Acoustic telemetry is a commonly used technology to monitor animal occupancy and infer movement in aquatic environments. The information that acoustic telemetry provides is vital for spatial planning and management decisions concerning aquatic and coastal environments by characterizing behaviors and habitats such as spawning aggregations, migrations, corridors, and nurseries, among others. However, performance of acoustic telemetry equipment and resulting detection ranges and efficiencies can vary as a function of environmental conditions, leading to potentially biased interpretations of telemetry data. Here, we characterize variation in detection performance using an acoustic telemetry receiver array deployed in Wellfleet Harbor, Massachusetts, USA from 2015 to 2017. The array was designed to study benthic invertebrate movements and provided an in situ opportunity to identify factors driving variation in detection probability.
The near-shore location proximate to environmental monitoring allowed for a detailed examination of factors influencing detection efficiency in a range-testing experiment. Detection ranges varied from < 50 to 1,500 m and efficiencies varied from 0 to 100% within those detection ranges. Detection efficiency was affected by distance, wind speed and direction, wave height and direction, water temperature, water depth, and water quality.
Performance of acoustic telemetry systems is strongly contingent on environmental conditions. Our study found that wind, waves, water temperature, water quality, and depth all affected performance to an extent that could seriously compromise a study if these effects were not taken into consideration. Other unmeasured factors may also be important, depending on the characteristics of each site. This information can help guide future telemetry study designs by helping researchers anticipate the density of receivers required to achieve study objectives. Researchers can further refine and document the reliability of their data by incorporating continuously deployed range-testing tags and prior knowledge on varying detection efficiency into movement and occupancy models.
","language":"English","publisher":"Springer","doi":"10.1186/s40317-023-00317-2","usgsCitation":"Long, M., Jordaan, A., and Castro-Santos, T.R., 2023, Environmental factors influencing detection efficiency of an acoustic telemetry array and consequences for data interpretation: Animal Biotelemetry, v. 11, 18, 13 p., https://doi.org/10.1186/s40317-023-00317-2.","productDescription":"18, 13 p.","ipdsId":"IP-141767","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-023-00317-2","text":"Publisher Index Page"},{"id":416951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.12152378095689,\n 41.97721573790295\n ],\n [\n -70.12152378095689,\n 41.80349781857885\n ],\n [\n -69.90189169540182,\n 41.80349781857885\n ],\n [\n -69.90189169540182,\n 41.97721573790295\n ],\n [\n -70.12152378095689,\n 41.97721573790295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Michael 0000-0001-6735-6878","orcid":"https://orcid.org/0000-0001-6735-6878","contributorId":261905,"corporation":false,"usgs":false,"family":"Long","given":"Michael","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":872227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordaan, Adrian","contributorId":257709,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":872228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":872229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70244269,"text":"70244269 - 2023 - Lake Ontario August gillnet survey and Lake Trout assessment, 2022","interactions":[],"lastModifiedDate":"2023-06-12T11:42:54.867391","indexId":"70244269","displayToPublicDate":"2023-04-06T06:40:05","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Lake Ontario August gillnet survey and Lake Trout assessment, 2022","docAbstract":"Lake Ontario Lake Trout (Salvelinus namaycush) rehabilitation has been annually assessed with fishery independent surveys since 1983, in an effort to evaluate program benchmarks and compare observations with management objectives. These surveys provide information on the abundance, strain composition, and condition of the adult Lake Trout stock, as well as information on levels of natural recruitment, Sea Lamprey (Petromyzon marinus) wounding rates, and abundance indices of other coldwater fish species (Burbot Lota lota, Cisco Coregonus artedi, and Lake Whitefish C. clupeaformis). In 2022, the catch per unit effort (CPUE) of total Lake Trout in gillnets remained high (18.9 fish/lift; highest since 1998) compared to lows observed during 2005–2009 (average = 7.5 fish/lift). The CPUE of immature Lake Trout in the 2022 survey was the highest since 1994. Wild-produced mature Lake Trout remain rare in the adult population (2.4% of adult catch). Strain composition of stocked fish indicated more than half (56%) of all coded-wire tagged Lake Trout captured in 2022 were from the Superior Klondike strain. Sea Lamprey wounding rates were above target levels in 2022 (3.15 A1 wounds per 100 Lake Trout) and were nearly double the 2021 rate. Lake Trout condition (predicted weight at length) was the highest since data collection began in 1983. Overall, the 2022 survey results indicate that adult Lake Trout are abundant and of high condition but composed mostly of hatchery-origin strains, suggesting recruitment of wild-produced offspring to the adult stock continues to be limited.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"O’Malley, B., Lantry, B.F., Minihkeim, S.P., Mckenna, J.D., Goretzke, J., Gatch, A.J., and Gorsky, D., 2023, Lake Ontario August gillnet survey and Lake Trout assessment, 2022, 12 p.","productDescription":"12 p.","ipdsId":"IP-151036","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":417998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417990,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/pubs/lake_committees/common_docs/Ontario_2022_LakeTroutReport.pdf"}],"country":"United States","otherGeospatial":"Lake 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Broadly, environmental conditions are expected to become warmer, potentially drier (particularly in arid regions), and more anthropogenically developed in the future, with spatiotemporally complex effects on ecological communities. We used functional traits to inform Chesapeake Bay Watershed fish responses to future climate and land-use scenarios (2030, 2060, and 2090). We modelled the future habitat suitability of focal species representative of key trait axes (substrate, flow, temperature, reproduction, and trophic) and used functional and phylogenetic metrics to assess variable assemblage responses across physiographic regions and habitat sizes (headwaters through large rivers). Our focal species analysis projected future habitat suitability gains for carnivorous species with preferences for warm water, pool habitats, and fine or vegetated substrates. At the assemblage level, models projected decreasing habitat suitability for cold-water, rheophilic, and lithophilic individuals but increasing suitability for carnivores in the future across all regions. Projected responses of functional and phylogenetic diversity and redundancy differed among regions. Lowland regions were projected to become less functionally and phylogenetically diverse and more redundant while upland regions (and smaller habitat sizes) were projected to become more diverse and less redundant. Next, we assessed how this model projected assemblage changes 2005-2030 related to observed time-series trends (1999–2016). Halfway through the initial projecting period (2005–2030), we found observed trends broadly followed modelled patterns of increasing proportions of carnivorous and lithophilic individuals in lowland regions but showed opposing patterns for functional and phylogenetic metrics. Leveraging observed and predicted analyses simultaneously helps elucidate the instances and causes of discrepancies between model predictions and ongoing observed changes. Collectively, results highlight the complexity of global change impacts across broad landscapes that likely relate to differences in assemblages’ intrinsic sensitivities and external exposure to stressors.
Landsat Collection 2 (C2) U.S. Analysis Ready Data (U.S. ARD) are bundles of tiled Landsat data that make the Landsat archive easier to analyze and reduce the amount of time users spend on data processing for time-series analysis. Landsat C2 was released in 2020 and includes improvements over Landsat Collection 1 data, including better geometric accuracy, which increases the number of available C2 U.S. ARD tiles. Landsat C2 U.S. ARD are processed to the highest scientific standards.
Landsat C2 U.S. ARD are available for the conterminous United States (1982–present), Alaska (1984–present), and Hawaii (1989–93 and 1999–present) using Landsat C2 Level-1 data processed into Albers Equal-Area Conic-projected Level-2 surface reflectance and surface temperature products.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Salinity is known to affect drinking-water supplies and damage irrigated agricultural lands. Selenium in high concentrations is harmful to fish and other wildlife. Land managers, water providers, and agricultural producers in the lower Gunnison River Basin in western Colorado expend resources mitigating the effects of these constituents. The U.S. Geological Survey revised existing salinity (total dissolved solids) and selenium models for the lower Gunnison River Basin in an attempt to better identify areas of greatest salinity and selenium yield. This effort developed maps of yields predicted from multiple linear regression (MLR) models for the lower Gunnison River Basin. The models included data for irrigation and nonirrigation seasons and two periods, 1992–2004 and 2005–13.
Concentrations of salinity and selenium and discharge measurements made at the time of sampling were used to compute loads for subbasins (component drainages of the larger lower Gunnison River Basin study area), which were adjusted for inflows and outflows of canal loads. Load regression equations were determined from explanatory basin characteristics that included physical properties, precipitation, land use and cover, surficial deposits (soil and unconsolidated geologic materials), and bedrock geology. Loads of salinity and selenium were converted to yields by using the subbasin drainage areas, and an empirical Bayesian kriging procedure was used to produce robust grids of yields for salinity and selenium.
Salinity yields ranged from 0.00667 to 6.564 tons per year per acre. The highest salinity yields, greater than about 5.0 tons per year per acre, are predicted on the western side of the Uncompahgre River upstream from Delta, Colorado, an area with a high density of irrigated land. The selenium yield map shows a similar pattern, but the highest yields are somewhat more confined to the eastern side of the lower Uncompahgre River and south of the Gunnison River near the confluence with the Uncompahgre River at Delta, Colorado. Selenium yields ranged from 2.6888 x 10-10 to 0.000445 pounds per day per acre. The highest predicted selenium yields, greater than 0.0003 pounds per day per acre, were in the area downstream from Montrose, Colorado, on the eastern side of the Uncompahgre River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235013","collaboration":"Prepared in cooperation with the Bureau of Reclamation and the Colorado Water Conservation Board","usgsCitation":"Williams, C.A., Gidley, R.G., and Stevens, M.R., 2023, Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013: U.S. Geological Survey Scientific Investigations Report 2023–5013, 37 p., https://doi.org/10.3133/sir20235013.","productDescription":"Report: vi, 37 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-127438","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":415136,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CW7Q1N","text":"USGS data release","linkHelpText":"Basin Characteristics and Salinity and Selenium Loads and Yields for Selected Subbasins in the Lower Gunnison River Basin, Western Colorado, 1992─2013"},{"id":415232,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5013/images"},{"id":415233,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5013/sir20235013.xml"},{"id":415255,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5013"},{"id":415135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5013/sir20235013.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5013"},{"id":415134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5013/coverthb.jpg"},{"id":415137,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":500707,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114651.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.66041487616633,\n 38.99638415429618\n ],\n [\n -108.6616273692395,\n 39.00186401594732\n ],\n [\n -108.6616273692395,\n 38.9967055439632\n ],\n [\n -108.6881779535584,\n 38.77194822283556\n ],\n [\n -108.58197561628282,\n 38.556862390281765\n ],\n [\n -108.19035825380965,\n 38.16912358075567\n ],\n [\n -108.12730061605224,\n 37.915589610235415\n ],\n [\n -107.96135946405924,\n 37.80029529902727\n ],\n [\n -107.76886772774775,\n 37.81078405090291\n ],\n [\n -107.61846017361093,\n 38.26998042097301\n ],\n [\n -107.25524694190712,\n 38.623585049765126\n ],\n [\n -107.0055365452011,\n 38.85181039167898\n ],\n [\n -106.90464345562309,\n 38.99114000809618\n ],\n [\n -106.95004534593326,\n 39.07538965450817\n ],\n [\n -107.02178619634307,\n 39.241636232003856\n ],\n [\n -107.71852594996861,\n 39.17165133541914\n ],\n [\n -108.1185061789019,\n 39.03147242299278\n ],\n [\n -108.37655793950398,\n 38.98134099584442\n ],\n [\n -108.55719417192573,\n 39.05652481206508\n ],\n [\n -108.66041487616633,\n 38.99638415429618\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
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Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to conventional oil and natural gas accumulations. The specific provinces are the Campos Basin (Brazil), the Santos Basin (Brazil), the North Sea Graben (northwestern Europe), the Middle Magdelena Basin (Colombia), the Sirte Basin (Libya), and the Kutei Basin (Indonesia). For each province, discovery data and well data through October 2019 are reported; from these data, depth distributions of the oil in oil fields and natural gas in gas fields were computed.
The concepts of delineated prospective area and explored area include elements of geographic spatial information and statistical data analytics. Graphs showing dynamic growth of discoveries that are tied to the delineated prospective area provide a means of grading prospective area. Visualizations put the results of exploration in the context of geographic and geologic features of the play or basin and can be a tool to assist geologists with the appraisal of the number and sizes of undiscovered petroleum accumulations. Visualizations of exploration drilling and discoveries can (1) assist in conceptualizing a geologic model of the basin, (2) highlight relations among discovered accumulations in different plays or assessment units within the basin, and (3) allow the geologist to identify the missing information needed to complete the geologic model of a basin. Further, if visualization attributes can be quantified, they may be used for formulating quantitative models that predict numbers and sizes of undiscovered oil and gas accumulations. Such modeling approaches include discovery process models, Bayesian network models that characterize play or assessment unit dependencies, and innovative applications of machine learning to complement standard geologic assessments.
The purpose of this report is to show how visualizations can further the understanding of exploration maturity for the six selected petroleum provinces. It also shows how the geologic framework, geologic data, and drilling and discovery trends can give context to the interpretation of the visualizations that lead to appraisal of exploration maturity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235010","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2023, Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada: U.S. Geological Survey Scientific Investigations Report 2023–5010, 38 p., https://doi.org/10.3133/sir20235010.","productDescription":"viii, 38 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119047","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":414671,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235010/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 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Reston, VA 20192
This preliminary geologic map presents mapping of the Leuhman Ridge area of Edwards Air Force Base, California, conducted between April 2020 and June 2021. The report focuses on surficial materials and bedrock to evaluate potential faults and other geologic features that may influence groundwater movement. The preliminary work confirms that the Spring Fault, previously mapped by Dibblee (1960, 1967), is a Quaternary-active fault but does not find convincing evidence to support the existence of the Leuhman Fault (Dibblee, 1960; 1967) within the map area. Several more possible and probable faults are identified by a combination of geomorphic lineaments and brecciated rock. Pleistocene and Holocene eolian deposits are widespread, manifesting as sand sheets, dunes, and admixtures into alluvial fans. Also, an incised pediment forms much of the upland south of Leuhman Ridge. In general, field observations indicate that Quaternary alluvial and eolian deposits are thin; this suggests that secondary bedrock porosity and permeability, defined by degree of weathering and fracture density that includes fault-related fracturing, are more important factors in the location and flow patterns of groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231014","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center","usgsCitation":"Cyr, A.J., and Miller, D.M., 2023, Preliminary surficial geologic map of Leuhman Ridge and the surrounding area, Edwards Air Force Base and Air Force Research Laboratory, Kern and San Bernardino Counties, California: U.S. Geological Survey Open-File Report 2023–1014, 1 sheet, scale 1:18,000, pamphlet 15 p., https://doi.org/10.3133/ofr20231014.","productDescription":"Report: v, 15 p.; 1 Sheet: 47.90 × 32.88 inches; 2 Data Releases","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129408","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":415172,"rank":1,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2023/1014/ofr20231014_pamphlet.pdf","text":"Pamphlet","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415174,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1014/covrthb.jpg"},{"id":415173,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2023/1014/ofr20231014_sheet.pdf","text":"Sheet","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415175,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WTKPBP","text":"Digital database for the preliminary surficial geologic map of Leuhman Ridge and the surrounding area, Edwards Air Force Base and Air Force Research Laboratory, Kern and San Bernardino Counties, California","description":"Cyr, A.J., and Miller, D.M., 2023, Digital database for the preliminary surficial geologic map of Leuhman Ridge and the surrounding area, Edwards Air Force Base and Air Force Research Laboratory, Kern and San Bernardino Counties, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9WTKPBP."},{"id":499737,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114652.htm","linkFileType":{"id":5,"text":"html"}},{"id":415176,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IXG2JX","text":"Data release for luminescence—Edwards Air Force Base (CA) and CA Water Science Center report including luminescence data and ages","description":"Mahan, S.A., Gray, H.J., Cyr, A.J., Krolczyk, E.T., and Miller, D.M., 2021, Data release for luminescence—Edwards Air Force Base (CA) and CA Water Science Center report including luminescence data and ages: U.S. Geological Survey data release, https://doi.org/10.5066/P9IXG2JX."}],"country":"United States","state":"California","county":"Kern County, San Bernardino County","otherGeospatial":"Edwards Air Force Base and Air Force Research Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.40677059742858,\n 35.245770173761414\n ],\n [\n -118.40677059742858,\n 34.35458990295869\n ],\n [\n -116.97271146209192,\n 34.35458990295869\n ],\n [\n -116.97271146209192,\n 35.245770173761414\n ],\n [\n -118.40677059742858,\n 35.245770173761414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Ambystoma barbouri (streamside salamanders) are stream-breeding mole salamanders that rely on seasonally intermittent, fishless streams for egg and larval development but are primarily fossorial as adults. Climate-driven changes are likely to alter streamflow duration, peak, and seasonality within the range of A. barbouri, reducing reproductive habitat and larval survival. Although future changes in precipitation volume within the geographic range of A. barbouri are uncertain, in the next 90 years, increasing temperatures will likely increase potential evapotranspiration. Decreasing ratio of precipitation to potential evapotranspiration will likely shorten flow duration for intermittent streams, potentially causing earlier stream dry downs before larval metamorphosis. Increased temperatures may also shorten developmental periods buffering A. barbouri larvae from the effects of increased stream no-flow days. Additionally, precipitation in the future will increasingly fall in heavy rainfall events. Heavy rain and subsequent flooding during early larval stages may displace A. barbouri larvae from fishless pools into downstream reaches with vertebrate predators that can reduce survival. Finally, agriculture and urban land cover may amplify the stresses of climate change on A. barbouri, altering reproductive habitat and reducing survival of larval, juvenile, and adult life stages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104C","usgsCitation":"Lyons, M.P., LeDee, O.E., and Boyles, R., 2023, Potential effects of climate change on Ambystoma barbouri (streamside salamander): U.S. Geological Survey Open-File Report 2021–1104–C, 14 p., https://doi.org/10.3133/ofr20211104C.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-138888","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":415229,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211104C/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":415166,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798865X","linkHelpText":"U.S. Geological Survey—Gap Analysis Project species range maps CONUS_2001"},{"id":415165,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/c/images"},{"id":415164,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/c/ofr20211104C.XML"},{"id":415163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/c/ofr20211104c.pdf","text":"Report","size":"1.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1104–C"},{"id":415162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/c/coverthb.jpg"}],"country":"United States","state":"Illinois, Indiana, Kentucky, Ohio, Tennessee, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.77637660661436,\n 37.29693429192959\n ],\n [\n -88.77637660661436,\n 36.81080154311158\n ],\n [\n -88.117170430842,\n 36.81080154311158\n ],\n [\n -88.117170430842,\n 37.29693429192959\n ],\n [\n -88.77637660661436,\n 37.29693429192959\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.87385719670998,\n 36.41563025729535\n ],\n [\n -86.87385719670998,\n 35.033754255529374\n ],\n [\n -85.60551113699587,\n 35.033754255529374\n ],\n [\n -85.60551113699587,\n 36.41563025729535\n ],\n [\n -86.87385719670998,\n 36.41563025729535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.70166692228476,\n 38.46265353132972\n ],\n [\n -82.70166692228476,\n 37.89197947744543\n ],\n [\n -82.06749389242742,\n 37.89197947744543\n ],\n [\n -82.06749389242742,\n 38.46265353132972\n ],\n [\n -82.70166692228476,\n 38.46265353132972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.22603714989201,\n 39.184248376047606\n ],\n [\n -83.62789332115464,\n 39.35866418136621\n ],\n [\n -84.06180118368843,\n 39.860090114776256\n ],\n [\n -84.02007927382951,\n 40.032813749497734\n ],\n [\n -85.0297494924173,\n 39.712611372960964\n ],\n [\n -85.78908825185144,\n 39.0353297194157\n ],\n [\n -86.43160566367993,\n 38.38420573496376\n ],\n [\n -86.36485060790544,\n 37.746963993740465\n ],\n [\n -84.23703320509641,\n 37.48919160202678\n ],\n [\n -83.66961523101361,\n 38.04328175226868\n ],\n [\n -82.55981242876403,\n 38.808111070451616\n ],\n [\n -82.22603714989201,\n 39.184248376047606\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Midwest Climate Adaptation Science Center
U.S. Geological Survey
1954 Buford Avenue
St. Paul, MN 55108
Dynamic earthquake rupture simulations are used to understand earthquake mechanics and the ground shaking that earthquakes produce. These simulations can help diagnose past earthquake behavior and are also used to generate scenarios of possible future earthquakes. Traditional dynamic rupture models generally assume elastic rock response, but this can lead to peak on‐fault slip rates and ground shaking that are higher than those inferred from seismological observations. Some have approached this challenge using inelastic off‐fault rock behavior to dissipate energy, but the addition of inelasticity can make it difficult to select parameters and establish suitable initial conditions, and increases the model’s complexity and computational cost. We propose a new method that works by adding a nonlinear radiation damping term to the friction law, with the surrounding rocks remaining linear elastic. Our new method results in lower peak slip rates, reduced seismic radiation, and an increasing slip‐weakening critical distance with increasing rupture propagation distance, all within a linear elastic model. In addition, it is easy to implement.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230001","usgsCitation":"Barall, M., and Harris, R.A., 2023, Nonlinear radiation damping: A new method for dissipating energy in dynamic earthquake rupture simulations: The Seismic Record, v. 3, no. 2, p. 69-76, https://doi.org/10.1785/0320230001.","productDescription":"8 p.","startPage":"69","endPage":"76","ipdsId":"IP-144269","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230001","text":"Publisher Index Page"},{"id":419736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Barall, Michael 0000-0001-7724-8563 mbarall@usgs.gov","orcid":"https://orcid.org/0000-0001-7724-8563","contributorId":271197,"corporation":false,"usgs":true,"family":"Barall","given":"Michael","email":"mbarall@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":880016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Ruth A. 0000-0002-9247-0768 harris@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-0768","contributorId":786,"corporation":false,"usgs":true,"family":"Harris","given":"Ruth","email":"harris@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":880017,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255034,"text":"70255034 - 2023 - High-resolution recording of foraging behaviour over multiple annual cycles shows decline in old Adélie penguins’ performance","interactions":[],"lastModifiedDate":"2024-06-12T14:10:31.29842","indexId":"70255034","displayToPublicDate":"2023-04-05T08:59:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3173,"text":"Proceedings of the Royal Society B","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution recording of foraging behaviour over multiple annual cycles shows decline in old Adélie penguins’ performance","docAbstract":"Age-related variation in foraging performance can result from both within-individual change and selection processes. These mechanisms can only be disentangled by using logistically challenging long-term, longitudinal studies. Coupling a long-term demographic data set with high-temporal-resolution tracking of 18 Adélie penguins (Pygoscelis adeliae, age 4–15 yrs old) over three consecutive annual cycles, we examined how foraging behaviour changed within individuals of different age classes. Evidence indicated within-individual improvement in young and middle-age classes, but a significant decrease in foraging dive frequency within old individuals, associated with a decrease in the dive descent rate. Decreases in foraging performance occurred at a later age (from 12–15 yrs old to 15–18 yrs old) than the onset of senescence predicted for this species (9–11 yrs old). Foraging dive frequency was most affected by the interaction between breeding status and annual life-cycle periods, with frequency being highest during returning migration and breeding season and was highest overall for successful breeders during the chick-rearing period. Females performed more foraging dives per hour than males. This longitudinal, full annual cycle study allowed us to shed light on the changes in foraging performance occurring among individuals of different age classes and highlighted the complex interactions among drivers of individual foraging behaviour.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2022.2480","usgsCitation":"Lescroël, A., Schmidt, A., Ainley, D., Dugger, K., Elrod, M., Jongsomjit, D., Morandini, V., Winquist, S., and Ballard, G., 2023, High-resolution recording of foraging behaviour over multiple annual cycles shows decline in old Adélie penguins’ performance: Proceedings of the Royal Society B, v. 290, no. 1996, 20222480, 10 p., https://doi.org/10.1098/rspb.2022.2480.","productDescription":"20222480, 10 p.","ipdsId":"IP-147917","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443946,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10261/309804","text":"External Repository"},{"id":430010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"290","issue":"1996","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lescroël, Amélie","contributorId":338339,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amélie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Annie","contributorId":338340,"corporation":false,"usgs":false,"family":"Schmidt","given":"Annie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ainley, David G.","contributorId":338341,"corporation":false,"usgs":false,"family":"Ainley","given":"David G.","affiliations":[{"id":81117,"text":"H. T. Harvey & Associates Ecological Consultants","active":true,"usgs":false}],"preferred":false,"id":903188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elrod, Megan","contributorId":338342,"corporation":false,"usgs":false,"family":"Elrod","given":"Megan","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jongsomjit, Dennis","contributorId":338343,"corporation":false,"usgs":false,"family":"Jongsomjit","given":"Dennis","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903191,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morandini, Virginia","contributorId":338344,"corporation":false,"usgs":false,"family":"Morandini","given":"Virginia","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903192,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winquist, Suzanne","contributorId":338345,"corporation":false,"usgs":false,"family":"Winquist","given":"Suzanne","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903193,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ballard, Grant","contributorId":338346,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903194,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243115,"text":"70243115 - 2023 - Assessment of three methods to evaluate the distribution of submersed aquatic vegetation in western Lake Erie","interactions":[],"lastModifiedDate":"2023-05-01T12:29:38.603703","indexId":"70243115","displayToPublicDate":"2023-04-05T07:26:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of three methods to evaluate the distribution of submersed aquatic vegetation in western Lake Erie","docAbstract":"Submersed aquatic vegetation (SAV) plays an important role in ecosystems. Inventories of SAV spatial distribution and composition are important for monitoring changes in SAV. In this study, we compared three common SAV sampling methods to quantify SAV in western Lake Erie. Aerial imagery of near-shore areas in western Lake Erie was classified using object-based image analysis (OBIA) and evaluated against field-based surveys using single-beam sonar or rake samples. To assess variation among methods, data were assigned either vegetation ‘presence’ or ‘absence’ and compared for simple correspondence and agreement (Cohen’s Kappa, κ). The two field-based methods had the highest correspondence at 78% (n = 782) and the highest κ = 0.545. Correspondence between OBIA and rake surveys was 69% (n = 245) and κ = 0.36. Correspondence between OBIA and hydroacoustics was the lowest of 54% (n = 30,768) with an agreement of κ = 0.17. Environmental factors such as water turbidity may have played a role in reduced agreement between OBIA and field methods. Determining the optimal method or combination of methods will depend upon research goals, effort, and cost, but each method can provide reliable SAV information for resource management.
Diurnal basking (“sunning”) is common in many ectotherms and is generally thought to be a behavioural mechanism for thermoregulation. Recent studies have reported the occurrence of nocturnal basking in a few distantly-related species of freshwater turtles, but the true extent of this behaviour is unknown, and it may be underreported due to sampling biases (e.g., not surveying for turtles at night). Therefore, we initiated a global, collaborative effort to systematically document and quantify basking activity (diurnal and nocturnal) across a wide range of freshwater turtle species and locations. We conducted camera trap or manual surveys in North America, the Caribbean, Europe, Asia, Africa, the Seychelles, and Australia. We collected 873,111 trail camera photographs (25,273 hrs of search effort) and obtained data on 29 freshwater turtle species from seven families. Nocturnal basking was documented in 13 species, representing six families (Chelidae, Emydidae, Geoemydidae, Kinosternidae, Pelomedusidae, and Trionychidae), including representatives in Central America, Trinidad and Tobago, Africa, the Seychelles, Asia, and Australia. Nocturnal basking was restricted to tropical and sub-tropical locations, suggesting that environmental temperature plays a role in this behaviour. However, the primary factors driving nocturnal basking are yet to be determined and may vary geographically and by species. The frequency and duration of nocturnal basking varied among species and seasons, but nocturnal basking events were often substantially longer than diurnal events. This is the first study to document a widespread occurrence of nocturnal basking, and our results suggest that nocturnal basking may be a common, although overlooked, aspect of many species’ ecology.
Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.
To inform responsible energy development, it is important to understand the ecological effects of contamination events. Wastewaters, a common byproduct of oil and gas extraction, often contain high concentrations of sodium chloride (NaCl) and heavy metals (e.g., strontium and vanadium). These constituents can negatively affect aquatic organisms, but there is scarce information for how wastewaters influence potentially distinct microbiomes in wetland ecosystems. Additionally, few studies have concomitantly investigated effects of wastewaters on the habitat (water and sediment) and skin microbiomes of amphibians or relationships among these microbial communities. We sampled microbiomes of water, sediment, and skin of four larval amphibian species across a gradient of chloride contamination (0.04–17,500 mg/L Cl) in the Prairie Pothole Region of North America. We detected 3129 genetic phylotypes and 68 % of those phylotypes were shared among the three sample types. The most common shared phylotypes were Proteobacteria, Firmicutes, and Bacteroidetes. Salinity of wastewaters increased dissimilarity within all three microbial communities, but not the diversity or richness of water and skin microbial communities. Strontium was associated with lower diversity and richness of sediment microbial communities, but not those of water or amphibian skin, likely because metal deposition occurs in sediment when wetlands dry. Based on Bray Curtis distance matrices, sediment microbiomes were similar to those of water, but neither had substantial overlap with amphibian microbiomes. Species identity was the strongest predictor of amphibian microbiomes; frog microbiomes were similar but differed from that of the salamander, whose microbiome had the lowest richness and diversity. Understanding how effects of wastewaters on the dissimilarity, richness, and diversity of microbial communities also influence the ecosystem function of communities will be an important next step. However, our study provides novel insight into the characteristics of, and associations among, different wetland microbial communities and effects of wastewaters from energy production.
Xyleborini (Coleoptera: Curculionidae) beetles on the island of Kauaʻi, Hawaiʻi, are of interest due to their role in the fungal disease complex, rapid ʻōhiʻa death (ROD), and the unique radiation of endemic ambrosia beetles found across the Hawaiian archipelago. We investigated the status of RODassociated and native ambrosia beetles on Kauaʻi by rearing beetles from bolts collected from ROD-Ceratocystis infested ʻōhiʻa lehua (ʻōhiʻa; Metrosideros polymorpha) trees and trapping in mixed ʻōhiʻa forests. Beetles associated with ROD on Kauaʻi include Xyleborinus saxesenii, Xyleborus affinis, Xyleborus ferrugineus, Xyleborus perforans, Xylosandrus crassiusculus, and Crossotarsus externedentatus. Xyleborus perforans was most abundant in ʻōhiʻa bolts followed by Xyleborus ferrugineus. From trap captures, we identified new island records of the native Xyleborus beetles including Xyleborus dubiosus, Xyleborus oahuensis, and Xyleborus tantalus. Xylosandrus crassiusculus was most abundant in traps. Additional work could contribute to understanding and mitigating the spread of ROD on Kauaʻi and to documenting the endemic Xyleborus beetles.
","language":"English","publisher":"ScholarSpace","doi":"10125/110083","usgsCitation":"Roy, K., Dunkle, E., Manandhar, R., Clark, M., Magnacca, K.N., Harshman, K., and Peck, R., 2023, Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae and Platypodinae) associated with rapid ohia death and mixed Metrosideros polymorpha forests on the Island of Kauai, Hawaii: Proceedings of the Hawaiian Entomological Society, v. 56, p. 61-71, https://doi.org/10125/110083.","productDescription":"11 p.","startPage":"61","endPage":"71","ipdsId":"IP-169929","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":482910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kauai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.81907120710895,\n 22.25305341906116\n ],\n [\n -159.81907120710895,\n 21.856904765473885\n ],\n [\n -159.2742849994599,\n 21.856904765473885\n ],\n [\n -159.2742849994599,\n 22.25305341906116\n ],\n [\n -159.81907120710895,\n 22.25305341906116\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roy, Kylle 0000-0002-7993-9031","orcid":"https://orcid.org/0000-0002-7993-9031","contributorId":213271,"corporation":false,"usgs":true,"family":"Roy","given":"Kylle","email":"","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":929665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunkle, Ellen 0000-0002-7081-0717","orcid":"https://orcid.org/0000-0002-7081-0717","contributorId":244898,"corporation":false,"usgs":false,"family":"Dunkle","given":"Ellen","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":929666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manandhar, Roshan","contributorId":332580,"corporation":false,"usgs":false,"family":"Manandhar","given":"Roshan","affiliations":[{"id":79499,"text":"University of Hawaiʻi at Mānoa, College of Tropical Agriculture and Human Resources","active":true,"usgs":false}],"preferred":false,"id":929667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Michelle","contributorId":332581,"corporation":false,"usgs":false,"family":"Clark","given":"Michelle","affiliations":[{"id":79500,"text":"U.S. Fish and Wild Life Service, Pacific Islands Fish and Wildlife Office","active":true,"usgs":false}],"preferred":false,"id":929668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Magnacca, Karl N.","contributorId":173504,"corporation":false,"usgs":false,"family":"Magnacca","given":"Karl","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":929669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harshman, Kalli","contributorId":332582,"corporation":false,"usgs":false,"family":"Harshman","given":"Kalli","affiliations":[{"id":79501,"text":"State of Hawai’i Department of Land and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":929670,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":929671,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242042,"text":"70242042 - 2023 - Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","interactions":[],"lastModifiedDate":"2023-04-07T16:56:20.79635","indexId":"70242042","displayToPublicDate":"2023-04-04T11:47:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","title":"Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals","docAbstract":"One of the key factors in obtaining precise and accurate 230Th ages of corals, especially for corals with ages less than a few thousand years, is the correction for non-radiogenic 230Th based on an initial 230Th/232Th value (230Th/232Th0). Studies that consider coral 230Th/232Th0 values in intertidal environments are limited, and it is in these environments that corals have Th concentrations 100–1000 times greater than open-ocean corals. Here we present 66 reconstructed U–Th isochrons of modern and Holocene Porites lutea and lobata corals from throughout the Sumatran forearc islands (SFI). Mean square of weighted deviates (MSWD) is used to evaluate the variabilities of 28 location-specific 230Th/232Th0 values before the regional mean. Results show no significant spatial difference between the means of 3.1 ± 3.2 × 10−6 for the northern SFI and 4.7 ± 1.3 × 10−6 for the southern SFI. Using the weighted mean of 4.3 ± 2.5 × 10−6 for all islands has the advantage that no data need be subjectively excluded in the calculation of reliable 230Th ages, where a local initial value is unknown. There were no clear centennial to millennial time-scale changes in 230Th/232Th0 in the past 2500 years. This study, therefore, suggests a reliable value of initial 230Th/232Th ratio in intertidal environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2023.108005","usgsCitation":"Chiang, H., Philibosian, B.E., Meltzner, A.J., Wu, C., Shen, C., Edwards, R.L., Chuang, C., Suwargadi, B.W., and Natawidjaja, D.H., 2023, Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals: Quaternary Science Reviews, v. 307, 108005, 11 p., https://doi.org/10.1016/j.quascirev.2023.108005.","productDescription":"108005, 11 p.","ipdsId":"IP-125955","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2023.108005","text":"Publisher Index Page"},{"id":415422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sumatran forearc islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94,\n 3.5\n ],\n [\n 94,\n -3.5\n ],\n [\n 103,\n -3.5\n ],\n [\n 103,\n 3.5\n ],\n [\n 94,\n 3.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"307","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chiang, Hong-Wei","contributorId":193421,"corporation":false,"usgs":false,"family":"Chiang","given":"Hong-Wei","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":868667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":868669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chung-Che","contributorId":193422,"corporation":false,"usgs":false,"family":"Wu","given":"Chung-Che","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chuan-Chou","contributorId":193424,"corporation":false,"usgs":false,"family":"Shen","given":"Chuan-Chou","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence 0000-0002-7027-5881","orcid":"https://orcid.org/0000-0002-7027-5881","contributorId":223143,"corporation":false,"usgs":false,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":868672,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chuang, Chih-Kai","contributorId":303947,"corporation":false,"usgs":false,"family":"Chuang","given":"Chih-Kai","email":"","affiliations":[{"id":30216,"text":"National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868673,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Suwargadi, Bambang W.","contributorId":150205,"corporation":false,"usgs":false,"family":"Suwargadi","given":"Bambang","email":"","middleInitial":"W.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Natawidjaja, Danny H.","contributorId":150204,"corporation":false,"usgs":false,"family":"Natawidjaja","given":"Danny","email":"","middleInitial":"H.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243078,"text":"70243078 - 2023 - 22 years of aquatic plant spatiotemporal dynamics in the upper Mississippi River","interactions":[],"lastModifiedDate":"2023-04-28T11:43:52.690731","indexId":"70243078","displayToPublicDate":"2023-04-04T06:41:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"22 years of aquatic plant spatiotemporal dynamics in the upper Mississippi River","docAbstract":"Since its introduction to volcanology in the mid-2000 s, the SO2 camera has become an important instrument for the acquisition of accurate and high time-resolution SO2 emission rates, aiding in hazard assessment and volcanological research. However, with the exception of a few locations (Stromboli, Etna, Kīlauea), hitherto the majority of measurements have been made on discrete field campaigns, which provide only brief snapshots into a volcano’s activity. Here, we present the development of a new, low-cost, low-power SO2 camera for permanent deployment on volcanoes, facilitating long-term, quasi-continuous (daylight hours only) measurements. We then discuss preliminary datasets from Lascar and Kīlauea volcanoes, where instruments are now in continuous operation. Further proliferation of such instrumentation has the potential to greatly improve our understanding of the transient nature of volcanic activity, as well as aiding volcano monitoring/eruption forecasting.
The Blackfeet Nation seeks an increased scientific understanding of the water resources within the Blackfeet Indian Reservation of northwestern Montana. Hydrologic information is needed to better inform water-management decisions as the Blackfeet Nation implements the Blackfeet Water Rights Compact, initiates new water-use projects, and improves the Blackfeet Irrigation Project.
The U.S. Geological Survey and the Blackfeet Water Department began cooperating in 2019 to design and implement a hydrologic data-collection program. The program is being implemented in phases that include discrete and continuous discharge measurements of streams and canals, installation, operation of streamgages, groundwater-level monitoring, and database management. Data collected will be used to characterize current hydrologic conditions on the reservation and will act as a baseline for comparison as Blackfeet Nation water projects are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233012","collaboration":"Prepared in cooperation with the Blackfeet Water Department","usgsCitation":"Lawlor, S.M., Caldwell, R.R., Bartos, T.T., and Price, B., 2023, U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana: U.S. Geological Survey Fact Sheet 2023–3012, 4 p., https://doi.org/10.3133/fs20233012.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141394","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":414914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3012/coverthb2.jpg"},{"id":414918,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3012/images"},{"id":414919,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":415102,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233012/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":499662,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114650.htm","linkFileType":{"id":5,"text":"html"}},{"id":414917,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3012"}],"country":"United States","state":"Montana","otherGeospatial":"Blackfeet Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.92382337824559,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 49.00115786005651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Within the Gulf of Alaska, in the North Pacific Ocean, three major events - both natural and human-caused – resulted in large-scale ecosystem changes during the last 50 years.
","language":"English","publisher":"Open Access Government","doi":"10.56367/OAG-038-10678","usgsCitation":"Suryan, R.M., Lindeberg, M., Arimitsu, M.L., Esler, D., Coletti, H., Hopcroft, R., and Pegau, W.S., 2023, Gulf watch Alaska: Long-term research and monitoring in the Gulf of Alaska: Open Access Government, v. 38, no. 1, p. 468-469, https://doi.org/10.56367/OAG-038-10678.","productDescription":"2 p.","startPage":"468","endPage":"469","ipdsId":"IP-148894","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443972,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.56367/oag-038-10678","text":"Publisher Index Page"},{"id":415847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.4399134359869,\n 52.574048355723704\n ],\n [\n -135.17789540456553,\n 56.85438463524653\n ],\n [\n -139.82767703845167,\n 59.79957173693737\n ],\n [\n -147.2251844144387,\n 61.1241382977862\n ],\n [\n -151.09164861277324,\n 58.87023875501092\n ],\n [\n -151.2988466630248,\n 60.668331578783324\n ],\n [\n -153.50284160357148,\n 59.11972555702502\n ],\n [\n -158.91367720917896,\n 55.710837343186455\n ],\n [\n -162.81404039809868,\n 54.85301697024397\n ],\n [\n -132.4399134359869,\n 52.574048355723704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Suryan, Robert M. 0000-0003-0755-8317","orcid":"https://orcid.org/0000-0003-0755-8317","contributorId":221852,"corporation":false,"usgs":false,"family":"Suryan","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40443,"text":"Oregon State University, NOAA","active":true,"usgs":false}],"preferred":false,"id":869688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindeberg, Mandy","contributorId":195895,"corporation":false,"usgs":false,"family":"Lindeberg","given":"Mandy","email":"","affiliations":[],"preferred":false,"id":869689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":869690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":869691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coletti, Heather","contributorId":258849,"corporation":false,"usgs":false,"family":"Coletti","given":"Heather","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":869692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hopcroft, Russell","contributorId":258854,"corporation":false,"usgs":false,"family":"Hopcroft","given":"Russell","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":869693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pegau, W. Scott","contributorId":304200,"corporation":false,"usgs":false,"family":"Pegau","given":"W.","email":"","middleInitial":"Scott","affiliations":[{"id":13600,"text":"Prince William Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":869694,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242647,"text":"70242647 - 2023 - Population dynamics and harvest management of eastern mallards","interactions":[],"lastModifiedDate":"2023-06-09T15:15:15.702993","indexId":"70242647","displayToPublicDate":"2023-04-03T07:03:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population dynamics and harvest management of eastern mallards","docAbstract":"Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision-making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2-season banding and recovery data, and hunter-harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub-model for survival including estimating the degree of harvest additivity and any age-specific trends. The top survival sub-model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.
The development of sample processing techniques that recover a broad suite of pesticides from solid matrices, while mitigating coextracted matrix interferences, and reducing processing time is beneficial for high throughput analyses. The objective of this study was to evaluate the effectiveness of an automated extraction system for pesticide analyses in solid environmental samples. An Energized Dispersive Guided Extraction (EDGE) system was used to evaluate two different extraction solvents in optimizing the extraction of 210 pesticides and pesticide transformation products. A graphitized carbon cleanup step was implemented, and three elution solvents were evaluated separately for analyte recoveries. Recoveries between 70 and 130% were achieved for 167 compounds in a test soil using acetonitrile as an extraction solvent and carbon cleanup with acetonitrile and dichloromethane elutions. Nine field samples (soil, sediment, and biosolids) were extracted using the newly developed method and were compared with a previously validated pressurized liquid extraction (PLE) method using an Accelerated Solvent Extraction (ASE) system. Concentrations obtained from the two methods were comparable (linear R2 > 0.999), suggesting similar performance between the EDGE and PLE extractions in complex matrices. The new method provided slightly better sensitivities in comparison to the PLE method, ranging from 0.09 to 2.56 ng g−1. The method presented here significantly reduces extraction setup and runtimes while also minimizing the volume of carcinogenic solvents (e.g., dichloromethane) used in the laboratory and presents a sensitive multiresidue method for a wide range of pesticides in solid matrices.
Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2214199120","usgsCitation":"Wagner, T., Schliep, E., North, J., Kundel, H., Custer, C., Ruzich, J., and Hansen, G., 2023, Predicting climate change impacts on poikilotherms using physiologically guided species abundance models: Ecology, v. 120, no. 15, e2214199120, 8 p., https://doi.org/10.1073/pnas.2214199120.","productDescription":"e2214199120, 8 p.","ipdsId":"IP-143425","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2214199120","text":"Publisher Index Page"},{"id":480740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Minnesota","active":true,"usgs":false}],"preferred":false,"id":924433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hansen, Gretchen J.A.","contributorId":349370,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924263,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260468,"text":"70260468 - 2023 - Protocol for the reintroduction of California red-legged frogs to Santa Monica Mountains National Recreation Area","interactions":[],"lastModifiedDate":"2024-11-05T15:42:08.317008","indexId":"70260468","displayToPublicDate":"2023-04-01T11:00:56","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SAMO/NRR—2023/2507","title":"Protocol for the reintroduction of California red-legged frogs to Santa Monica Mountains National Recreation Area","docAbstract":"Once common and widespread in Southern California, California red-legged frogs (Rana draytonii) began declining sometime in the middle of the 20th century. They were listed as threatened under the Endangered Species Act in 1996. Three small and isolated populations remained in Los Angeles and Ventura Counties by the start of the 21st century. The nearest population of California red-legged frogs to Santa Monica Mountains National Recreation Area is critically small, located 15 km to the north, yet there is evidence of persistence, including successful reproduction each year it has been measured. A potential solution to alleviate small population size and isolation is to reintroduce a species back to habitable historical locations nearby. In 2011, we initiated a project to reintroduce California red-legged frogs back to the Santa Monica Mountains, where historical records showed they were once widespread. We developed a procedure to transfer partial egg masses into tadpole rearing pens located within streams determined to be suitable for the species. This translocation protocol outlines our procedure and results for the first five years of the project. It is our hope that this protocol will guide and inform similar conservation efforts for California red-legged frogs in other parts of their range as well as other amphibian conservation efforts throughout the world.
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