A U.S. Geological Survey-led assessment of data gaps and science needs across the Great Lakes ecosystem indicated the following:
• Expanded data collection or monitoring would provide basic ecosystem, social, and public health data to manage the Great Lakes system and to develop and test models and decision support tools.
• New science and advanced technologies (for example, sensors and high-performance computing capability) would improve the understanding of critical threats, such as harmful algae blooms and high-water levels.
Although there is significant scientific knowledge in specific areas or for specific topics, managers could use improved models and decision support tools, strengthened by extensive data collection and developed at multiple scales, to better inform decision making in the future. Enhanced coordination of agency efforts and associated data collection across data types (for example, prey fish populations and water levels) is needed to effectively manage the Great Lakes.
This report highlights the data gaps; benefits of better, more structured coordination; and areas of concern specifically related to data collection/measurement and science efforts. It summarizes and analyzes stakeholder feedback and information from review of scientific literature. Finally, the report outlines steps necessary to create an integrated Great Lakes science plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211096","usgsCitation":"Carl, L.M., Hortness, J.E., and Strach, R.M., 2021, U.S. Geological Survey Great Lakes Science Forum—Summary of remaining data and science needs and next steps: U.S. Geological Survey Open-File Report 2021–1096, 4 p., https://doi.org/10.3133/ofr20211096.","productDescription":"iii, 4 p.","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-133589","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":390007,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.xml","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1096 xml"},{"id":390006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.pdf","text":"Report","size":"655 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1096"},{"id":390005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1096/coverthb.jpg"}],"contact":"Director, Midwest Regional Director’s Office
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop an example of a prototype tool for optimizing tidal marsh management decisions for selected marsh management units at the Rachel Carson National Wildlife Refuge in Maine. The goal was to create a prototype that could be available for future implementation. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of seven marsh management units within the refuge and estimated the outcomes of each action in terms of regional performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale.
Management costs were estimated using limited available information, and estimated costs of individual management actions reflected relative differences among actions rather than actual expected expenditures. Results from this prototype showed how, for the objectives, actions, and estimated outcomes used for this example, total management benefits may increase consistently up to a certain estimated cost, and may continue to increase, at a lower rate, with further expenditures. Potential management actions in optimal portfolios at moderate total estimated costs included breaching or removing dikes, roads, or embankments; planting Spartina alterniflora (smooth cordgrass); and digging runnels, or shallow creeks, on the marsh platform to improve surface-water drainage. Potential management actions in optimal portfolios at high estimated costs (for example, up to $550,000) included breaching embankments to restore tidal exchange followed by planting salt marsh vegetation. The potential management benefits were derived from predicted increases in the numbers of tidal marsh obligate birds and spiders (as an indicator of trophic health), and expected improvement in the capacity of marsh elevation to keep pace with sea-level rise and reduced duration of marsh-surface inundation. The prototype presented here does not resolve current management decisions; rather, it provides a framework for decision making at the Rachel Carson National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., O’Brien, K.M., Benvenuti, B., and Kleinert, R., 2021, Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1080, 35 p., https://doi.org/10.3133/ofr20211080.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126540","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1080/coverthb.jpg"},{"id":389744,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.pdf","text":"Report","size":"4.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1080"},{"id":389737,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":389746,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1080/images/"},{"id":389747,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.XML"}],"country":"United States","state":"Maine","otherGeospatial":"Rachel Carson National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63796997070312,\n 43.20417480788432\n ],\n [\n -70.61325073242188,\n 43.153101551466385\n ],\n [\n -70.477294921875,\n 43.257205668363206\n ],\n [\n -70.43472290039062,\n 43.38508989465156\n ],\n [\n -70.53634643554688,\n 43.393073720674415\n ],\n [\n -70.63796997070312,\n 43.31418735795809\n ],\n [\n -70.63796997070312,\n 43.20417480788432\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The U.S. Geological Survey mined data from a variety of national and state agencies including USGS, Oregon Water Resources Department, National Oceanic and Atmospheric Administration, Washington Department of Ecology, Pacific Northwest National Laboratory, Portland State University, and U.S. Army Corps of Engineers. A comprehensive dataset of streamflow, stage, and tidal elevations for the Lower Columbia River basin was compiled. Data were compiled from gaging stations in Oregon and Washington along the Columbia River from Astoria to The Dalles and along the Willamette River from Salem to Portland. Tidal gages along the Washington, Oregon, and California coasts were also compiled. Seasonal maximum values were calculated for both streamflow and stage for the winter (November–March) and spring (April–July) flow seasons, as well as for the full water year when underlying data were available. The aggregated datasets are available at https://doi.org/10.5066/P9R6RT0Z.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201138","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","usgsCitation":"Boudreau, C.L., Stewart, M.A., and Stonewall, A.J., 2021, Historical streamflow and stage data compilation for the Lower Columbia River, Pacific Northwest: U.S. Geological Survey Open-File Report 2020–1138, 50 p., https://doi.org/10.3133/ofr20201138.","productDescription":"Report: viii, 50 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101122","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":389696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1138/coverthb.jpg"},{"id":389697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1138/ofr20201138.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1138"},{"id":389698,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R6RT0Z","text":"USGS data release","description":"USGS Data release","linkHelpText":"Historical streamflow and stage data for the lower Columbia River basin and the coasts of Washington, Oregon, and northern California"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Lower Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.63964843750001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 41.672911819602085\n ],\n [\n -120.80566406250001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 49.26780455063753\n ],\n [\n -125.63964843750001,\n 41.672911819602085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Harney Basin is a closed river basin in southeastern Oregon. Surface water in the basin is used for a variety of social, economic, and ecological benefits. While some surface water uses compete with one another, others are complementary or jointly produce multiple beneficial outcomes. The objective of this study is to conduct an economic assessment of surface water in the basin as it relates to wet meadow pasture production and outdoor recreation. Given the complex interactions between surface water management on public and private land and the various goods and services that are derived from adequate water resources, an economic assessment of surface water management can be used to assist future decision making in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211087","usgsCitation":"Bair, L.S., Flyr, M., and Huber, C., 2021, Economic assessment of surface water in the Harney Basin, Oregon: U.S. Geological Survey Open-File Report 2021-1087, 43 p., https://doi.org/10.3133/ofr20211087.","productDescription":"vii, 43 p.","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-122032","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":389611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1087/covrthb.jpg"},{"id":389612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1087/ofr20211087.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.05859375,\n 42.24478535602799\n ],\n [\n -117.454833984375,\n 42.24478535602799\n ],\n [\n -117.454833984375,\n 44.38669150215206\n ],\n [\n -120.05859375,\n 44.38669150215206\n ],\n [\n -120.05859375,\n 42.24478535602799\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"The U.S. Geological Survey conducts ecological monitoring of rocky subtidal communities at four permanent sites around San Nicolas Island. The sites—Nav Fac 100, West End, Dutch Harbor, and Daytona 100—were based on ones that had been monitored since 1980 by the U.S. Geological Survey and, in cooperation with the U.S. Navy, were combined or expanded in 2014 for better comparability with monitoring programs conducted at the other California Channel Islands. At the sites, we counted a suite of kelps and invertebrates on benthic band transects, measured bottom cover of algae and sessile invertebrate species in quadrats, and counted and sized fish on swimming transects. Holdfast diameter and number of stipes of giant kelp (Macrocystis pyrifera) were recorded on these transects and size data were collected for urchins, sea stars, and shelled mollusks. Bottom temperatures were recorded at hourly intervals by archival data loggers that were deployed at the sites. Typically, this monitoring work is conducted semi-annually, in fall and spring. Because the spring 2020 trip was cancelled due to the Coronavirus Disease 2019 pandemic, this report focuses primarily on data collected in fall 2019 and makes comparisons with data collected in previous years, beginning in fall 2014.
The sites are distributed around the island and differ in their physical and ecological characteristics. Nav Fac 100, situated on the north side of San Nicolas Island, has a relatively low benthic profile. The invasive brown alga Sargassum horneri was first observed at this site in 2015. West End, to the southwest of the island, also lacks much bottom relief but has more crevice habitat associated with boulders. For almost three decades, West End has been a focal point for the small, but growing, population of southern sea otters (Enhydra lutris nereis) at the island. Dutch Harbor, on the south side, has many high relief rocky reefs and had the greatest fish and non-motile invertebrate densities. Daytona 100, on the southeast side, has moderate relief and has remained a patchwork of kelp and sea urchin dominated areas.
There were no major changes at the sites since spring 2019, but some trends observed during the last few years continued whereas others changed. Red urchins continued a declining trend (observed during the last 4 years) at Daytona 100. The wavy turban snail (Megastraea undosa) began to increase rapidly at Nav Fav 100 in 2015 and has subsequently been increasing at the other sites as well, after more than a decade of very low numbers at all sites. Sea star wasting syndrome, which has devastated multiple species of sea stars along the Pacific coast of North America, affected most species at San Nicolas Island in the year prior to the fall 2014 sampling. Since then, there has been a reduction in the number of bat stars (Patiria miniata), and very few sea stars of other species have been observed. There has been a slight recovery of P. miniata since 2016 but little sign of change in other species. All the sites had a slight decline in the densities of purple urchins following an increase during the previous 2 years. Long-term data are presented to illustrate trends and changes during almost four decades of monitoring this dynamic system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211081","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Wildlife Program","usgsCitation":"Kenner, M.C., and Tomoleoni, J., 2021, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California—Fall 2019, sixth annual report: U.S. Geological Survey Open-File Report 2021–1081, 97 p., https://doi.org/10.3133/ofr20211081.","productDescription":"ix, 97 p.","numberOfPages":"97","onlineOnly":"Y","ipdsId":"IP-128532","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":389297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1081/covrthb.jpg"},{"id":389298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1081/ofr20211081.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389299,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1081/ofr20211081.xml"},{"id":389300,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1081/images"}],"country":"California","otherGeospatial":"Naval Base Ventura County, San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.59304809570312,\n 33.20824398778792\n ],\n [\n -119.42138671875,\n 33.20824398778792\n ],\n [\n -119.42138671875,\n 33.29724715520414\n ],\n [\n -119.59304809570312,\n 33.29724715520414\n ],\n [\n -119.59304809570312,\n 33.20824398778792\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Benthic samples were collected from the Sacramento–San Joaquin Delta of northern California to examine the effect of the changing hydrologic flow on the bivalves Potamocorbula and Corbicula before, during, and after the False River Barrier (hereafter, barrier) was in operation (May–November 2015). Potamocorbula moved upstream in the Sacramento River as the salinity intruded. Given the lower electrical conductivity of the San Joaquin River, Potamocorbula did not move as far upriver as it did in the Sacramento River. Potamocorbula recruits settled in the Sacramento and False Rivers, whereas Corbicula recruits were mostly found in the San Joaquin River. When the grazing rates for the two bivalves were combined, new populations of Potamocorbula plus existing Corbicula likely reduced the net growth rate of the phytoplankton in and just upstream from the Sacramento and San Joaquin River confluence region when the barrier was in place. Prior to the barrier installation, a very dry period assumably aided the success of Potamocorbula in the confluence region; nonetheless, they also responded to the increasing salinity in the Sacramento River and their population spatially expanded. Potamocorbula’s upriver incursion was stopped owing to the return of freshwater flow due to the removal of the barrier, but the adults of the species were still present at the upstream end of Decker Island in January 2016. Corbicula adults did not seem to respond to the increased salinity caused by the barrier and maintained their biomass at all locations compared to what was recorded before the barrier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211088","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Parchaso, F., Zierdt Smith, E.L., and Thompson, J.K., 2021, Effect of the emergency drought barrier on the distribution, biomass, and grazing rate of the bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California: U.S. Geological Survey Open-File Report 2021–1088, 22 p., https://doi.org/10.3133/ofr20211088.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-120260","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":389246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1088/coverthb.jpg"},{"id":389247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1088/ofr20211088.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1088"}],"country":"United States","state":"California","otherGeospatial":"False River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87957763671874,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.005902055387075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Resources, Earth System Processes Division
U.S. Geological Survey
345 Middlefield Road
Menlo Park, California, 94025
The restoration of coastal habitats, particularly coral reefs, can reduce risks by decreasing the exposure of coastal communities to flooding hazards. In the United States, the protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of where potential coral reef restoration could decrease the hazard faced by Florida and Puerto Rico’s reef-fronted coastal communities. The three restoration scenarios considered: (1) Ecological restoration, ‘E25’, which assumes planting 0.25-meter (m)-high corals on a (cross-shore) 25-m-wide reef; (2) Structural plus ecological, ‘S25’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 25 m wide reef; and (3) structural plus ecological, ‘S05’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 5 m wide reef. Planted corals are assumed to increase hydrodynamic roughness, thereby dissipating incident wave energy and decreasing flooding potential. We used a standardized approach to ‘place’ potential restoration projects throughout the whole (linear) extent of reefs bordering Florida and Puerto Rico to identify where coral reef restoration could be useful for meeting flood reduction benefits. We always sited potential restoration projects within the existing distribution of reefs even though many sites were far (kilometers [km]) offshore and some sites were relatively deep (up to 7 m depth). We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 980 km of Florida and Puerto’s Rico reef-lined shorelines for the three potential coral reef restoration scenarios and compare them to the flood zones without coral reef restoration. We quantified the potential coastal flood risk reduction provided by coral reef restoration using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculate the change in annual expected damages, a measure of the annual protection gained because of coral reef restoration. We found that the benefits of reef restoration off Florida and Puerto Rico are spatially highly variable. In most areas, we found little or no benefit from reef restoration (for example, restoration sites were far offshore or deep). However, there were a number of key areas where reef restoration could have substantial benefits for flood risk reduction. In particular, we estimated the protection gained by Florida and Puerto Rico’s coral reefs from coral reef restoration to result in:
Thus, the annual value of flood risk reduction provided by potential coral reef restoration in Florida and Puerto Rico is more than 3,100 people and $272.9 million (2010 U.S. dollars) in economic activity. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when potential coral reef restoration in Florida and Puerto Rico can increase critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211054","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Viehman, T.S., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the coastal hazard risks reduction provided by potential coral reef restoration in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1054, 35 p., https://doi.org/10.3133/ofr20211054.","productDescription":"Report: vi, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-125062","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386211,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1054/covrthb.jpg"},{"id":386212,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1054/ofr20211054.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386214,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQKZR9","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida, the Commonwealth of Puerto Rico, and the Territory of the U.S. Virgin Islands for current and potentially restored coral reefs"},{"id":386215,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386216,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.390380859375,\n 18.594188856740413\n ],\n [\n -66.7529296875,\n 18.70869162255995\n ],\n [\n -67.08251953125,\n 18.729501999072138\n ],\n [\n -67.32421875,\n 18.510865709091377\n ],\n [\n -67.401123046875,\n 18.25021997706561\n ],\n [\n -67.30224609375,\n 17.916022703877665\n ],\n [\n -66.895751953125,\n 17.853290114098012\n ],\n [\n -66.42333984375,\n 17.8742034396575\n ],\n [\n -65.885009765625,\n 17.821915515968854\n ],\n [\n -65.467529296875,\n 17.926475979176438\n ],\n [\n -65.32470703125,\n 18.218916080017465\n ],\n [\n -65.511474609375,\n 18.46918890441719\n ],\n [\n -66.390380859375,\n 18.594188856740413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed, in social and economic terms, with the annual protection provided by U.S. coral reefs off the coast of the State of Florida estimated to be more than 5,600 people and $675 million (2010 U.S. dollars). Degradation of coral reef ecosystems over the past several decades and during tropical storm events has caused regional-scale erosion of the shallow seafloor that serves as a protective barrier against coastal hazards along Southeast Florida, increasing risks to coastal populations. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of the increased hazard faced by Florida’s reef-fronted coastal communities because of the projected degradation of its adjacent coral reefs. We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 430 kilometers of Florida’s reef-lined shorelines for both the current and projected future coral reef conditions. We quantified the coastal flood risk increase caused by coral reef degradation using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of projected coral reef degradation. We found that degradation of the coral reefs off Florida increases future risks significantly. In particular, we estimated the protection lost by Florida’s coral reefs from projected coral reef degradation will result in:
Thus, the annual value of increased flood risk caused by the projected degradation of Florida’s coral reefs is more than 7,300 people and $823.6 million (2010 U.S. dollars). These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when degradation of Florida’s coral reefs will decrease critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211055","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Yates, K.K., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Zawada, D.G., Arsenault, S.R., Fehr, Z.W., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of projected coral reef degradation on coastal hazard risk in Florida: U.S. Geological Survey Open-File Report 2021–1055, 27 p., https://doi.org/10.3133/ofr20211055.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-125063","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386221,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"},{"id":386222,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"},{"id":386220,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D9LDEP","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida with and without projected coral reef degradation"},{"id":386218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1055/covrthb.jpg"},{"id":386219,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1055/ofr20211055.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. These damages were measured in post-storm surveys of reefs and assessed in terms of their impact on reef condition and height, which are critical parameters for evaluating the coastal defense benefits of reefs. We combined engineering, ecologic, geospatial, social, and economic data and tools to value the increased risks in Florida and Puerto Rico from hurricane-induced damages to their adjacent coral reefs. We followed risk-based valuation approaches to map flooding at 10-square-meter resolution along all 980 kilometers of Florida and Puerto Rico’s reef-lined shorelines considering reef condition before (undamaged) and after (damaged) the 2017 hurricanes. We quantified the coastal flood risk increase caused by the hurricane-induced damage to the coral reefs using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of the reef damage caused by the 2017 hurricanes. We found that the damages to the coral reefs off Florida and Puerto Rico from Hurricanes Irma and Maria increased future risks significantly. In particular, we estimated the protection lost by Florida and Puerto Rico’s coral reefs from the 2017 hurricanes to result in:
Thus, the annual value of increased flood risk caused by the damage to Florida and Puerto Rico’s coral reefs from hurricanes in 2017 is more than 4,300 people and $181.5 mil-lion (2010 U.S. dollars) in economic impacts. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when the damage from the 2017 hurricanes decreased critical coastal storm flood reduction benefits to Florida and Puerto Rico’s coral reefs. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211056","collaboration":"Prepared in cooperation with the University of California, Santa Cruz and the National Oceanic and Atmospheric Administration","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Viehman, T.S., Cumming, K.A., Cole, A.D., Shope, J.B., Groves, S.H., Gaido L., C., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1056, 29 p., https://doi.org/10.3133/ofr20211056.","productDescription":"Report: v, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-125064","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386227,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EHOBKO","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida and the Commonwealth of Puerto Rico before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs"},{"id":386229,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386225,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1056/covrthb.jpg"},{"id":386226,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1056/ofr20211056.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386228,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.368408203125,\n 18.542116654448996\n ],\n [\n -66.961669921875,\n 18.646245142670608\n ],\n [\n -67.269287109375,\n 18.552532366385577\n ],\n [\n -67.357177734375,\n 18.198043686762652\n ],\n [\n -67.236328125,\n 17.916022703877665\n ],\n [\n -66.59912109375,\n 17.832374329567518\n ],\n [\n -65.643310546875,\n 17.95783210227242\n ],\n [\n -65.291748046875,\n 18.22935133838668\n ],\n [\n -65.54443359375,\n 18.500447458475094\n ],\n [\n -66.368408203125,\n 18.542116654448996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
This study was undertaken by the U.S. Geological Survey to assess the detectability of landslides in the densely forested and mountainous island of Pohnpei in the Federated States of Micronesia. The study used existing field-observed land-cover changes and landslides visible on Google Earth (GE) images. A limited number of ALOS-2 PALSAR-2 L-band synthetic aperture radar (SAR) images were collected on two adjacent orbit paths before and after an intense rainfall event that affected Pohnpei in mid-March 2018. Similar sets of images were collected in 2019 and 2020. Low coherence throughout the island interior eliminated use of phase-change products, and change analysis identified no landslide features as having formed in 2019 or 2020. The assessment of red-green-blue image composites and application of the log-ratio method to the 2018 ground-range SAR images identified 5 of the 11 landslides observed on the GE images. Visual comparisons of the co-event and post-event coherence image products detected 9 of the 11 landslides observed on the GE images. Combined, the ground-based SAR and interferometric SAR coherence change detections overcame high temporal and spatial decorrelations, identified all but one landslide visible in the GE comparison, and included substantial redundancy. The robustness of the landslide detection indicates that an increased collection frequency of L-band images could support systematic monitoring of land-cover change on Pohnpei at the scale reported in this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211084","usgsCitation":"Ramsey, E.W., III, and Rangoonwala, A., 2021, Using ALOS-2 synthetic aperture radar (SAR) and interferometric SAR to detect landslides on the mountainous island of Pohnpei, Federated States of Micronesia: U.S. Geological Survey Open-File Report 2021–1084, 28 p., https://doi.org/10.3133/ofr20211084.","productDescription":"vii, 28 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-131020","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":388659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1084/coverthb.jpg"},{"id":388660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1084/ofr20211084.pdf","text":"Report","size":"5.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1084"},{"id":388661,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1084/images"}],"country":"Federated States of Micronesia","otherGeospatial":"Island of Pohnpei","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 158.06442260742188,\n 6.770988820924266\n ],\n [\n 158.38577270507812,\n 6.770988820924266\n ],\n [\n 158.38577270507812,\n 7.027297875479451\n ],\n [\n 158.06442260742188,\n 7.027297875479451\n ],\n [\n 158.06442260742188,\n 6.770988820924266\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
A two-year study (2013 and 2017) was conducted to determine if annual releases of hatchery rainbow trout (resident Oncorhynchus mykiss) in the upper Cowlitz River Basin, Washington adversely affected anadromous fish in the basin. Rainbow trout tagged with radio transmitters were monitored after release to describe movement patterns, entrainment rates at Cowlitz Falls Dam, and survival. Additionally, trout that were radio-tagged in 2017 were monitored during spring 2018 to determine if any moved upstream and entered tributaries where winter steelhead (anadromous Oncorhynchus mykiss) spawning occurs. A total of 580 hatchery rainbow trout (122 in 2013 and 458 in 2017) were radio-tagged and released at three release sites: (1) Cowlitz Falls Campground on Cowlitz River Arm of Lake Scanewa river kilometer (rkm) 155, (2) Cispus River Arm of Lake Scanewa rkm 1, and (3) Day Use Park on Cowlitz River Arm of Lake Scanewa rkm 146. Most radio-tagged trout (70 percent) remained within 6.4 rkm of the release site but some fish moved at least 25.7 rkm from the release site. The predominant movement direction was downstream. More than twice as many fish released at Cowlitz Falls Campground in 2017 (compared to the other two release sites) remained in the Cowlitz River, where potential overlap with steelhead occurs. A total of 28.3 percent of the study fish were entrained at Cowlitz Falls Dam. Apparent survival (time until movement ceased) for most tagged trout was fewer than 100 days from release in both years and no fish were detected moving during the spring following their release. In summary, hatchery rainbow trout released upstream from Cowlitz Falls Dam seem to remain primarily in Lake Scanewa or entrained at Cowlitz Falls Dam with few fish surviving to winter months. We found no evidence of hatchery trout interacting with steelhead in spawning tributaries during spring months. These results suggest that trout stocking in the upper Cowlitz River Basin poses minimal threat to anadromous fish in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211085","collaboration":"Prepared in cooperation with the Bonneville Power Administration and Public Utility District Number 1 of Lewis County, Washington","usgsCitation":"Hansen, A.C., Kock, T.J., Ekstrom, B.K., and Liedtke, T.L., 2021, Behavior and survival of hatchery rainbow trout (Oncorhynchus mykiss) in the upper Cowlitz River Basin, Washington, 2013 and 2017 (ver. 1.1, September 2021): U.S. Geological Survey Open-File Report 2021–1085, 14 p., https://doi.org/10.3133/ofr20211085.","onlineOnly":"Y","ipdsId":"IP-127058","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":397377,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1085/ofr20211085.XML"},{"id":397376,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1085/images"},{"id":388965,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1085/versionhist.txt"},{"id":403443,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211085/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1085"},{"id":388676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1085/ofr20211085.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1085"},{"id":388675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1085/coverthb2.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Upper Cowlitz River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.13476562499997,\n 46.057985244793024\n ],\n [\n -121.66259765624999,\n 46.057985244793024\n ],\n [\n -121.66259765624999,\n 46.73986059969267\n ],\n [\n -123.13476562499997,\n 46.73986059969267\n ],\n [\n -123.13476562499997,\n 46.057985244793024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Endangered Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers primarily use lotic habitats during the spring spawning season in the Upper Klamath Lake watershed. However, summer-time surveys of the upper Lost River watershed in 1972, 1975 and 1989–90 indicated that adults of both endangered species use tributaries of Clear Lake Reservoir (hereafter: Clear Lake) year-round. Adult shortnose suckers have also been documented to use tributaries of Gerber Reservoir year-round. We surveyed the tributaries of Clear Lake and Gerber Reservoir to provide up-to-date information on the timing, distribution, and habitat use within the upper Lost River drainage by these two endangered sucker species.
Contrary to previous studies, this study did not capture any Lost River suckers in the Clear Lake tributaries. Genetics samples from suckers collected during this study were used to verify that no Lost River suckers were captured. At the time of this study, genetics could not identify the differences between shortnose and the non-endangered Klamath largescale suckers (Catostomus snyderi), therefore, morphology was used to separate these two species. Furthermore, the shortnose suckers and the Klamath largescale suckers documented in the upper Lost River drainage are more similar to Klamath largescale suckers than shortnose suckers that exist in the Upper Klamath Lake recovery unit. Therefore, the suckers we documented during our surveys were most likely Klamath largescale suckers.
We captured suckers, age-0 to age-9, in the Clear Lake tributaries within stream pools and flooded meadows behind water retention structures. However, no suckers were collected in small reservoirs sampled upstream of Clear Lake. Suckers were found in habitats with mud and fine substrate at depths of 0.5–3.0 meters, with most captured at 1.0 meter or less. Suckers co-occurred with nonnative species, which were more abundant in our survey than in previous surveys in the tributaries to Clear Lake.
Gerber Reservoir tributaries yielded more suckers per unit effort than Clear Lake tributaries. All suckers captured in the tributaries of Gerber Reservoir were identified as Klamath Largescale suckers. The suckers in tributaries to Gerber Reservoir were collected in similar habitat as those in Clear Lake tributaries and were age-0 to age-6.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211077","collaboration":"Prepared in cooperation with the U.S. Bureau of Reclamation","usgsCitation":"Martin, B.A., Burdick, S.M., Staiger, S.T., and Kelsey, C., 2021, Water quality, instream habitat, and the distribution of suckers in the upper Lost River watershed of Oregon and California, summer 2018: U.S. Geological Survey Open-File Report 2021–1077, 29 p., https://doi.org/10.3133/ofr20211077.","productDescription":"v, 29 p.","onlineOnly":"Y","ipdsId":"IP-122858","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1077/coverthb.jpg"},{"id":388610,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1077/ofr20211077.pdf","text":"Report","size":"3.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1077"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Lost River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.27783203125,\n 41.63186741069748\n ],\n [\n -120.73974609374999,\n 41.63186741069748\n ],\n [\n -120.73974609374999,\n 42.66628070564928\n ],\n [\n -122.27783203125,\n 42.66628070564928\n ],\n [\n -122.27783203125,\n 41.63186741069748\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
A 2-year telemetry study was conducted April–July in 2018 and 2019 to evaluate migration behavior and survival of juvenile steelhead (Oncorhynchus mykiss) and coho salmon (O. kisutch) in the Klickitat River, Washington. A total of 612 natural-origin steelhead, collected in a smolt trap on the Klickitat River, were tagged, released, and monitored as they outmigrated through the lower 17 kilometers (km) of the Klickitat River, and in the 52 km reach between the mouth of the Klickitat River and Bonneville Dam. The primary goal of the steelhead study was to estimate survival through the Klickitat River delta, the 2 km reach located at the confluence of the Klickitat and Columbia rivers. A total of 400 hatchery-origin coho salmon were tagged and released at the Klickitat Hatchery and monitored during migration through the lower 68 km of the Klickitat River and in the Columbia River to Bonneville Dam. The primary goals of the coho salmon study were (1) to estimate survival through the Klickitat River delta and (2) to determine residence time in the Klickitat River to assess potential for interactions with rearing natural-origin fish.
Many tagged steelhead and coho salmon moved quickly downstream and left the Klickitat River shortly after release. Median elapsed time from release to Klickitat River exit ranged from 1.4 to 1.5 days for steelhead, and from 5.1 to 12.9 days for coho salmon during the two-year study. Ten percent of the tagged coho salmon in 2018 remained in the Klickitat River for 21.9–29.2 days before entering the Columbia River. In 2019, ten percent of the tagged coho salmon remained in the Klickitat River for 36.0–45.5 days before entering the Columbia River. This suggests that some hatchery fish spend considerable time in the river after hatchery release. Migration rates were consistently slow for both species in the Klickitat River delta compared to upstream reaches of the free-flowing Klickitat River and downstream reaches of the Columbia River. Similarly, reach-specific survival was highest in free-flowing reaches of the Klickitat River and lowest near the Klickitat River delta. Cumulative survival from release to sites located downstream of the Klickitat River delta were 0.78 for juvenile steelhead in both 2018 and 2019, and 0.57 and 0.61 for juvenile coho salmon in 2018 and 2019. Standardized survival estimates (survival per 100 river kilometers) were 0.243 in 2018 and 0.302 in 2019 for steelhead, and 0.100 in 2018 and 0.153 in 2019 for coho salmon. These estimates of standardized survival are low compared to similar estimates from other rivers in Washington, Oregon, Idaho, and California. This study provided new information about survival and residence time of juvenile steelhead and coho salmon in the Klickitat River. Additional studies would be helpful to understand factors affecting outmigration survival and overlap between hatchery-origin and natural-original juvenile steelhead and coho salmon in the system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211083","collaboration":"Prepared in cooperation with Yakama Nation Fisheries","usgsCitation":"Evans, S.D., Lindley, D.S., Kock, T.J., Hansen, A.C., Perry, R.W., Zendt, J.S., and Romero, N., 2021, Evaluation of movement and survival of juvenile steelhead (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch) in the Klickitat River, Washington, 2018–2019: U.S. Geological Survey Open-File Report 2021–1083, 20 p., https://doi.org/10.3133/ofr20211083.","productDescription":"vi, 17 p.","onlineOnly":"Y","ipdsId":"IP-126889","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1083/coverthb.jpg"},{"id":388573,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1083/ofr20211083.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1083"}],"country":"United States","state":"Washington","otherGeospatial":"Klickitat River, Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.70654296874999,\n 45.583289756006316\n ],\n [\n -120.69580078124999,\n 45.583289756006316\n ],\n [\n -120.71777343749997,\n 45.98169518512228\n ],\n [\n -121.75048828124997,\n 45.96642454131025\n ],\n [\n -121.70654296874999,\n 45.583289756006316\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during seven annual surveys consisting of two censuses each over a 34-year period. Additional data were collected when marked tortoises were recovered dead and removed between survey years. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. Three size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary), and three survival states (not-yet-entered, entered/alive, and dead/removed) were incorporated into the model. We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181094","usgsCitation":"Berry, K.H., and Yee, J.L., 2021, Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study: U.S. Geological Survey Open-File Report 2018–1094, 55 p., https://doi.org/10.3133/ofr20181094.","productDescription":"vi, 55 p.","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-086643","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388564,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2018/1094/images"},{"id":388563,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.xml"},{"id":388562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1094/covrthb.jpg"}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999.
The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030J","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., Park, S., and Cantrell, S., 2021, System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A), chap. J of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 35 p., https://doi.org/10.3133/ofr20211030J.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":388510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/j/ofr20211030j.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030J"},{"id":388509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/j/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Within the forests of Pictured Rocks National Lakeshore, biologists are trying to understand the effects beech bark disease has on wildlife species, especially species that need forest connectivity to thrive. This project used aerial imagery collected in 2005, shortly after beech bark disease infestation, and satellite imagery from 2018. The 2018 imagery represents present day conditions and was used to locate forest canopy gaps through object-based image analysis. Forest canopy gaps were identified using the multiresolution segmentation algorithm within Trimble’s eCognition software. A time change analysis was completed to understand how the forest canopy had changed from 2005 to 2018. The analysis showed areas that had maintained forest canopy, maintained a forest canopy gap, created a new canopy gap (closed forest canopy in 2005 but open canopy gap in 2018), or created new forest canopy (open canopy gap in 2005 but closed forest canopy in 2018). There were 9,127 acres of forest canopy lost, and 72.8 percent of that lost canopy occurred in a forest type where Fagus grandifolia Ehrh. (American beech) is a common tree species. The datasets developed through this project can enhance knowledge of where canopy gaps exist and help place focus on certain areas for wildlife studies. In addition, these datasets can be used in future studies to monitor the health of the forest and conduct additional change analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211069","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Sattler, S.R., 2021, Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan: U.S. Geological Survey Open-File Report 2021–1069, 12 p., https://doi.org/10.3133/ofr20211069.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-124452","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388432,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1069/images"},{"id":388429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1069/coverthb.jpg"},{"id":388430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1069/ofr20211069.pdf","text":"Report","size":"6.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1069"},{"id":388431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EZEAYD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Effects of beech bark disease on forest connectivity in Pictured Rocks National Lakeshore from 2005 to 2018"}],"country":"United States","state":"Michigan","otherGeospatial":"Pictures Rocks National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.62307739257812,\n 46.42176587242696\n ],\n [\n -86.4349365234375,\n 46.45961954102543\n ],\n [\n -86.20147705078125,\n 46.57585481240773\n ],\n [\n -86.02706909179688,\n 46.619261036171515\n ],\n [\n -86.00509643554686,\n 46.669229446893404\n ],\n [\n -86.08612060546875,\n 46.66545985627255\n ],\n [\n -86.14105224609375,\n 46.677710064644344\n ],\n [\n -86.4459228515625,\n 46.557916007595786\n ],\n [\n -86.48712158203125,\n 46.55602736725248\n ],\n [\n -86.6217041015625,\n 46.44826620185314\n ],\n [\n -86.62307739257812,\n 46.42176587242696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Arctic barriers islands are highly dynamic features influenced by a variety of oceanographic, geologic, and environmental factors. Many Alaskan barrier islands and spits serve as habitat and protection for native species, as well as shelter the coast from waves and storms that cause flooding and degradation of coastal villages. This study summarizes changes to barrier morphology in time and space along the North Slope coast of Alaska between the United States-Canadian border and Cape Beaufort from 1947 to 2020. Changes considered in this study include number of barriers, area and perimeter, shoreline length, barrier sinuosity and width, presence and number of relict terminus features, presence and coverage of tundra vegetation, barrier orientation, and elevation metrics. Wave conditions are also summarized and related to changes in barrier morphology. The results in this report help to better predict future barrier evolution and prevalence along Alaska’s coast by increasing our understanding of Arctic barrier development, migration and degradation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211074","usgsCitation":"Hamilton, A.I., Gibbs, A.E., Erikson, L.H., and Engelstad, A.C., 2021, Assessment of barrier island morphological change in northern Alaska: U.S. Geological Survey Open-File Report 2021–1074, 28 p., https://doi.org/10.3133/ofr20211074.","productDescription":"Report: vi , 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-122308","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":388442,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EQ1H7","linkHelpText":"Historical shorelines and morphological metrics for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019"},{"id":388441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1074/ofr20211074.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1074/covrthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.83984375,\n 69.16255790810499\n ],\n [\n -141.240234375,\n 69.16255790810499\n ],\n [\n -141.240234375,\n 72.01972876525514\n ],\n [\n -160.83984375,\n 72.01972876525514\n ],\n [\n -160.83984375,\n 69.16255790810499\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time.
To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement:
“How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?”
A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives.
To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201122","collaboration":"Prepared in Cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fournier, A.M.V., Wilson, R.R., Lyons, J.E., Gleason, J.S., Adams, E.M., Barnhill, L.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M.J.L., Eaton, M.J., Frederick, P.C., Just, M.G., Seymour, M.A., Tirpak, J.M, and Woodrey, M.S., 2021, Structured decision making and optimal bird monitoring in the northern Gulf of Mexico: U.S. Geological Survey Open-File Report 2020–1122, 62 p., https://doi.org/10.3133/ofr20201122.","productDescription":"Report: ix, 62 p.; 6 Companion Files","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100582","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387878,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.xlsm","text":"2. Portfolio Analysis Spreadsheet","size":"139 KB"},{"id":387871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1122/coverthb.jpg"},{"id":387876,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix.xlsx","text":"5. Matrix of Management Actions and Bird Species","size":"45.5 KB"},{"id":387874,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds.xlsx","text":"1. Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"727 KB"},{"id":387872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122.pdf","text":"Report","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1122"},{"id":387875,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds_csv.zip","text":"1. Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"47.1 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387873,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_database.docx","text":"6. R Code for Using Deepwater Horizon Project Tracker Database","size":"14.1 KB"},{"id":387882,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_proposals.docx","text":"3. R Code to Simulate Monitoring Proposals","size":"15.7 KB"},{"id":387881,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios_csv.zip","text":"4. All Test Projects and Portfolios","size":"101 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387877,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix_csv.zip","text":"5. Matrix of Management Actions and Bird Species","size":"2.83 KB","linkHelpText":"- Zip file of tables in CSV format"},{"id":387880,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_projects-portfolios.xlsm","text":"4. All Test Projects and Portfolios","size":"1.28 MB"},{"id":387879,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.zip","text":"2. Portfolio Analysis Spreadsheet","size":"5.35 KB","linkHelpText":"- Zip file of tables in CSV format"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 25.997549919572112\n ],\n [\n -91.43920898437499,\n 26.194876675795218\n ],\n [\n -87.703857421875,\n 26.175158990178133\n ],\n [\n -86.37451171875,\n 27.205785724383325\n ],\n [\n -85.4736328125,\n 26.204734267107604\n ],\n [\n -83.155517578125,\n 24.647017162630366\n ],\n [\n -81.71630859375,\n 24.347096633808512\n ],\n [\n -80.22216796875,\n 24.926294766395593\n ],\n [\n -79.881591796875,\n 26.115985925333536\n ],\n [\n -80.584716796875,\n 27.926474039865017\n ],\n [\n -81.221923828125,\n 27.858503954841247\n ],\n [\n -81.793212890625,\n 28.806173508854776\n ],\n [\n -82.957763671875,\n 30.344435586368462\n ],\n [\n -83.265380859375,\n 30.65681556429287\n ],\n [\n -84.957275390625,\n 30.751277776257812\n ],\n [\n -85.0341796875,\n 31.015278981711266\n ],\n [\n -87.51708984375,\n 30.987027960280326\n ],\n [\n -87.7587890625,\n 31.512995857454676\n ],\n [\n -88.29711914062499,\n 31.55981453201843\n ],\n [\n -88.450927734375,\n 30.996445897426373\n ],\n [\n -89.395751953125,\n 30.949346915468563\n ],\n [\n -89.97802734375,\n 30.826780904779774\n ],\n [\n -90.758056640625,\n 30.477082932837682\n ],\n [\n -91.92260742187499,\n 30.543338954230222\n ],\n [\n -94.10888671875,\n 30.344435586368462\n ],\n [\n -94.7900390625,\n 30.230594564932193\n ],\n [\n -95.69091796875,\n 29.735762444449076\n ],\n [\n -95.51513671875,\n 29.372601506681402\n ],\n [\n -95.9326171875,\n 29.19053283229458\n ],\n [\n -96.5478515625,\n 29.094577077511826\n ],\n [\n -97.6025390625,\n 28.488005204159457\n ],\n [\n -97.987060546875,\n 27.819644755099446\n ],\n [\n -98.009033203125,\n 27.176469131898898\n ],\n [\n -97.767333984375,\n 26.352497858154024\n ],\n [\n -97.2509765625,\n 25.997549919572112\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
We created this guide to introduce the user to the San Francisco Volcanic Field as a terrestrial analog site for planetary volcanic processes. For decades, the San Francisco Volcanic Field has been used to teach scientists to recognize the products of common types of volcanic eruptions and associated volcanic features. The volcanic processes and products observed in this volcanic field are like those observed on lunar and Martian surfaces. As a result, this region has been a favored location for training National Aeronautics and Space Administration astronauts and engineers since the Apollo missions.
Though the San Francisco Volcanic Field has more than 600 volcanic vents and flows, this guide will focus on S P Mountain (known locally as S P Crater, located ~30 miles north of Flagstaff, Arizona), one of the best preserved and most accessible of the volcanic cones and lava flows. S P Mountain presents both major types of basaltic eruptions—explosive and effusive—as well as some commonly associated tectonic landforms.
We assume that the user has a basic understanding of geologic concepts and terminology. For more specialized terminology, we include tables showing the classification scheme for lava compositions, styles of eruptions, and tephra sizes (tables 1, 2, and 3). If a further introduction or refresher in volcanological terminology is desired, we suggest reviewing such terms on the U.S. Geological Survey Volcano Science Center’s online glossary (https://volcanoes.usgs.gov/vsc/glossary/).
One term requires clarification at the start of this guide—the term cinder. The terms cinder and cinder cone are widely used to describe the material and edifice produced by lava fountains. However, the term comes from the mining and construction industries and has no clear or formal definition. The international committees in geology and volcanology have chosen the term tephra to be the general term to describe pyroclasts (material ejected through a volcanic explosion or from a volcanic vent). Therefore, in this guide, we use the term tephra rather than cinder.
This guide is outlined as follows:
Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
U.S. Geological Survey
MS 303
12201 Sunrise Valley Drive
Reston, VA 20192
Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Aside from construction aggregate materials, the value of nonfuel mineral commodities that have been produced in North Dakota is small, although there is potential for the existence of several mineral resource deposit types which are not economically viable at this time. In this report, we present a mineral resource inventory of the State of North Dakota, developed by the U.S. Geological Survey at the request the Bureau of Land Management. To set the stage for that inventory, we briefly outline the long and complex geologic history of North Dakota that extends back more than 3 billion years. Using several existing databases, we summarize the distribution of known mineral commodities and the results of commodity exploration over time. Using all available data, we discuss the potential for economic occurrences of 13 commodities in North Dakota, including some listed as Critical Minerals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211057","collaboration":"Prepared in cooperation with Bureau of Land Management","usgsCitation":"Box, S.E., and Cossette, P.M., 2021, Mineral resource inventory of North Dakota: U.S. Geological Survey Open-File Report 2021–1057, 42 p., https://doi.org/10.3133/ofr20211057.","productDescription":"Report: vii, 42 p.; 4 Appendixes","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-116051","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":399509,"rank":12,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94BA7RO","text":"USGS data release","description":"USGS data release","linkHelpText":"Dataset for mineral resource inventory of North 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Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Long Island National Wildlife Refuge Complex in New York. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of five marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge-complex scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about $24,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $24,000 consistently included approaches for increasing drainage from the marsh surface within the marsh management units. The potential management benefits were derived from expected improvements in surface-water drainage and capacity for marsh elevation to keep pace with sea-level rise, and presumed increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Long Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211070","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Williams, M.R., 2021, Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2021–1070, 34 p., https://doi.org/10.3133/ofr20211070.","productDescription":"Report: vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126538","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387845,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1070/versionHist.txt","size":"640 B","linkFileType":{"id":2,"text":"txt"}},{"id":387151,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1070/ofr20211070.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1070"},{"id":387150,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1070/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.576412521044425\n ],\n [\n -73.6138916015625,\n 40.54720023441049\n ],\n [\n -73.1854248046875,\n 40.60978237983301\n ],\n [\n -72.66357421875,\n 40.77638178482896\n ],\n [\n -72.015380859375,\n 40.96330795307353\n ],\n [\n -71.795654296875,\n 41.091772220976644\n ],\n [\n -72.2625732421875,\n 41.18278832811288\n ],\n [\n -72.7294921875,\n 41.02964338716638\n ],\n [\n -73.245849609375,\n 40.94256444133327\n ],\n [\n -73.4820556640625,\n 40.967455873296714\n ],\n [\n -73.707275390625,\n 40.8595252289932\n ],\n [\n -73.8775634765625,\n 40.79301881008675\n ],\n [\n -74.0203857421875,\n 40.693134153308065\n ],\n [\n -74.0478515625,\n 40.576412521044425\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 13, 2021; Version 1.1: August 11, 2021","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
This report addresses system characterization of Planet’s Dove-R and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove-R has an interior geometric performance in the range of −0.306 (−0.102 pixel) to 0.286 m (0.095 pixel) in easting and 0.090 (0.030 pixel) to 1.084 m (0.361 pixel) in northing in band-to-band registration, an exterior geometric performance of −5.10 m (−0.51 pixel) in easting and 3.30 m (0.33 pixel) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.023 to −0.008 in offset and 0.948 to 1.077 in slope, and a spatial performance in the range of 2.96 to 3.15 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.003.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030D","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove-R, chap. D of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 34 p., https://doi.org/10.3133/ofr20211030D.","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-126678","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/d/ofr20211030d.pdf","text":"Report","size":"3.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030D"},{"id":387783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/d/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Arkansas bauxite district, which comprises about 275 square miles (710 square kilometers) of central Arkansas, produced an order of magnitude more bauxite and alumina than the other bauxite districts in the United States combined. Bauxite was mined in the region continuously from 1898 to 1982. These bauxites are laterite deposits, formed from intensive in-place weathering of the exposed surface of the Granite Mountain pluton, a Late Cretaceous batholith composed mainly of nepheline syenite and lesser amounts of syenite. Nepheline syenite was the aluminum source for the bauxite and clay deposits that blanket the pluton. The early Eocene continental sedimentary rocks that contain and overlie the bauxite deposits indicate that central Arkansas had a warm tropical environment during bauxite formation.
Bauxite ores are the principal sources of aluminum. Some of the global bauxite deposits have been found to contain co-occurring metals that have essential applications in modern technologies. For example, bauxite is the largest global source of gallium (Ga), used in semiconductors, which is recovered as a byproduct of processing bauxite to recover alumina. Other critical metal commodities within some bauxites that reportedly have potential for byproduct recovery include niobium (Nb), scandium (Sc), and rare earth elements (REEs). Currently (2021), the United States is wholly dependent on imports for its supplies of bauxite for processing to produce alumina. The United States is also dependent on foreign sources of gallium, niobium, and scandium, as well for most of its domestic requirements of REEs.
For these reasons, samples were collected from Arkansas bauxite deposits, associated clays, mill residue wastes (respectively referred to as red muds and black sands), and the parent nepheline syenite to determine their elemental content, with a particular focus on gallium, niobium, scandium, and REEs. Each sample was analyzed for 60 elements; these data and the methods used are published as a U.S. Geological Survey data release.
The results indicate that, of the critical metals in bauxites, gallium is a potential byproduct from the central Arkansas bauxite deposits. The highest gallium concentrations occur in the raw bauxite ore, with an average concentration of 76 parts per million (ppm). Gallium partitions with alumina (the product) rather than into mine waste residues. Results indicate an average niobium content of 662 ppm in the Arkansas bauxite ores. Niobium progressively increases in concentration from parent syenite (247 ppm) to clays (315 ppm) and further from bauxite (662 ppm) to processed residues (1,075 ppm). Low concentrations of scandium were found in all samples, averaging 10 ppm or less in the parent rock (syenite), bauxite, clays, and processing residues. Modest concentrations of the light and heavy REEs were found in samples of bauxite ores, bauxitic clays and interbedded clays, syenite, and the residues of ore. The highest REE values were found in processed residues, with average concentrations of 613 ppm total light REEs and 130 ppm total heavy REEs. These concentrations suggest that additional processing to recover REEs is unlikely to be economic in the foreseeable future.
Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225