between ridges offer a way to quantify the effects of flow blockage and tortuous flow paths on overland flow. Combined with theory these data provide the capacity to simulate overland flow in both the historical, pre-drainage Everglades as well as in the present-day managed Everglades. Also provided are the hydrologic data, e.g., water slopes, water depths and overland flow velocities, that can be used to verify a biophysical model. Ultimately, the purpose is to anticipate how changing flow and water depth will interact with evolving vegetation and landscape conditions to influence future water availability for society and for the ecosystem, both in the Everglades and in other low-gradient floodplains.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511451.1","usgsCitation":"Harvey, J., and Choi, J., 2022, Biophysical methods and data analysis for simulating overland flow in the Everglades: ESSOAr, 51 p., https://doi.org/10.1002/essoar.10511451.1.","productDescription":"51 p.","ipdsId":"IP-140509","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447677,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/essoar.10511451.1","text":"External Repository"},{"id":435841,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQYB1O","text":"USGS data release","linkHelpText":"Biophysical Data for Simulating Overland Flow in the Everglades"},{"id":408968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.07811851388726,\n 26.46536235501027\n ],\n [\n -82.07811851388726,\n 24.821342005916392\n ],\n [\n -79.90282554513692,\n 24.821342005916392\n ],\n [\n -79.90282554513692,\n 26.46536235501027\n ],\n [\n -82.07811851388726,\n 26.46536235501027\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856292,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237019,"text":"70237019 - 2022 - Teams, networks, and networks of networks advancing our understanding and conservation of inland waters","interactions":[],"lastModifiedDate":"2022-09-27T18:26:23.163194","indexId":"70237019","displayToPublicDate":"2022-05-23T12:57:32","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Teams, networks, and networks of networks advancing our understanding and conservation of inland waters","docAbstract":"Networks are defined as groups of interconnected people and things, and by this definition, networks play a major role in the science of inland waters. In this article, we bring the latest social network research to understand and improve inland waters science and conservation outcomes. What we found is that relationships matter.\n\nDifferent teams and networks have different objectives and lifespans. Consider this: Data collection networks may persist for decades, whereas knowledge-generating teams may exist only for months. The structure of connections in a network determines how easily information or resources can flow or pass through a network, which then influences the ability of the network to accomplish work like creating and applying new knowledge, integrating knowledge across fields, or coordinating collective action.\n\nWhen independent networks designed around different purposes become connected to achieve new goals, a network of networks is formed, where each layer is a unique network defined by social, geographic, and temporal boundaries and distinct types of connections. This structure has a lot of potential for transformative work, but is especially susceptible to failure if one of the cross-network connections fails.\n\nFrom the smallest of inland waters research teams to the largest, multi-institutional, international collaborations, an understanding of how the connections between people are created and maintained can be used to set up conditions for success.","largerWorkTitle":"Encyclopedia of inland waters","language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00054-2","usgsCitation":"Read, E., Cross, J., Herman-Mercer, N.M., Oliver, S.K., and O’Reilly, C.M., 2022, Teams, networks, and networks of networks advancing our understanding and conservation of inland waters, chap. of Encyclopedia of inland waters, v. 4, p. 607-624, https://doi.org/10.1016/B978-0-12-819166-8.00054-2.","productDescription":"18 p.","startPage":"607","endPage":"624","ipdsId":"IP-126937","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","edition":"2nd","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tockner, Klement","contributorId":224174,"corporation":false,"usgs":false,"family":"Tockner","given":"Klement","email":"","affiliations":[{"id":40838,"text":"FWF Austrian Science Fund","active":true,"usgs":false}],"preferred":false,"id":853141,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Mehner, Thomas","contributorId":272917,"corporation":false,"usgs":false,"family":"Mehner","given":"Thomas","email":"","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":853142,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Read, Emily 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":190110,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":853096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Jennifer 0000-0002-5582-4192","orcid":"https://orcid.org/0000-0002-5582-4192","contributorId":297016,"corporation":false,"usgs":false,"family":"Cross","given":"Jennifer","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":853098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Reilly, Catherine M.","contributorId":150334,"corporation":false,"usgs":false,"family":"O’Reilly","given":"Catherine","email":"","middleInitial":"M.","affiliations":[{"id":18004,"text":"Illinois State University","active":true,"usgs":false}],"preferred":false,"id":853100,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237245,"text":"70237245 - 2022 - Hydrological cycle and water budgets","interactions":[],"lastModifiedDate":"2022-10-05T14:33:45.429244","indexId":"70237245","displayToPublicDate":"2022-05-23T09:28:40","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hydrological cycle and water budgets","docAbstract":"In this chapter, we describe the hydrological cycle and each of its components (pools). The hydrological cycle is important to the transport and cycling of nutrients and energy. Quantifying the various components of the hydrological cycle, referred to as constructing water budget for a defined area, is an important framework for wise and equitable water management. The hydrological cycle has changed as the result of human activity affecting specific components of the water budget and the movement of water between the components. Water budgets are provided for two defined areas: the earth as a whole and the watershed of a small inland lake.
Given a specific area with well-defined boundaries, constructing a water budget consists of quantifying the amount and relationships among inflow, outflow, and change in storage within a defined area of the hydrological cycle, water budgets relevant to inland waters and aquatic ecosystems, and how the hydrological cycle and water budgets have been affected by anthropogenic modifications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00008-6","usgsCitation":"Robertson, D., Perlman, H.A., and Narisimhan, T.N., 2022, Hydrological cycle and water budgets, chap. of Encyclopedia of inland waters, p. 19-27, https://doi.org/10.1016/B978-0-12-819166-8.00008-6.","productDescription":"9 p.","startPage":"19","endPage":"27","ipdsId":"IP-121572","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Second Edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perlman, Howard A. 0000-0002-2392-0737","orcid":"https://orcid.org/0000-0002-2392-0737","contributorId":297327,"corporation":false,"usgs":true,"family":"Perlman","given":"Howard","email":"","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Narisimhan, T. N.","contributorId":297329,"corporation":false,"usgs":false,"family":"Narisimhan","given":"T.","email":"","middleInitial":"N.","affiliations":[{"id":33770,"text":"University of California at Berkeley","active":true,"usgs":false}],"preferred":false,"id":853824,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243254,"text":"70243254 - 2022 - An introduction to current climate projections and their use in climate impacts research","interactions":[],"lastModifiedDate":"2023-05-05T13:37:14.428567","indexId":"70243254","displayToPublicDate":"2022-05-23T08:28:07","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"An introduction to current climate projections and their use in climate impacts research","docAbstract":"Using climate projections to evaluate future climate impacts and their associated risks requires a background knowledge of the nature of climate change, use of climate models to develop future projections, and knowledge of how to address climate scenario uncertainty. This chapter provides an overview of climate and climate change, some of the foundational climate science that underlies current climate change assessments, and a brief introduction to climate models and climate scenario uncertainty. Global projections of temperature and precipitation changes from the recent Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) and a brief comparison to the prior assessment (AR5) are provided. The main sources of uncertainty in these projections include climate variability, climate model differences and treatment of scientific knowledge gaps, and greenhouse gas (GHG) emissions. When projections are downscaled to local resolution, downscaling is an additional source of uncertainty. These uncertainties can be incorporated in assessments of climate impacts by choosing a range of scenarios that directly address the sources of uncertainty. Evaluating the likelihood of a given climate impact on animal health or management strategies requires consideration of the main sources of climate projection uncertainties. Adaptation requires consideration of global-to-regional contexts of climate changes and impacts, but also adaptive capacity.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Climate change and animal health","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9781003149774-1","usgsCitation":"Littell, J., 2022, An introduction to current climate projections and their use in climate impacts research, chap. 1 of Climate change and animal health, p. 1-21, https://doi.org/10.1201/9781003149774-1.","productDescription":"21 p.","startPage":"1","endPage":"21","ipdsId":"IP-135325","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Stephen, Craig","contributorId":168939,"corporation":false,"usgs":false,"family":"Stephen","given":"Craig","email":"","affiliations":[],"preferred":false,"id":871855,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Duncan, Colleen G.","contributorId":15512,"corporation":false,"usgs":false,"family":"Duncan","given":"Colleen","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":871856,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871684,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236937,"text":"70236937 - 2022 - Intrapopulation differences in polar bear movement and step selection patterns","interactions":[],"lastModifiedDate":"2022-09-22T11:38:20.457404","indexId":"70236937","displayToPublicDate":"2022-05-23T06:34:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Intrapopulation differences in polar bear movement and step selection patterns","docAbstract":"The spatial ecology of individuals often varies within a population or species. Identifying how individuals in different classes interact with their environment can lead to a better understanding of population responses to human activities and environmental change and improve population estimates. Most inferences about polar bear (Ursus maritimus) spatial ecology are based on data from adult females due to morphological constraints on applying satellite radio collars to other classes of bears. Recent studies, however, have provided limited movement data for adult males and sub-adults of both sexes using ear-mounted and glue-on tags. We evaluated class-specific movements and step selection patterns for polar bears in the Chukchi Sea subpopulation during spring.
We developed hierarchical Bayesian models to evaluate polar bear movement (i.e., step length and directional persistence) and step selection at the scale of 4-day step lengths. We assessed differences in movement and step selection parameters among the three classes of polar bears (i.e., adult males, sub-adults, and adult females without cubs-of-the-year).
Adult males had larger step lengths and less directed movements than adult females. Sub-adult movement parameters did not differ from the other classes but point estimates were most similar to adult females. We did not detect differences among polar bear classes in step selection parameters and parameter estimates were consistent with previous studies.
Our findings support the use of estimated step selection patterns from adult females as a proxy for other classes of polar bears during spring. Conversely, movement analyses indicated that using data from adult females as a proxy for the movements of adult males is likely inappropriate. We recommend that researchers consider whether it is valid to extend inference derived from adult female movements to other classes, based on the questions being asked and the spatial and temporal scope of the data. Because our data were specific to spring, these findings highlight the need to evaluate differences in movement and step selection during other periods of the year, for which data from ear-mounted and glue-on tags are currently lacking.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00326-5","usgsCitation":"Wilson, R., St Martin, M., Regehr, E.V., and Rode, K.D., 2022, Intrapopulation differences in polar bear movement and step selection patterns: Movement Ecology, v. 10, 25, 12 p., https://doi.org/10.1186/s40462-022-00326-5.","productDescription":"25, 12 p.","ipdsId":"IP-135708","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":447686,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00326-5","text":"Publisher Index Page"},{"id":407206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":852744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"St Martin, Michelle","contributorId":296903,"corporation":false,"usgs":false,"family":"St Martin","given":"Michelle","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":852745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":852746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":852747,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252816,"text":"70252816 - 2022 - Environmental drivers of cyanobacterial abundance and cyanotoxin production in backwaters of the Upper Mississippi River","interactions":[],"lastModifiedDate":"2024-04-08T23:47:34.73073","indexId":"70252816","displayToPublicDate":"2022-05-22T08:46:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Environmental drivers of cyanobacterial abundance and cyanotoxin production in backwaters of the Upper Mississippi River","docAbstract":"High densities of cyanobacteria in aquatic ecosystems can cause impacts to ecosystem services because they serve as a poor-quality food resource, produce toxins and can indirectly cause a variety of other negative impacts to water quality. There are many hypotheses about the potential environmental drivers of variation in cyanobacterial abundance and toxicity, but these hypotheses have rarely been considered in combination and rarely been examined in large river ecosystems. Here we use monthly data from backwater habitats of the Upper Mississippi River (UMR) to evaluate associations between environmental conditions and cyanobacterial abundance and toxicity (microcystin and anatoxin-a) that would be expected based on several hypotheses. Backwaters in the Mississippi River vary in flushing rate, temperature, turbidity, nutrient availability, water depth and vegetative cover. We find support for hypotheses that suggest physical conditions in backwaters (flushing rate, temperature, turbidity, rooted vegetation cover and water depth) and nutrient availability influence cyanobacterial abundance and toxicity. We then used structural equation modeling to incorporate several hypotheses into a causal modeling framework, which indicated that backwater connectivity (flushing) strongly influences cyanobacterial abundance via the regulation of water temperature, and that nutrient availability strongly influences the presence of microcystin concentrations above our detection limit. Our data suggest that management of backwater connectivity could influence cyanobacterial abundance and toxicity in UMR backwaters. Reconnecting backwaters (via alteration of levees) could serve as a local adaptation to minimize the effects of climate change and excessive nutrient loading.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3987","usgsCitation":"Giblin, S.M., Larson, J.H., and King, J.D., 2022, Environmental drivers of cyanobacterial abundance and cyanotoxin production in backwaters of the Upper Mississippi River: River Research and Applications, v. 38, no. 6, p. 1115-1128, https://doi.org/10.1002/rra.3987.","productDescription":"14 p.","startPage":"1115","endPage":"1128","ipdsId":"IP-134311","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":427556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Stares","state":"Wisconsin","otherGeospatial":"Blue Lake, Great River Backwater, Indian Slough, Lizzy Paul's Pond, Mertes Lake, Second Lake, Stoddard Backwater, Trempealeau Wildlife Refuge, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.7533542542472,\n 44.157127527506105\n ],\n [\n -91.7533542542472,\n 43.58283679178368\n ],\n [\n -90.94310098095912,\n 43.58283679178368\n ],\n [\n -90.94310098095912,\n 44.157127527506105\n ],\n [\n -91.7533542542472,\n 44.157127527506105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Giblin, Shawn M.","contributorId":335419,"corporation":false,"usgs":false,"family":"Giblin","given":"Shawn","email":"","middleInitial":"M.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":898322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":898323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Jeremy D.","contributorId":335420,"corporation":false,"usgs":false,"family":"King","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":898324,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232532,"text":"70232532 - 2022 - Cryptic extinction risk in a western Pacific lizard radiation","interactions":[],"lastModifiedDate":"2022-08-02T15:06:39.035711","indexId":"70232532","displayToPublicDate":"2022-05-22T06:37:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Cryptic extinction risk in a western Pacific lizard radiation","docAbstract":"Cryptic ecologies, the Wallacean Shortfall of undocumented species’ geographical ranges and the Linnaean Shortfall of undescribed diversity, are all major barriers to conservation assessment. When these factors overlap with drivers of extinction risk, such as insular distributions, the number of threatened species in a region or clade may be underestimated, a situation we term ‘cryptic extinction risk’. The genus Lepidodactylus is a diverse radiation of insular and arboreal geckos that occurs across the western Pacific. Previous work on Lepidodactylus showed evidence of evolutionary displacement around continental fringes, suggesting an inherent vulnerability to extinction from factors such as competition and predation. We sought to (1) comprehensively review status and threats, (2) estimate the number of undescribed species, and (3) estimate extinction risk in data deficient and candidate species, in Lepidodactylus. From our updated IUCN Red List assessment, 60% of the 58 recognized species are threatened (n = 15) or Data Deficient (n = 21), which is higher than reported for most other lizard groups. Species from the smaller and isolated Pacific islands are of greatest conservation concern, with most either threatened or Data Deficient, and all particularly vulnerable to invasive species. We estimated 32 undescribed candidate species and linear modelling predicted that an additional 18 species, among these and the data deficient species, are threatened with extinction. Focusing efforts to resolve the taxonomy and conservation status of key taxa, especially on small islands in the Pacific, is a high priority for conserving this remarkably diverse, yet poorly understood, lizard fauna. Our data highlight how cryptic ecologies and cryptic diversity combine and lead to significant underestimation of extinction risk.
Coastal wetlands are defined herein as inundated, vegetated ecosystems with hydrology, and biogeochemistry influenced by sea levels, at timescales of tides to millennia. Coastal wetlands are necessary components of global greenhouse gas estimation and scenario modeling, both for continental and oceanic mass balances. The carbon pools and fluxes on coastal lands, especially those influenced by tidal drivers and sea level rise, are distinct in their magnitude, rates, and uncertainties. We describe herein the pathways taken for a US scale estimation of blue carbon based on annual timesteps and bottom-up modeling, as appropriate for the first effort to include coastal wetlands in the Intergovernmental Panel on Climate Change (IPCC) guidelines for a National Greenhouse Gas Inventory (NGGI). As such, we summarize multiple efforts to reconcile mapping, modeling, and measurement issues and we report the assumptions we made based on data availability. Provided as requested feedback to the IPCC.
Subsidiary Body for Scientific and Technological Advice (SBSTA) evaluation of guidance criteria, these analyses synergistically point scientists, practitioners, and policy makers toward the greatest uncertainties to address in future assessments: coastal wetland methane emissions and carbon dioxide emissions associated with the fate of eroded soil. This is a story of what was learned in the 2014–2018 NASA Carbon Monitoring System project (https://carbon.nasa.gov/cgi-bin/cms_projects.pl), how it informs “good practice” (IPCC 2006) in reporting coastal wetland emissions and removals, and where it points scientifically toward data needs at different temporal and spatial scales.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","publisherLocation":"Balancing greenhouse gas budgets: Accounting for natural and anthropogenic flows of CO2 and other trace gases","doi":"10.1016/B978-0-12-814952-2.00001-0","usgsCitation":"Windham-Myers, L., Holmquist, J., Kroeger, K.D., and Troxler, T., 2022, Greenhouse gas balances in coastal ecosystems: Current challenges in “blue carbon” estimation and significance to national greenhouse gas inventories, p. 403-425, https://doi.org/10.1016/B978-0-12-814952-2.00001-0.","productDescription":"23 p.","startPage":"403","endPage":"425","ipdsId":"IP-123602","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":406543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmquist, James R.","contributorId":272628,"corporation":false,"usgs":false,"family":"Holmquist","given":"James R.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":832253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":832254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Troxler, Tiffany G.","contributorId":272629,"corporation":false,"usgs":false,"family":"Troxler","given":"Tiffany G.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":832255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231754,"text":"70231754 - 2022 - A critical review of bioaccumulation and biotransformation of organic chemicals in birds","interactions":[],"lastModifiedDate":"2022-05-25T15:29:48.479448","indexId":"70231754","displayToPublicDate":"2022-05-20T10:20:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5344,"text":"Reviews of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"A critical review of bioaccumulation and biotransformation of organic chemicals in birds","docAbstract":"A literature review of bioaccumulation and biotransformation of organic chemicals in birds was undertaken, aiming to support scoping and prioritization of future research. The objectives were to characterize available bioaccumulation/biotransformation data, identify knowledge gaps, determine how extant data can be used, and explore the strategy and steps forward. An intermediate approach balanced between expediency and rigor was taken given the vastness of the literature. Following a critical review of > 500 peer-reviewed studies, > 25,000 data entries and 2 million information bytes were compiled on > 700 organic compounds for ~ 320 wild species and 60 domestic breeds of birds. These data were organized into themed databases on bioaccumulation and biotransformation, field survey, microsomal enzyme activity, metabolic pathway, and bird taxonomy and diet. Significant data gaps were identified in all databases at multiple levels. Biotransformation characterization was largely fragmented over metabolite/pathway identification and characterization of enzyme activity or biotransformation kinetics. Limited biotransformation kinetic data constrained development of an avian biotransformation model. A substantial shortage of in vivo biotransformation kinetics has been observed as most reported rate constants were derived in vitro. No metric comprehensively captured all key contaminant classes or chemical groups to support broad-scope modeling of bioaccumulation or biotransformation. However, metrics such as biota-feed accumulation factor, maximum transfer factor, and total elimination rate constant were more readily usable for modeling or benchmarking than other reviewed parameters. Analysis demonstrated the lack of bioaccumulation/biotransformation characterization of shorebirds, seabirds, and raptors. In the study of bioaccumulation and biotransformation of organic chemicals in birds, this review revealed the need for greater chemical and avian species diversity, chemical measurements in environmental media, basic biometrics and exposure conditions, multiple tissues/matrices sampling, and further exploration on biotransformation. Limitations of classical bioaccumulation metrics and current research strategies used in bird studies were also discussed. Forward-looking research strategies were proposed: adopting a chemical roadmap for future investigations, integrating existing biomonitoring data, gap-filling with non-testing approaches, improving data reporting practices, expanding field sampling scopes, bridging existing models and theories, exploring biotransformation via avian genomics, and establishing an online data repository.
","language":"English","publisher":"Springer Link","doi":"10.1007/s44169-021-00007-1","usgsCitation":"Kuo, D.T., Rattner, B.A., Marteinson, S.C., Letcher, R.J., Fernie, K.J., Treu, G., Deutsch, M., Johnson, M.S., Deglin, S., and Embry, M., 2022, A critical review of bioaccumulation and biotransformation of organic chemicals in birds: Reviews of Environmental Contamination and Toxicology, v. 260, 6, 22 p., https://doi.org/10.1007/s44169-021-00007-1.","productDescription":"6, 22 p.","ipdsId":"IP-125200","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s44169-021-00007-1","text":"Publisher Index Page"},{"id":401051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"260","noUsgsAuthors":false,"publicationDate":"2022-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuo, Dave T. F.","contributorId":292043,"corporation":false,"usgs":false,"family":"Kuo","given":"Dave","email":"","middleInitial":"T. F.","affiliations":[{"id":62810,"text":"City University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":843691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":843713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marteinson, Sarah C.","contributorId":292044,"corporation":false,"usgs":false,"family":"Marteinson","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":843714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Letcher, Robert J.","contributorId":176209,"corporation":false,"usgs":false,"family":"Letcher","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":843715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernie, Kim J.","contributorId":211241,"corporation":false,"usgs":false,"family":"Fernie","given":"Kim","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":843716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Treu, Gabriele","contributorId":218385,"corporation":false,"usgs":false,"family":"Treu","given":"Gabriele","email":"","affiliations":[{"id":39836,"text":"Leibniz Institute for Zoo and Wildlife Research","active":true,"usgs":false}],"preferred":false,"id":843717,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Deutsch, Markus","contributorId":292048,"corporation":false,"usgs":false,"family":"Deutsch","given":"Markus","email":"","affiliations":[{"id":62812,"text":"Umweltbundesamt","active":true,"usgs":false}],"preferred":false,"id":843718,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Mark S.","contributorId":86058,"corporation":false,"usgs":true,"family":"Johnson","given":"Mark","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":843719,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Deglin, Sandrine","contributorId":292050,"corporation":false,"usgs":false,"family":"Deglin","given":"Sandrine","email":"","affiliations":[{"id":62814,"text":"Health and Environmental Science Institutue","active":true,"usgs":false}],"preferred":false,"id":843720,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Embry, Michelle","contributorId":176356,"corporation":false,"usgs":false,"family":"Embry","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":843721,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70231755,"text":"70231755 - 2022 - Assessing climate change impacts on Pacific salmon using bioenergetics and spatiotemporal explicit river temperature predictions under varying riparian conditions","interactions":[],"lastModifiedDate":"2022-05-25T15:00:58.75195","indexId":"70231755","displayToPublicDate":"2022-05-20T09:56:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing climate change impacts on Pacific salmon using bioenergetics and spatiotemporal explicit river temperature predictions under varying riparian conditions","docAbstract":"Pacific salmon and trout populations are affected by timber harvest, the removal and alteration of riparian vegetation, and the resulting physical changes to water quality, temperature, and associated delivery of high-quality terrestrial prey. Juvenile salmon and trout growth, a key predictor of survival, is poorly understood in the context of current and future (climate-change mediated) conditions, with resource managers needing information on how land use will impact future river conditions for these commercially and culturally important species. We used the Heat Source water temperature modeling framework to develop a spatiotemporal model to assess how riparian canopy and vegetation preservation and addition could influence river temperatures under future climate predictions in a coastal river fed by a moraine-dammed lake: the Quinault River in Washington State. The model predicted higher water temperatures under future carbon emission projections, representative concentration pathway (RCP) 4.5 and 8.5, with varying magnitude based on different riparian vegetation scenarios. We used the daily average temperature output from these scenarios to predict potential juvenile fish growth using the Wisconsin bioenergetics model. A combination of riparian vegetation removal and continued high carbon emissions resulted in a predicted seven-day average daily maximum temperature (7DADM) increase of 1.7°C in the lower river by 2080; increases in riparian shading mitigate this 7DADM increase to only 0.9°C. Under the current thermal regime, bioenergetics modeling predicts juvenile fish lose weight in the lower river; this loss of potential growth worsens by an average of 20–83% in the lower river by 2080, increasing with the loss of riparian shading. This study assess the impact of riparian vegetation management on future thermal habitat for Pacific salmon and trout under warming climates and provide a useful spatially explicit modeling framework that managers can use to make decisions regarding riparian vegetation management and its mechanistic impact to water temperature and rearing juvenile fish.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0266871","usgsCitation":"Spanjer, A.R., Gendaszek, A.S., Wulfkuhle, E.J., Black, R.W., and Jaeger, K.L., 2022, Assessing climate change impacts on Pacific salmon using bioenergetics and spatiotemporal explicit river temperature predictions under varying riparian conditions: PLoS ONE, v. 17, no. 5, e0266871, 25 p., https://doi.org/10.1371/journal.pone.0266871.","productDescription":"e0266871, 25 p.","ipdsId":"IP-119800","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":447705,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0266871","text":"Publisher Index Page"},{"id":435843,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XGI6GS","text":"USGS data release","linkHelpText":"Quinault River water temperature and salmon bioenergetics model data"},{"id":435842,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GSX4QE","text":"USGS data release","linkHelpText":"Water temperature and riparian vegetation survey data for the lower Quinault River, WA for select periods in 2018 and 2019"},{"id":401045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Quinault, Quinault River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.31854248046875,\n 47.292270864380086\n ],\n [\n -123.82553100585936,\n 47.292270864380086\n ],\n [\n -123.82553100585936,\n 47.50421439972969\n ],\n [\n -124.31854248046875,\n 47.50421439972969\n ],\n [\n -124.31854248046875,\n 47.292270864380086\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Spanjer, Andrew R. 0000-0002-7288-2722 aspanjer@usgs.gov","orcid":"https://orcid.org/0000-0002-7288-2722","contributorId":150395,"corporation":false,"usgs":true,"family":"Spanjer","given":"Andrew","email":"aspanjer@usgs.gov","middleInitial":"R.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843702,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulfkuhle, Elyse J.","contributorId":207132,"corporation":false,"usgs":false,"family":"Wulfkuhle","given":"Elyse","email":"","middleInitial":"J.","affiliations":[{"id":37427,"text":"Quinault Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":843703,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843704,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843705,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231812,"text":"70231812 - 2022 - Nearshore bathymetric changes along the Alaska Beaufort Sea coast and possible physical drivers","interactions":[],"lastModifiedDate":"2022-05-27T13:29:13.252547","indexId":"70231812","displayToPublicDate":"2022-05-20T08:24:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Nearshore bathymetric changes along the Alaska Beaufort Sea coast and possible physical drivers","docAbstract":"Erosion rates along Alaska's Beaufort Sea coast, among the highest in the world, are negatively impacting communities, industrial and military infrastructure, and wildlife habitat. Decreasing maximal winter ice extent and increasing summer open water duration and extent in the Beaufort Sea may be making the coast more vulnerable to destructive storm waves than during recent, colder, icier decades. Previous studies of Beaufort Sea coastal change have been limited to subaerial analyses of the shoreline. Here we describe nearshore seafloor change by comparing post-World War II (WWII) (1945-53) bathymetry data to recently acquired (1985–2018) bathymetry data and relate the observed seafloor change to adjacent shoreline change near Utqiagvik, within Stefansson Sound, and immediately west of Barter Island and Kaktovik. Within the Utqiagvik region, seabed erosion was generally highest (>1.0 m of loss) offshore of Point Barrow and along the eastern end of the Tapkaluk Islands, while there were lesser amounts of deposition (<0.5 m of gain) within the protected waters of Elson Lagoon. Sedimentation was generally highest offshore of Point Barrow, in a region of converging currents, and on the landward side of the barrier islands and spits fronting Elson Lagoon, which is likely related to a regional trend of westerly sediment transport and landward migration of the barrier islands. Within Stefansson Sound, perhaps the most notable changes from post-WWII bathymetry data compared to recent data are a switch from mixed, low erosion and deposition in 1997 to low deposition (<0.5 m) in 2018 east of the Boulder Patch, a switch from low erosion in 1997 to neutral depth change in 2018 in the channel between the north and south Boulder Patch areas, and higher deposition from 1997 to 2018 landward of the rapidly retreating barrier islands along the Sound's northern border. At Barter Island, high erosion near north-facing shorelines and high deposition near west-facing shorelines generally matched shoreline changes. One of our goals is to identify possible processes responsible for the depth changes we quantified. Using simple metrics that relate sediment characteristics with modeled waves and non-wave induced currents, we show that sediment resuspension and transport by both wave and non-wave driven currents likely contribute to the overall patterns of change within the ∼13 m isobath along the open coast, and that the influence of wave action affecting sediment transport is expanding seaward.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2022.104745","usgsCitation":"Zimmermann, M., Erikson, L.H., Gibbs, A.E., Prescott, M., Escarzaga, S.M., Tweedie, C.E., Kasper, J., and Duvoy, P.X., 2022, Nearshore bathymetric changes along the Alaska Beaufort Sea coast and possible physical drivers: Continental Shelf Research, v. 242, 104745, 15 p., https://doi.org/10.1016/j.csr.2022.104745.","productDescription":"104745, 15 p.","ipdsId":"IP-132441","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447707,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.csr.2022.104745","text":"Publisher Index Page"},{"id":401293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.00390625,\n 69.33383491964828\n ],\n [\n -140.9326171875,\n 69.33383491964828\n ],\n [\n -140.9326171875,\n 72.39570570653261\n ],\n [\n -164.00390625,\n 72.39570570653261\n ],\n [\n -164.00390625,\n 69.33383491964828\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmermann, Mark 0000-0002-5786-3814","orcid":"https://orcid.org/0000-0002-5786-3814","contributorId":200380,"corporation":false,"usgs":false,"family":"Zimmermann","given":"Mark","email":"","affiliations":[],"preferred":false,"id":843888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":843889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":843890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prescott, Megan M.","contributorId":292137,"corporation":false,"usgs":false,"family":"Prescott","given":"Megan M.","affiliations":[{"id":62835,"text":"Lynker Technologies, Under contract to Alaska Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":843891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Escarzaga, Stephen M.","contributorId":279732,"corporation":false,"usgs":false,"family":"Escarzaga","given":"Stephen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":843892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tweedie, Craig E.","contributorId":200176,"corporation":false,"usgs":false,"family":"Tweedie","given":"Craig","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":843893,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":843894,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duvoy, Paul X.","contributorId":292138,"corporation":false,"usgs":false,"family":"Duvoy","given":"Paul","email":"","middleInitial":"X.","affiliations":[{"id":62836,"text":"Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, AK, USA","active":true,"usgs":false}],"preferred":false,"id":843895,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70246306,"text":"70246306 - 2022 - Value of information and decision pathways: Concepts and case studies","interactions":[],"lastModifiedDate":"2023-06-30T11:44:53.979969","indexId":"70246306","displayToPublicDate":"2022-05-20T06:43:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16138,"text":"Frontiers in Environmental Science (Environmental Economics and Management)","active":true,"publicationSubtype":{"id":10}},"title":"Value of information and decision pathways: Concepts and case studies","docAbstract":"Information used in decision making arises from the structuring of observations and data. The collection, dissemination, and use of information has monetary and non-monetary costs (e.g., competition for attention) and necessitates trade-offs. Understanding the benefits of having information (i.e., the value of information, VOI), including resulting societal outcomes, is useful to information producers/funders and decision makers. Using theory, use cases, and hypotheticals, we describe how information (e.g., geospatial information) is valued and incorporated in decisions and actions related to managing natural resources, environments, and the impacts of natural and anthropogenic hazards. We discuss the nature of information and how it relates to models (conceptual, mental, scientific), beliefs, knowledge, and economic analyses. VOI approaches and behavioral factors that potentially affect information use and value are summarized. Framing of information and VOI through data to decision pathways (DDPs) at first simplifies understanding, then illustrates the benefits of information, and the human and societal challenges encountered in valuing and using it. We present approaches to overcome these challenges. Our transdisciplinary analysis concludes with a summary of critical issues affecting DDPs and VOI, and suggestions for improving both economic analyses and the actionability and use of information.
Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108990","usgsCitation":"Schaeffer, B., Urquhart, E., Coffer, M., Salls, W., Stumpf, R., Loftin, K.A., and Werdell, P., 2022, Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales: Ecological Indicators, v. 140, 108990, 14 p., https://doi.org/10.1016/j.ecolind.2022.108990.","productDescription":"108990, 14 p.","ipdsId":"IP-140263","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":447718,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108990","text":"Publisher Index Page"},{"id":400909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": 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The airborne electromagnetic data were used to develop resistivity models of the subsurface, where the mean depth of investigation is about 300 meters below the land surface and thus characterizes parts of the Kern River Formation and overlying sediments. Resistivity models along with water table elevation, historical total dissolved solids measurements of water samples from wells, well lithologic records, borehole geophysical logs, and mapped surface geology were used to develop an understanding of local hydrogeologic controls on resistivity. Interpretation of these data indicate the resistivity structure primarily reflects the general lithologic character and geologic structure of the study area, with more subtle influences from variations in saturation and salinity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/dr1155","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Zamudio, K.D., Ball, L.B., and Stephens, M.J., 2022, Airborne electromagnetic survey results near the Poso Creek oil field, San Joaquin Valley, California, fall 2016: U.S. Geological Survey Data Report 1155, 55 p., https://doi.org/10.3133/dr1155.","productDescription":"Report: vii, 59 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-131476","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501206,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113076.htm","linkFileType":{"id":5,"text":"html"}},{"id":400702,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1155/dr1155.xml"},{"id":400701,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1155/images"},{"id":400662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1155/coverthb.jpg"},{"id":400663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1155/dr1155.pdf","text":"Report","size":"14.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1155"},{"id":400664,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H9AVZY","text":"USGS data release","linkHelpText":"Airborne electromagnetic and magnetic survey data, southeastern San Joaquin Valley near Cawelo, California, 2016"}],"country":"United States","state":"California","otherGeospatial":"Poso Creek Oil Field, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.2,\n 35.4667\n ],\n [\n -119.0667,\n 35.4667\n ],\n [\n -119.0667,\n 35.5833\n ],\n [\n -119.2,\n 35.5833\n ],\n [\n -119.2,\n 35.4667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
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Wind has been the dominant agent of landscape modification on Mars for the past ~3 billion years. Among the diversity of features formed by aeolian abrasion on the surface of Mars are periodic bedrock ridges (PBRs), a relatively recently recognized class of erosional bedforms on Mars for which Earth analogues are rare. Gale crater, the field site for NASA’s Mars Science Laboratory Curiosity rover since it landed there in 2012, contains a diverse and extensive record of aeolian deposition and erosion. This study focuses on a series of periodic, linear bedrock ridges that occur within the Fe/Mg-smectite clay-bearing Glen Torridon region of Aeolis Mons (informally Mount Sharp). During Curiosity’s exploration of the Glen Torridon region between sols ~2300-3080, the rover drove through this field of ridges, providing the first opportunity for the in situ observation of these enigmatic erosional features. This study characterizes the Glen Torridon ridges using orbiter and rover data to determine their morphology, spatial distribution, compositional and material properties, and association with other aeolian features in the area. Based on these observations, the Glen Torridon ridges are interpreted to be consistent with an origin as wind-eroded periodic bedrock ridges carved during the most recent exhumation of Mount Sharp into the present-day mound. Although there is evidence for multidirectional winds in the Glen Torridon region based on the orientation of modern ripples, megaripples, TARs and other bedrock indicators, the consistent orientation of the Glen Torridon ridges, coupled with morphologic asymmetries within the ridges, support formation and elongation of the Glen Torridon PBRs forms parallel to a net regional northerly wind direction in and around Gale crater.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JE007096","usgsCitation":"Stack, K.M., Dietrich, W.E., Lamb, M.P., Sullivan, R., Christian, J.R., Newman, C.E., O’Connell-Cooper, C., Sneed, J.W., Day, M.D., Baker, M., Arvidson, R.A., Fedo, C.M., Khan, S., Williams, R., Bennett, K.A., Bryk, A.B., Cofield, S., Edgar, L.A., Fox, V.F., Fraeman, A.A., House, C.H., Rubin, D.M., Sun, V.Z., and Van Beek, J., 2022, Orbital and in-situ investigation of periodic bedrock ridges in Glen Torridon, Gale Crater, Mars: Journal of Geophysical Research E: Planets, v. 127, no. 6, e2021JE007096, 33 p., https://doi.org/10.1029/2021JE007096.","productDescription":"e2021JE007096, 33 p.","ipdsId":"IP-133144","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447723,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021je007096","text":"External Repository"},{"id":421607,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale Crater, Glen Torridon, Mars","volume":"127","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Stack, K. 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A changing climate and the effects of river and coastal management are altering freshwater inflows into productive oyster areas, causing more frequent and extreme salinity exposure. Although eastern oysters are tolerant to a wide range of salinity means and variations, more frequent and extreme exposure to low salinity (<5‰) impacts oyster populations and aquaculture operations. This study assessed four Louisiana eastern oyster stocks to explore population-specific responses to low-salinity exposure. Hatchery-produced progeny (10–25 mm) were deployed in baskets kept off-bottom on longline systems in a low-salinity (mean ± 1 standard error of the mean daily salinity = 8.7 ± 0.2‰; range = 1.2–19.0‰) and a moderate-salinity (16.8 ± 0.3‰; 4.8–30.0‰) environment for 1 year, beginning in December 2019, with growth and mortality determined monthly. Significant differences in cumulative mortality between stocks at the end of the study were found at the low-salinity site, with the greatest increase in cumulative mortality occurring mid-July to mid-August. Mortality differences between stocks suggest that some eastern oyster populations (i.e., stocks) may be better suited to low salinity or low-salinity events than others. This difference may be attributed to similarity between site of origin and grow-out site conditions and/or to greater salinity variability and therefore higher phenotypic plasticity in some eastern oyster populations compared with others. The identification of oyster stocks able to survive under extreme low-salinity conditions may facilitate the development of “low-salinity-tolerant” broodstock to support aquaculture in areas experiencing and predicted to experience low-salinity events.
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