Background
Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae.
Results
A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi. No evidence was found for genetic diversification across depth in G. childressi, likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments.
Conclusions
These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
","language":"English","publisher":"Springer","doi":"10.1186/s12862-022-02027-4","usgsCitation":"DeLeo, D.M., Morrison, C., Sei, M., Salamone, V.J., Demopoulos, A., and Quattrini, A., 2022, Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin: BMC Ecology & Evolution, v. 22, 76, 16 p., https://doi.org/10.1186/s12862-022-02027-4.","productDescription":"76, 16 p.","ipdsId":"IP-138328","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12862-022-02027-4","text":"Publisher Index Page"},{"id":403719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Ocean, Baltimore Canyon Seep, Blake Ridge Seep, Chincoteague Seep, Norfolk Canyon Seep","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.52001953125,\n 36.54494944148322\n ],\n [\n -74.68505859374999,\n 36.049098959065645\n ],\n [\n -73.2568359375,\n 37.94419750075404\n ],\n [\n -74.33349609375,\n 38.41055825094609\n ],\n [\n -75.52001953125,\n 36.54494944148322\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.70654296875,\n 31.484893386890164\n ],\n [\n -74.92675781249999,\n 31.484893386890164\n ],\n [\n -74.92675781249999,\n 32.89803818160521\n ],\n [\n -76.70654296875,\n 32.89803818160521\n ],\n [\n -76.70654296875,\n 31.484893386890164\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"DeLeo, Danielle M.","contributorId":293171,"corporation":false,"usgs":false,"family":"DeLeo","given":"Danielle","email":"","middleInitial":"M.","affiliations":[{"id":63241,"text":"Smithsonian National Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":846576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Cheryl 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":202644,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":846577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sei, Mariki","contributorId":293173,"corporation":false,"usgs":false,"family":"Sei","given":"Mariki","email":"","affiliations":[],"preferred":false,"id":846578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salamone, Veronica J. 0000-0002-6274-6401","orcid":"https://orcid.org/0000-0002-6274-6401","contributorId":293174,"corporation":false,"usgs":true,"family":"Salamone","given":"Veronica","email":"","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":846579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":221145,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":846580,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quattrini, Andrea M. 0000-0002-4247-3055","orcid":"https://orcid.org/0000-0002-4247-3055","contributorId":62339,"corporation":false,"usgs":false,"family":"Quattrini","given":"Andrea M.","affiliations":[],"preferred":false,"id":846581,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234409,"text":"70234409 - 2022 - Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security","interactions":[],"lastModifiedDate":"2022-08-11T14:23:16.553989","indexId":"70234409","displayToPublicDate":"2022-07-13T08:15:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security","docAbstract":"Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2088651","usgsCitation":"Gumma, M., Thenkabail, P., Panjala, P., Teluguntla, P., Yamano, T., and Mohammad, I., 2022, Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security: GIScience & Remote Sensing, v. 59, no. 1, p. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.","productDescription":"30 p.","startPage":"1048","endPage":"1077","ipdsId":"IP-135578","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2088651","text":"Publisher Index 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Murali Krishna","contributorId":294754,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali Krishna","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":848826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panjala, Pranay","contributorId":294756,"corporation":false,"usgs":false,"family":"Panjala","given":"Pranay","email":"","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi","contributorId":294758,"corporation":false,"usgs":false,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":63639,"text":"Bay Area Environmental Research Institute (BAERI) @ USGS","active":true,"usgs":false}],"preferred":false,"id":848828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamano, Takashi","contributorId":294759,"corporation":false,"usgs":false,"family":"Yamano","given":"Takashi","email":"","affiliations":[{"id":63641,"text":"Asian Development Bank (ADB)","active":true,"usgs":false}],"preferred":false,"id":848829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mohammad, Ismail","contributorId":294760,"corporation":false,"usgs":false,"family":"Mohammad","given":"Ismail","email":"","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236982,"text":"70236982 - 2022 - Can we accurately estimate sediment budgets on Mars?","interactions":[],"lastModifiedDate":"2022-09-26T22:08:52.426374","indexId":"70236982","displayToPublicDate":"2022-07-12T17:03:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Can we accurately estimate sediment budgets on Mars?","docAbstract":"Sediment budgets are fundamentally important for planetary science. However, only one primary method, based on remote sensing, is currently available for determining extraterrestrial sediment budgets. For determining sediment budgets on Earth, both in-situ and remote sensing methods are available. Despite the widespread use of the two methods, there has been surprisingly little research on how well the sediment budgets produced by these two approaches reconcile with one another, which highlights the lack of quantitative understanding of errors for sediment budgets measured with remote sensing in planetary research. Therefore, there is a general need to expand our knowledge of sediment budgets. Here we use a background review and analog case study of an aeolian dunefield in Grand Canyon, Earth to frame a path forward for addressing shortcomings of remote sensing sediment budgets on Mars. We estimate a 53% percent difference in the sediment budget determined with remote sensing relative to in-situ methods for a simple endmember scenario of a dunefield within a unimodal wind directional regime and no external sediment supply. However, when we incorporated key sources of uncertainty in remote sensing change detection following methods commonly used by geomorphologists on Earth, the estimates of sediment budget differences relative to the in-situ method spanned a much larger range, from 3% to 138%. Our case study also suggests that sediment budget errors could be much larger under more complex wind direction, sediment supply, and physiographic settings, and that variability in those landscape characteristics might be used to better estimate errors for dunefield sediment budgets. We conclude that by comparing sediment budgets derived from in-situ measurements of sediment fluxes and from remote sensing measurements at many more analog sites on Earth, the aeolian research community, and the geomorphology discipline, could gain an understanding of the errors of the remote sensing method, which is used by investigators on other planetary bodies such as Mars. This could improve the ability to quantify sediment budgets on Mars – and, in the future, other planetary environments where high-resolution topographic data are available – as well as directly improve our ability to interpret extraterrestrial landscape evolution related to climate, weather, and geologic history.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2022.117682","usgsCitation":"Sankey, J., Kasprak, A., Chojnacki, M., Titus, T.N., Caster, J., and DeBenedetto, G., 2022, Can we accurately estimate sediment budgets on Mars?: Earth and Planetary Science Letters, v. 593, 117682, 11 p., https://doi.org/10.1016/j.epsl.2022.117682.","productDescription":"117682, 11 p.","ipdsId":"IP-137953","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447133,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2022.117682","text":"Publisher Index Page"},{"id":435775,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971IOAI","text":"USGS data release","linkHelpText":"Sediment budget data for Lees Ferry dune field, February-May 2019"},{"id":407376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"593","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":852909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chojnacki, Matthew 0000-0001-8497-8994","orcid":"https://orcid.org/0000-0001-8497-8994","contributorId":296931,"corporation":false,"usgs":false,"family":"Chojnacki","given":"Matthew","email":"","affiliations":[{"id":64240,"text":"Planetary Science Institute, Lakewood, CO, USA","active":true,"usgs":false}],"preferred":false,"id":852910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":852911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeBenedetto, Geoffrey 0000-0003-0696-4567 gdebened@usgs.gov","orcid":"https://orcid.org/0000-0003-0696-4567","contributorId":220988,"corporation":false,"usgs":true,"family":"DeBenedetto","given":"Geoffrey","email":"gdebened@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852913,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232445,"text":"ofr20211125 - 2022 - Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19","interactions":[],"lastModifiedDate":"2026-03-25T17:54:53.444918","indexId":"ofr20211125","displayToPublicDate":"2022-07-12T12:35:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1125","displayTitle":"Characterization of the Bathymetry, Hydrodynamics, Water Quality, Infrastructure, and Channel Condition of the Old Erie Canal from DeWitt to its Junction with the Current Erie Canal in Verona, near Rome, New York, 2018–19","title":"Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19","docAbstract":"The Old Erie Canal has undergone sedimentation and aquatic growth that have restricted flow and diminished the aesthetic quality of the canal during the nearly 200 years since its construction. During 2018–2019, the U.S. Geological Survey (USGS) in cooperation with the Madison County Planning Department and the New York State Canal Corporation conducted a study of the Old Erie Canal between the Town of DeWitt, New York, and its junction with the current Erie Canal of the New York State Canal System near Rome, N.Y. The study comprised bathymetric, velocity, and water-quality surveys and documentation of the canal infrastructure. The USGS established benchmarks and staff gages along the 30.8 miles of the canal study area to reference the water-surface level in the canal to the North American Vertical Datum of 1988 (NAVD 88). Bathymetric survey results indicated that during the time of the survey, the canal depths ranged from 1.26 feet (ft) to 7.33 ft between the Butternut and Durhamville aqueducts (with a mean depth of 3.52 ft). Shallow depths are located throughout the canal, but the section north of the Durhamville aqueduct was the shallowest, with depths ranging from 0.68 ft to 2.44 ft (and a mean depth of 1.36 ft). The reach-averaged water velocity was 0.28 feet per second. The system generally flows west to east from the Butternut aqueduct to the entrance to the Erie Canal.
Water-quality data (dissolved oxygen, water temperature, specific conductance, pH, and turbidity) were collected concurrently with the bathymetric survey (spring 2018) to characterize changes in water quality along the length of the canal. Specific-conductance values measured upstream from the hamlet of Kirkville, Manlius, N.Y. may reflect road salts being flushed into the canal through the Butternut and Limestone feeder system (designed to divert water from nearby creeks to supply water for the Old Erie Canal) from recent stormwater runoff. Increases in pH in the downstream direction are possibly caused by increasing amounts of aquatic vegetation. During the time of the survey, turbidity was highest near inflows from the canal feeder system and tributary inputs which were elevated by stormwater runoff that transported sediment into the canal.
The canal infrastructure was documented to provide a baseline assessment. The feeder system, designed to bring water into the canal, does not deliver flow when the creeks supplying water to those feeders are at base flow, but does bring water into the system when flows in the feeder creeks are elevated. A recent report provides an example of repair work completed on the Chittenango feeder to improve flow through the feeder into the canal (Welch and Madison County Planning Department, 1996). Two non-regulated tributaries, Meadow Brook and Pools Brook, consistently delivered flow to the canal. Outfalls where canal water discharges into nearby creeks were sealed in the Butternut and Limestone aqueducts. Outfalls in the Chittenango, Cowaselon, and Durhamville aqueducts were found with flashboards installed at an elevation that allows water to be discharged from the canal. These structures are designed to accept additional flashboards to raise the canal water surface with the potential to convey flow farther down the system. The general condition of the channel was open and navigable between Butternut aqueduct and Chittenago aqueduct. On the segment of the canal east of the Chittenango aqueduct, an increasing number of downed trees and tangled wads of vegetation affected flow and made navigation by boat difficult to the Durhamville aqueduct. North of the Durhamville aqueduct, numerous downed trees and an increased density of aquatic vegetation limited navigation by boat and reduced the flow rate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211125","usgsCitation":"Wernly, J.F., 2022, Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19: U.S. Geological Survey Open-File Report 2021–1125, 75 p., https://doi.org/10.3133/ofr20211125.","productDescription":"Report: viii, 75 p.; Data Release","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118164","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":402850,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.XML"},{"id":402848,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.pdf","text":"Report","size":"94.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1125"},{"id":402847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1125/coverthb.jpg"},{"id":402849,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QRL294","text":"USGS data release","linkHelpText":"Geospatial dataset of the characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to Rome, New York 2018–2019"},{"id":402851,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1125/images/"},{"id":403542,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211125/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1125"},{"id":501536,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113265.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Old Erie Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.13250732421875,\n 42.974511174899156\n ],\n [\n -76.11328125,\n 42.968984647488014\n ],\n [\n -75.9375,\n 42.96044267380142\n ],\n [\n -75.74798583984375,\n 42.96446257387128\n ],\n [\n -75.57769775390625,\n 43.002638523957906\n ],\n [\n -75.41839599609375,\n 43.1270477646888\n ],\n [\n -75.35522460937499,\n 43.207177786666655\n ],\n [\n -75.42388916015625,\n 43.271206115959785\n ],\n [\n -75.52001953125,\n 43.25920592943639\n ],\n [\n -75.65460205078125,\n 43.23920036180898\n ],\n [\n -75.92926025390625,\n 43.219188223481325\n ],\n [\n -76.09405517578125,\n 43.13105676219153\n ],\n [\n -76.18194580078124,\n 43.07891929985966\n ],\n [\n -76.18743896484375,\n 43.022721607058344\n ],\n [\n -76.13250732421875,\n 42.974511174899156\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
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The Kodiak Islands lie near the southern terminus of the 1964 Great Alaska earthquake rupture area and within the Kodiak subduction zone segment. Both local and trans-Pacific tsunamis were generated during this devastating megathrust event, but the local tsunami source region and the causative faults are poorly understood. We provide an updated view of the tsunami and earthquake hazard for the Kodiak Islands region through tsunami modeling and geophysical data analysis. Using seismic and bathymetric data, we characterize a regionally extensive seafloor lineament related to the Kodiak shelf fault zone, with focused uplift along a 50-km-long portion of the newly named Ugak fault as the most likely source of the local Kodiak Islands tsunami in 1964. We present evidence of Holocene motion along the Albatross Banks fault zone, but we suggest that this fault did not produce a tsunami in 1964. We relate major structural boundaries to active forearc splay faults, where tectonic uplift is collocated with gravity lineations. Differences in interseismic locking, seismicity rates, and potential field signatures argue for different stress conditions at depth near presumed segment boundaries. We find that the Kodiak segment boundaries have a clear geophysical expression and are linked to upper-plate structure and splay faulting. The tsunamigenic fault hazard is higher for the Kodiak shelf fault zone when compared to the nearby Albatross Banks fault zone, suggesting short wave travel paths and little tsunami warning time for nearby communities.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02486.1","usgsCitation":"Ramos, M.D., Liberty, L.M., Haeussler, P., and Humphreys, R.J., 2022, Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA: Geosphere, v. 18, no. 5, p. 1474-1491, https://doi.org/10.1130/GES02486.1.","productDescription":"18 p.","startPage":"1474","endPage":"1491","ipdsId":"IP-135286","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02486.1","text":"Publisher Index Page"},{"id":408601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.4037941171657,\n 60.562811262269065\n ],\n [\n -151.83516963619837,\n 61.55980516417185\n ],\n [\n -156.47488362730599,\n 57.79635099382884\n ],\n [\n -154.29366188512998,\n 55.202746146556194\n ],\n [\n -147.4037941171657,\n 60.562811262269065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos, Marlon D. 0000-0003-4449-8624","orcid":"https://orcid.org/0000-0003-4449-8624","contributorId":293255,"corporation":false,"usgs":false,"family":"Ramos","given":"Marlon","email":"","middleInitial":"D.","affiliations":[{"id":63266,"text":"Air Force Research Lab","active":true,"usgs":false}],"preferred":false,"id":855359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liberty, Lee M","contributorId":194078,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":855360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":855361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphreys, Robert John 0000-0002-6733-6399","orcid":"https://orcid.org/0000-0002-6733-6399","contributorId":298308,"corporation":false,"usgs":true,"family":"Humphreys","given":"Robert","email":"","middleInitial":"John","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855362,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233556,"text":"70233556 - 2022 - Revisiting the 1899 earthquake series using integrative geophysical analysis in Yakutat Bay, Alaska","interactions":[],"lastModifiedDate":"2023-11-08T18:01:12.38369","indexId":"70233556","displayToPublicDate":"2022-07-12T07:16:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting the 1899 earthquake series using integrative geophysical analysis in Yakutat Bay, Alaska","docAbstract":"A series of large earthquakes in 1899 affected southeastern Alaska near Yakutat and Disenchantment Bays. The largest of the series, a MW 8.2 event on 10 September 1899, generated an ~12-m-high tsunami and as much as 14.4 m of coseismic uplift in Yakutat Bay, the largest coseismic uplift ever measured. Several complex fault systems in the area are associated with the Yakutat terrane collision with North America and the termination of the Fairweather strike-slip system, but because faults local to Yakutat Bay have been incompletely or poorly mapped, it is unclear which fault system(s) ruptured during the 10 September 1899 event. Using marine geophysical data collected in August 2012, we provide an improved tectonic framework for the Yakutat area, which advances our understanding of earthquake hazards. We combined 153 line km of 2012 high-resolution multichannel seismic (MCS) reflection data with compressed high-intensity radar pulse (Chirp) profiles, basin-scale MCS data, 2018 seafloor bathymetry, published geodetic models and thermochronology data, and previous measurements of coseismic uplift to better constrain fault geometry and subsurface structure in the Yakutat Bay area. We did not observe any active or concealed faults crossing Yakutat Bay in our high-resolution data, requiring faults to be located entirely onshore or nearshore. We interpreted onshore faults east of Yakutat Bay to be associated with the transpressional termination of the Fairweather fault system, forming a series of splay faults that exhibit a horsetail geometry. Thrust and reverse faults on the west side of the bay are related to Yakutat terrane underthrusting and collision with North America. Our results include an updated fault map, structural model of Yakutat Bay, and quantitative assessment of uncertainties for legacy geologic coseismic uplift measurements. Additionally, our results indicate the 10 September 1899 rupture was possibly related to stress loading from the earlier Yakutat terrane underthrusting event of 4 September 1899, with the majority of 10 September coseismic slip occurring on the Esker Creek system on the northwest side of Yakutat Bay. Limited (~2 m) coseismic or postseismic slip associated with the 1899 events occurred on faults located east of Yakutat Bay.
Advances in multi-species monitoring have prompted an increase in the use of multi-species occupancy analyses to assess patterns of co-occurrence among species, even when data were collected at scales likely violating the assumption that sites were closed to changes in the occupancy state for the target species. Violating the closure assumption may lead to erroneous conclusions related to patterns of co-occurrence among species. Occurrence for two hypothetical species was simulated under patterns of avoidance, aggregation, or independence, when the closure assumption was either met or not. Simulated populations were sampled at two levels (N = 250 or 100 sites) and two scales of temporal resolution for surveys. Sample data were analyzed with conditional two-species occupancy models, and performance was assessed based on the proportion of simulations recovering the true pattern of co-occurrence. Estimates of occupancy were unbiased when closure was met, but biased when closure violations occurred; bias increased when sample size was small and encounter histories were collapsed to a large-scale temporal resolution. When closure was met and patterns of avoidance and aggregation were simulated, conditional two-species models tended to correctly find support for non-independence, and estimated species interaction factors (SIF) aligned with predicted values. By contrast, when closure was violated, models tended to incorrectly infer a pattern of independence and power to detect simulated patterns of avoidance or aggregation that decreased with smaller sample size. Results suggest that when the closure assumption is violated, co-occurrence models often fail to detect underlying patterns of avoidance or aggregation, and incorrectly identify a pattern of independence among species, which could have negative consequences for our understanding of species interactions and conservation efforts. Thus, when closure is violated, inferred patterns of independence from multi-species occupancy should be interpreted cautiously, and evidence of avoidance or aggregation is likely a conservative estimate of true pattern or interaction.
Contaminants of emerging concern (CECs) are a loosely defined group of chemicals whose wide-spread usage or presence in the environment has occurred more recently or for which there has been relatively little research done until recently. Many of these CECs are not currently regulated. The National Toxicology Program within the U.S. Department of Health and Human Services estimates that about 2000 CECs are introduced each year (https://ntp.niehs.nih.gov/about/). An unknown number may pose a risk to human or animal health. The Phase 1 (2010 – 2014) CEC work in birds, which is the subject of this report, assessed exposure across the Great Lakes to polybrominated diphenyl ethers (PBDEs), perfluorinated compounds (PFASs), and polycyclic aromatic hydrocarbons (PAHs), and put those exposures into context with data from biologically relevant endpoints such as reproductive success, as well as, physiological response indicators (bioindicators) to assess possible effects. The group of chemicals included in Phase 1 were mainly those chemicals that bioaccumulate in tissues. Phase 2 (2015 – 2019) CEC work with tree swallows was expanded to include CECs whose occurrence in the environment is more temporary or seasonal, and that do not necessarily bioaccumulate. These are often called pseudo-persistent, because, while they are not long-lived in the environment, there are often daily inputs via waste water treatment plants, and run-off from farm fields and storm drainages, thereby making them available to biota year-round. These include pharmaceuticals, personal care products, and newer pesticides including herbicides. Tree swallow work on these less persistent CECs will be reported in the future, however see other Appendices in this report for information on some of these types of CECs (Appendices A, B, D).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Contaminants of emerging concern in the Great Lakes: Science to inform management practices for protecting the health and integrity of wildlife populations from adverse effects","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Environmental Protection Agency","doi":"10.23645/epacomptox.21044455","collaboration":"U.S. Environmental Protection Agency (US EPA); Great Lakes Restoration Initiative (GLRI);","usgsCitation":"Custer, C.M., Custer, T.W., and Dummer, P.M., 2022, Exposure and effects of bioaccumulative contaminants of emerging concern in tree swallows nesting across the Laurentian Great Lakes, 16 p., https://doi.org/10.23645/epacomptox.21044455.","productDescription":"16 p.","startPage":"77","endPage":"92","ipdsId":"IP-099857","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences 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}\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Custer, Christine M. 0000-0003-0500-1582 ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":852706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Custer, Thomas W. 0000-0003-3170-6519 tcuster@usgs.gov","orcid":"https://orcid.org/0000-0003-3170-6519","contributorId":2835,"corporation":false,"usgs":true,"family":"Custer","given":"Thomas","email":"tcuster@usgs.gov","middleInitial":"W.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":852707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dummer, Paul M. 0000-0002-2055-9480 pdummer@usgs.gov","orcid":"https://orcid.org/0000-0002-2055-9480","contributorId":3015,"corporation":false,"usgs":true,"family":"Dummer","given":"Paul","email":"pdummer@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":852708,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236998,"text":"70236998 - 2022 - Contaminants of emerging concern in the Great Lakes: Science to inform management practices for protecting the health and integrity of wildlife populations from adverse effects: GLRI action plan I, focus area 1, goal 5","interactions":[],"lastModifiedDate":"2022-09-27T13:55:45.724543","indexId":"70236998","displayToPublicDate":"2022-07-09T08:30:32","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":12606,"text":"Group Progress Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"EPA/600/R-22/057","title":"Contaminants of emerging concern in the Great Lakes: Science to inform management practices for protecting the health and integrity of wildlife populations from adverse effects: GLRI action plan I, focus area 1, goal 5","docAbstract":"Executive Summary: Under Action Plan I (2010-2014) of the Great Lakes Restoration Initiative (GLRI), Federal and Academic partners began an investigation of the presence and distribution of contaminants of emerging concern (CECs) in the Great Lakes and potential impacts on fish and wildlife. The term CECs is applied to a broad range of chemicals that are currently in use but for which we currently lack good understanding of whether fish, wildlife, or humans are being exposed and/or whether negative health or environmental effects are expected if exposure occurs. Pharmaceuticals, personal care products, flame retardants, many current use pesticides, and poly- and perfluorinated chemicals are some well-known groups of CECs, but there is no definitive or comprehensive list that can be used to support the management of CECs to reduce impacts on the Great Lakes ecosystem.
Four overarching goals were identified for this collaborative investigation:
1. Evaluate the sources, occurrence, and distribution of CECs across the Great Lakes Basin.
2. Examine associations between the distribution of CECs and land-use patterns.
3. Review both scientific literature and field-generated data to determine the potential for CECs to cause adverse effects on Great Lakes fish and wildlife populations.
4. Develop efficient strategies to survey and/or monitor for threats that CECs may pose in order to take effective management actions before those threats evolve into large scale impacts on Great Lakes ecosystems or the services they provide.
Achievement of these goals ensures progress towards Focus Area 1: Toxic Substances and Areas of Concern from GLRI Action Plan I, Goal 5: “The health and integrity of wildlife populations and habitat are protected from adverse chemical and biological effects associated with the presence of toxic substances in the Great Lakes Basin”.
This large-scale research effort was comprised of individual and collaborative projects from multiple federal agencies and academic institutions, involving over 85 investigators, and overseen by the U.S. Environmental Protection Agency (EPA) Region 5, Great Lakes National Program Office. Partners include the United States Geological Survey, the National Oceanic and Atmospheric Administration, U.S. Fish and Wildlife Service, Saint Cloud State University, the U.S. EPA Office of Research and Development, and the U.S. Army Corps of Engineers.
Key findings:
1. Contaminants of emerging concern were found throughout the monitored Great Lakes tributaries, but types and concentrations vary in association with regional land use. CECs were detected in nearly all samples collected. The type and concentration of the specific contaminants detected varied considerably among field sites and in association with land use type, such as urban, agricultural, wetland, 2 or forest. Contaminants were detected in the water column, sediment, and tissues of all species surveyed in the current work (mussels, aquatic insects, fish, and insect-eating birds).
2. There were over 20 contaminants for which CEC concentrations approached or exceeded those reported to cause toxicity in laboratory experiments. This was based on detection in water, sediments and or biota at one or more field sites. These contaminants represent compounds that warrant further investigation and monitoring with respect to potential impacts in certain areas of the Great Lakes basin. Based on the present investigation, compounds of greatest concern include: polycyclic aromatic hydrocarbons, associated with oil-based products and combustion of organic matter; atrazine, an herbicide; dichlorvos, an insecticide; and ibuprofen and venlafaxine, both pharmaceuticals.
3. Results suggest that mixtures of CECs presently found in most Great Lakes tributary locations surveyed may elicit subtle biological effects, but likely are not, alone, causing obvious detriment to current communities of fish and wildlife. CECs detected in the Great Lakes were associated with subtle biological effects like changes in gene expression, altered circulating glucose, etc. in both wild-caught and laboratory-reared organisms. These effects were generally not indicative of reproductive failure or mortality. However, the effects may have more serious implications when combined with other sources of stress like habitat degradation, changing climate conditions, and competition with invasive species. Due to limited historical data, it is unknown whether severe CEC-related impacts have already affected aquatic communities in waterbodies that have received long-term inputs of these contaminants. Likewise, under Action Plan I, biological effects were not necessarily evaluated at the sites where CEC concentrations exceeding laboratory toxicity thresholds were detected. As a result, strategic, ongoing surveillance and monitoring of CECs is warranted.
This collaborative investigation resulted in new tools, approaches, and data that can be used to inform and support the management of CECs to reduce their impacts on Great Lakes natural resources. The following products of this research effort are available through https://communities.geoplatform.gov/glri/ or by contacting the investigators (see technical chapters found in Appendices A-F):
1. Database of CEC occurrence and concentrations in US tributary streams. The database includes CEC detections in water, sediment, and fish and wildlife tissues, and represents the most comprehensive survey of CECs in the Great Lakes Region.
2. Synopses of results and key findings. Integrated summaries of results, conclusions, and management implications of the CEC research are available through reports, topical fact sheets, and presentations.
3. Technical publications: This collaborative research effort has resulted in over 50 peer-reviewed publications, agency reports, and data releases that can be of use to resource managers, the scientific community, and members of the public.
4. Innovative tools. Innovative monitoring devices, sampling equipment, conceptual frameworks, and software applications were developed over the course of this 3 research. These tools are transferable to stakeholders via internet accessibility or via specifications, instructions, and demonstration detailed in technical publications.
Hypotheses to guide CECs research under Action Plan II. Findings from 2010-2014 were used to guide further research in 2015-2018 for basin-wide surveillance of CECs and for sites warranting further study of potential biological impacts of CECs. Additional surveillance included both evaluation of additional classes of contaminants and expanded lists for chemical classes shown to be of greatest concern. Mixtures of some of the most frequently detected contaminants were also tested in laboratory studies to understand whether long term exposures to multiple contaminants may result in effects not evident from uncontrolled, short-term field experiments.
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L.","contributorId":141084,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":853000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Custer, Christine M. 0000-0003-0500-1582 ccuster@usgs.gov","orcid":"https://orcid.org/0000-0003-0500-1582","contributorId":1143,"corporation":false,"usgs":true,"family":"Custer","given":"Christine","email":"ccuster@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, W. Edward","contributorId":296967,"corporation":false,"usgs":false,"family":"Johnson","given":"W.","email":"","middleInitial":"Edward","affiliations":[],"preferred":false,"id":853002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hummel, Stephanie L.","contributorId":296241,"corporation":false,"usgs":false,"family":"Hummel","given":"Stephanie","email":"","middleInitial":"L.","affiliations":[{"id":16956,"text":"US Fish & Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853003,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":853004,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perkins, Edward J.","contributorId":89063,"corporation":false,"usgs":false,"family":"Perkins","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":853005,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zack, Sarah A.","contributorId":296968,"corporation":false,"usgs":false,"family":"Zack","given":"Sarah","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":853006,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232693,"text":"70232693 - 2022 - Paralytic shellfish toxins associated with Arctic Tern mortalities in Alaska","interactions":[],"lastModifiedDate":"2023-01-09T18:53:34.856543","indexId":"70232693","displayToPublicDate":"2022-07-09T06:43:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Paralytic shellfish toxins associated with Arctic Tern mortalities in Alaska","docAbstract":"Harmful algal blooms produce biotoxins that can injure or kill fish, wildlife, and humans. These blooms occur naturally but have intensified in many locations globally due to recent climatic changes, including ocean warming. Such changes are especially pronounced in northern regions, where the effects of paralytic shellfish toxins (PSTs) on marine wildlife are of growing concern. In Alaska, seabird mortality events have increased in frequency, magnitude, and duration since 2015 alongside anomalously high ocean temperatures. Although starvation has been implicated as the apparent cause of death in many of these die-offs, saxitoxin (STX) and other PSTs have been identified as possible contributing factors. Here, we describe a mortality event at a nesting colony of Arctic Terns (Sterna paradisaea) near Juneau, Alaska in 2019 and report elevated concentrations of PSTs in bird, forage fish, and mussel samples. Concentrations of STX and other PSTs in tern tissues (2.5–51.2 µg 100g−1 STX-equivalents [STX-eq]) were of similar magnitude to those reported from other PST-induced bird die-offs. We documented high PST concentrations in blue mussels (>11,000 µg 100g−1 STX-eq; Mytilus edulis spp.) collected from nearby beaches, as well as in forage fish (up to 494 µg 100g−1 STX-eq) retrieved from Arctic Tern nests, thereby providing direct evidence of PST exposure via the terns’ prey. At maximum concentrations measured in this study, a single 5 g Pacific Sand Lance (Ammodytes personatus) could exceed the median lethal STX dose (LD50) currently estimated for birds, offering strong support for PSTs as a likely source of tern mortality. In addition to describing this localized bird mortality event, we used existing energetics data from adult and nestling Arctic Terns to calculate estimated cumulative daily PST exposure based on ecologically relevant concentrations in forage fish. Our estimates revealed potentially lethal levels of PST exposure even at relatively low (≤30 ug 100g−1 STX-eq) toxin concentrations in prey. These findings suggest that PSTs present a significant hazard to Arctic Terns and other northern seabirds and should be included in future investigations of avian mortality events as well as assessments of population health.
From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).
","language":"English","publisher":"Copernicus","doi":"10.5194/essd-14-3115-2022","usgsCitation":"McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S., Peters-Lidard, C., and Verdin, J., 2022, A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan: Earth System Science Data, v. 14, no. 7, p. 3115-3135, https://doi.org/10.5194/essd-14-3115-2022.","productDescription":"21 p.","startPage":"3115","endPage":"3135","ipdsId":"IP-134002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-3115-2022","text":"Publisher Index Page"},{"id":429405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Afghanistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[61.21082,35.65007],[62.23065,35.27066],[62.98466,35.40404],[63.19354,35.85717],[63.9829,36.00796],[64.54648,36.31207],[64.74611,37.11182],[65.58895,37.30522],[65.74563,37.66116],[66.21738,37.39379],[66.51861,37.36278],[67.07578,37.35614],[67.83,37.14499],[68.13556,37.02312],[68.85945,37.34434],[69.19627,37.15114],[69.51879,37.609],[70.11658,37.58822],[70.27057,37.73516],[70.3763,38.1384],[70.80682,38.48628],[71.34813,38.25891],[71.2394,37.95327],[71.54192,37.90577],[71.44869,37.06564],[71.84464,36.73817],[72.19304,36.94829],[72.63689,37.04756],[73.26006,37.49526],[73.9487,37.42157],[74.98,37.41999],[75.15803,37.13303],[74.57589,37.02084],[74.06755,36.83618],[72.92002,36.72001],[71.84629,36.50994],[71.26235,36.07439],[71.49877,35.65056],[71.61308,35.1532],[71.11502,34.73313],[71.15677,34.34891],[70.8818,33.98886],[69.93054,34.02012],[70.32359,33.35853],[69.68715,33.1055],[69.26252,32.50194],[69.31776,31.90141],[68.92668,31.62019],[68.55693,31.71331],[67.79269,31.58293],[67.68339,31.30315],[66.93889,31.30491],[66.38146,30.7389],[66.34647,29.88794],[65.04686,29.47218],[64.35042,29.56003],[64.148,29.34082],[63.55026,29.46833],[62.54986,29.31857],[60.87425,29.82924],[61.78122,30.73585],[61.69931,31.37951],[60.94194,31.54807],[60.86365,32.18292],[60.53608,32.98127],[60.9637,33.52883],[60.52843,33.67645],[60.80319,34.4041],[61.21082,35.65007]]]},\"properties\":{\"name\":\"Afghanistan\"}}]}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"McNally, 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Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sarmiento, Daniel","contributorId":337031,"corporation":false,"usgs":false,"family":"Sarmiento","given":"Daniel","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoell, Andrew","contributorId":337032,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":901826,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pervez, Shahriar 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mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901829,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kumar, Sujay","contributorId":337039,"corporation":false,"usgs":false,"family":"Kumar","given":"Sujay","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901830,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Peters-Lidard, Christa","contributorId":337041,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901831,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verdin, 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crocodiles (Crocodylus acutus) in Florida has been both positively and negatively influenced by anthropogenic and natural factors since the species was placed on the federally endangered species list in 1975. This includes a shift in nesting sites and an expansion of nesting to anthropogenic habitat. Using a 50-year record of monitoring data (1970-2020), we assessed factors influencing nesting ecology (number of nests, nest morphology, success rate, and habitat use) from a total of 3,013 nests recorded across South Florida. We detected a change in nesting success rate, increasing from 61% in the 1970’s to near 90% since 2010. Our hot spot analysis illustrates that nesting sites in northeastern Florida Bay and Flamingo/Cape Sable (Everglades National Park) were important for American crocodiles. Anthropogenic habitats, such as canals provided vital habitat nesting in areas such as Flamingo/Cape Sable (Everglades National Park), Turkey Point Power Plant, and Crocodile Lake National Wildlife Refuge for the current Florida population. Environmental parameters suspected to affect nesting success have shown an increasing trend over the past 50 years and minimum temperature and rainfall, during the summer season, are correlated with increased nesting success and temporal variation across South Florida. The adaptive capacity that American crocodiles exhibited in Florida gave the species advantages to face changes in climate and landscape over the last 50 years, however, it does not imply that the adaptive capacity of the species to face these changes (evolutionary potential) cannot reach a limit if changes continue. Here, we document C. acutus nesting ecology population responses to ecosystem restoration efforts in Florida; and further demonstrate the value of protecting and restoring habitat to support recovery of listed species.
This report describes the background information related to and the contents of the Lee Acres-Giant Bloomfield Refinery Database (LAGBRD), which is a compilation of monitoring data collected at the Lee Acres Landfill and the Giant Bloomfield Refinery near Farmington, New Mexico. LAGBRD includes monitoring data from as early as 1985, when awareness was increasing regarding contamination from liquid waste lagoons at the landfill and fuel releases at the refinery. Water quality and groundwater elevation data from sampling locations at the landfill and the refinery are included in the database. LAGBRD was compiled by the U.S. Geological Survey in cooperation with the Bureau of Land Management, which operates the Lee Acres Landfill, in order to facilitate future studies into the characteristics of groundwater contamination and background geochemistry at the landfill and refinery sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1154","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Gray, E.L., and Ferguson, C.L., 2022, Database of water quality and groundwater elevation within and surrounding the Lee Acres Landfill, New Mexico, 1985–2020: U.S. Geological Survey Data Report 1154, 80 p., https://doi.org/10.3133/dr1154.","productDescription":"Report: xi, 80 p.; Database","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-127569","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501205,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113258.htm","linkFileType":{"id":5,"text":"html"}},{"id":402827,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1154/dr1154_database.zip","size":"14.4 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Lee Acres-Giant Bloomfield Refinery Database (LAGBRD)"},{"id":402825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1154/dr1154.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1154"},{"id":402824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1154/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Lee Acres Landfill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.06564331054688,\n 36.683288049295015\n ],\n [\n -108.00590515136717,\n 36.683288049295015\n ],\n [\n -108.00590515136717,\n 36.72072349483175\n ],\n [\n -108.06564331054688,\n 36.72072349483175\n ],\n [\n -108.06564331054688,\n 36.683288049295015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Anthropogenic activities, including groundwater withdrawals, return flow from irrigated agriculture, and treated wastewater-effluent disposal have the potential to affect groundwater quality in the Lucerne Valley groundwater basin, located in the southwest Mojave Desert. Questions regarding the current state and potential future of groundwater quality in this basin were addressed by (1) considering groundwater data from and findings of historical water-quality studies, (2) evaluating recent (1990–2021) U.S. Geological Survey water-quality and geochemical-tracer data, and (3) assessing groundwater-quality results from samples collected in 2021 to better understand the transport of applied treated wastewater effluent in the subsurface and associated effects of this practice on water quality. As observed by previous studies, differences in groundwater quality existed among the upper, middle, and lower aquifers of the Lucerne Valley groundwater basin, with the lower aquifer characterized by high dissolved-solid content relative to the middle and upper aquifers. Stable and radioisotope tracers indicate that most of the groundwater sampled in the basin was recharged during cooler, wetter climate conditions than those of the present day (2022). Analyses of the 2021 samples collected to examine the subsurface transport of applied treated wastewater effluent were not conclusive but indicate that water from applied treated wastewater effluent is currently (2022) limited to the upper aquifer and likely to remain so given the extensive confining unit below the upper aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221063","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Fackrell, J.K., 2022, Groundwater quality of the Lucerne Valley groundwater basin, California: U.S. Geological Survey Open-File Report 2022-1063, 19 p., https://doi.org/10.3133/ofr20221063.","productDescription":"viii, 19 p.","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-137528","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501818,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113259.htm","linkFileType":{"id":5,"text":"html"}},{"id":403158,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1063"},{"id":403163,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225048","text":"Scientific Investigations Report 2022-5048","description":"Stamos, C.L., Larsen, J.D., Powell, R.E., Matti, J.C., and Martin, P., 2022, Hydrogeology and simulation of groundwater flow in the Lucerne Valley groundwater basin, California: U.S. Geological Survey Scientific Investigations Report 2022-5048, 120 p., https://doi.org/10.3133/sir20225048.","linkHelpText":"- Hydrogeology and Simulation of Groundwater Flow in the Lucerne Valley Groundwater Basin, California"},{"id":403159,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.xml"},{"id":403160,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1063/images"},{"id":403157,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1063/covrthb.jpg"},{"id":403185,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221063/full","text":"Report","description":"Open-File Report 2022-1063"}],"country":"United States","state":"California","otherGeospatial":"Lucerne Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.666667,\n 34.266667\n ],\n [\n -117.083333,\n 34.266667\n ],\n [\n -117.083333,\n 34.666667\n ],\n [\n -116.666667,\n 34.666667\n ],\n [\n -116.666667,\n 34.266667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale.
We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion.
Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%).
Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00299-x","usgsCitation":"Weller, F.G., Beatty, W.S., Webb, E.B., Kesler, D.C., Krementz, D.G., Asante, K., and Naylor, L.W., 2022, Environmental drivers of autumn migration departure decisions in midcontinental mallards: Movement Ecology, v. 10, 1, 13 p., https://doi.org/10.1186/s40462-021-00299-x.","productDescription":"1, 13 p.","ipdsId":"IP-132818","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447212,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00299-x","text":"Publisher Index Page"},{"id":432679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341013,"corporation":false,"usgs":false,"family":"Weller","given":"Florian","email":"","middleInitial":"G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kesler, Dylan C.","contributorId":216845,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","email":"","middleInitial":"C.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":908795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908796,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Asante, Kwasi","contributorId":59632,"corporation":false,"usgs":true,"family":"Asante","given":"Kwasi","email":"","affiliations":[],"preferred":false,"id":908797,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Naylor, Luke W.","contributorId":145840,"corporation":false,"usgs":false,"family":"Naylor","given":"Luke","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":908798,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232502,"text":"fs20223045 - 2022 - Connecticut and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:04:51.64174","indexId":"fs20223045","displayToPublicDate":"2022-07-05T13:52:57","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3045","displayTitle":"Connecticut and Landsat","title":"Connecticut and Landsat","docAbstract":"Connecticut, the third-smallest State by land area, is the fourth most densely populated in the United States. Connecticut’s tightly packed cities serve as international hubs for the finance and insurance industries. These same urban enclaves host highly regarded institutions of higher learning, such as Yale, the University of Connecticut, and Quinnipiac and Wesleyan Universities.
Yet Connecticut has surprisingly varied natural features. Its residents are never far from pristine environments that teem with wildlife and convey the sounds, scents, and scenery of the natural world.
The Connecticut River, the longest in New England at 407 miles, cuts through the State’s center and drains into Long Island Sound, as do the Thames and Housatonic Rivers. Together, these rivers form an estuary spawning ground for hundreds of freshwater and saltwater aquatic species. North and east of the coastal lowlands that hug the estuary are the highlands of the Southern Taconic Range and the Berkshires. North and west of the coastline, smaller rivers and valleys weave through densely forested landscapes dotted with State parks and scenic lakes.
Imagery from the U.S. Geological Survey Landsat archive, a trusted public resource with more than 50 years of worldwide Earth observations, provides a powerful tool for analyzing the interplay of human habitation and natural landscapes in Connecticut. Landsat data also offer land managers a critical information source for managing damage from invasive species and monitoring the local landscape effects of a changing global climate.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.
State wildlife agencies have long struggled to identify optimal hunting season dates for migratory game bird species that meet the diverse and often competing interests of stakeholders. Many approaches have been used to ensure the regulated community participates in the decision-making process, including public hearings, hunter season-date preference surveys, and hunter task forces or committees. Although hearings, surveys, and task force approaches include portions of the regulated community (typically the most avid) they may not necessarily reflect the opinions and values of all stakeholders. Additionally, these approaches rely heavily on limited anecdotal observations that may be unduly influenced by hunter avidity (e.g., days spent afield), hunter density, species preferences, and property access. To address the challenges caused by engaging only portions of the stakeholder community, we used a structured decision-making framework that included a 2017 duck hunter survey to elucidate values of a representative sample of the regulated community in each waterfowl hunting zone in the state of New York, USA. Rather than asking duck hunters about their specific duck hunting season date preferences, we asked them to rank 6 objectives describing what they value in their hunting experience (e.g., maximizing the opportunity to see mallards [Anas platyrhynchos] and black ducks [Anas rubripes], maximizing the number of weekend days). Four of the 6 objectives described duck species availability (i.e., abundance or immigration) and the remaining 2 described considerations that affect an individual hunter's opportunity or limitation to going duck hunting (i.e., holidays or conflicting hunting seasons). We used spatiotemporal abundance models derived from eBird citizen science data to estimate abundance and immigration rates of ducks in each waterfowl zone. We evaluated up to 9 unique season date alternatives developed by duck hunter task forces to determine which season date alternative best satisfied the competing objectives of duck hunters in each zone. The approach we developed allowed for selection of optimal duck hunting season dates and successfully involved avid duck hunters in the regulation development stages, while ensuring that the values of a representative sample of all stakeholders were directly considered through a clear and transparent decision-making process.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1313","usgsCitation":"Stiller, J., Siemer, W., Perkins, K., and Fuller, A.K., 2022, Choosing an optimal duck season: Integrating hunter values and duck abundance: The Journal of Wildlife Management, v. 46, no. 3, e1313, 21 p., https://doi.org/10.1002/wsb.1313.","productDescription":"e1313, 21 p.","ipdsId":"IP-130711","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480827,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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This is the case of ciscoes (Coregonus spp.) in the Laurentian Great Lakes, which are characterized by a complex evolutionary history and are commonly described as having undergone an adaptive radiation. In this study, morphometrics, stable isotopes and transcriptome sequencing were used to study the relationships within the Coregonus artedi complex in western Lake Superior. We observed general concordance for morphological, ecological and genomic variation, but the last was more taxonomically informative as it showed less overlap among species in multivariate space. Low levels of genetic differentiation were observed between individuals morphologically identified as Coregonus hoyi and C. zenithicus, which could be evidence of incomplete lineage sorting or recent hybridization between the two groups. Transcriptome-based single nucleotide polymorphisms exhibited significant divergence for genes associated with vision, development, metabolism and immunity among species that occupy different habitats. This study highlights the importance of using an integrative approach when studying groups of taxa with a complex evolutionary history, as individual-level analyses of multiple independent data sets can provide a clearer picture of the patterns and processes associated with the origins of biodiversity.
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While immediate effects of ponding on the distribution of carbon within the 1-m soil profile were apparent, there were no effects of ponding on the overall, integrated, carbon stocks 14 years, following wetland submergence. Rather, the model predicts that soil carbon losses in the first meter will be realized over long periods of time (∼200 years) due to changes in the source of carbon (biomass vs. mineral sediment) with minimal losses through mineralization.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG006807","usgsCitation":"Schoolmaster, D.R., Stagg, C., Creamer, C., Laurenzano, C., Ward, E., Waldrop, M., Baustian, M., Aw, T., Merino, S., Villani, R.K., and Scott, L., 2022, A model of the spatiotemporal dynamics of soil carbon following coastal wetland loss applied to a Louisiana salt marsh in the Mississippi River Deltaic Plain: Journal of Geophysical Research: Biogeosciences, v. 127, no. 6, e2022JG006807, 15 p.; Data Release, https://doi.org/10.1029/2022JG006807.","productDescription":"e2022JG006807, 15 p.; Data Release","ipdsId":"IP-133880","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":402921,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417836,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P916JH3L"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.0050048828125,\n 29.807284450222504\n ],\n [\n -91.263427734375,\n 29.176145182559758\n ],\n [\n -90.274658203125,\n 29.036960648558267\n ],\n [\n -89.17053222656249,\n 28.91682310329166\n ],\n [\n -89.000244140625,\n 29.10897615145302\n ],\n [\n -89.3023681640625,\n 29.869228848968312\n ],\n [\n -90.439453125,\n 30.130875412002318\n ],\n [\n -90.889892578125,\n 30.424992973925598\n ],\n [\n -91.351318359375,\n 31.01057105944174\n ],\n [\n -91.9171142578125,\n 30.99173704508671\n ],\n [\n -92.0050048828125,\n 29.807284450222504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolmaster, Donald R. 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