Radio collars have been used to examine the spatial ecology of all North American ungulates, but are rarely used on feral horses due to concerns that they may cause injury. Due to public concerns for animal welfare, an alternative to radio collars for tracking feral horses, particularly stallions, over the short term would be useful. We developed a method of attaching a global positioning system (GPS) transmitter to feral horse tails, and provide step by step instructions so that others may apply this method. We braided the tail and affixed a transmitter tag to the braid with epoxy, cable ties, and an attachment cord run through the braid. Between 2016 and 2017 we fitted 114 VHF or VHF-GPS tags in the tails of free-roaming feral horses in western Utah. From when tags were fitted to September 2020 tag retention time ranged from <1 to 36 months (n = 111, mean = 8.50 ± SD 6.39 months). We found that our braided, tail-mounted transmitter tags can provide a viable alternative to radio collars for meeting shorter-term data collection needs once data transmission difficulties are overcome.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1338","usgsCitation":"King, S.R., and Schoenecker, K., 2022, Application of tail transmitters for tracking feral horses as an alternative to radio collars: Wildlife Society Bulletin, v. 46, no. 4, e1338, 9 p., https://doi.org/10.1002/wsb.1338.","productDescription":"e1338, 9 p.","ipdsId":"IP-129913","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Conger Herd Management Area, Frisco Herd Management Area, Sulphur Springs Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.03358980597041,\n 41.99245468276973\n ],\n [\n -114.05551299770433,\n 37.00574853871275\n ],\n [\n -113.3320476704814,\n 37.01450169249067\n ],\n [\n -112.29069606311519,\n 38.28160056526153\n ],\n [\n -112.04954095404076,\n 39.914801662668765\n ],\n [\n -114.03358980597041,\n 41.99245468276973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"King, Sarah R. B. 0000-0002-9316-7488","orcid":"https://orcid.org/0000-0002-9316-7488","contributorId":280063,"corporation":false,"usgs":false,"family":"King","given":"Sarah","email":"","middleInitial":"R. B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":881741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881742,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70235785,"text":"70235785 - 2022 - Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for U.S. NSHM 2023","interactions":[],"lastModifiedDate":"2022-08-19T13:37:36.641728","indexId":"70235785","displayToPublicDate":"2022-08-18T07:19:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for U.S. NSHM 2023","docAbstract":"As part of the U.S. National Seismic Hazard Model (NSHM) update planned for 2023, two databases were prepared to more completely represent Quaternary-active faulting across the western United States: the NSHM23 fault sections database (FSD) and earthquake geology database (EQGeoDB). In prior iterations of NSHM, fault sections were included only if a field-measurement-derived slip rate was estimated along a given fault. By expanding this inclusion criteria, we were able to assess a larger set of faults for use in NSHM23. The USGS Quaternary Fault and Fold Database served as a guide for assessing possible additions to the NSHM23 FSD. Reevaluating available data from published sources yielded an increase of fault sections from ~650 faults in NSHM18 to ~1,000 faults proposed for use in NSHM23. EQGeoDB, a companion dataset linked to NSHM23 FSD, contains geologic slip rate estimates for fault sections included in FSD. Together, these databases serve as common input data used in deformation modeling, earthquake rupture forecasting, and additional downstream uses in NSHM development.
Open-source intelligence (OSINT) evolved in spy agencies but now is rapidly changing many fields of study, from anthropology to zoology. Despite the fact that OSINT occasionally is used in conservation biology, there is little recognition that some tools and frameworks used by conservation professionals are drawn from this well-established field. The history and conceptual foundations of OSINT stem from the intelligence community, although OSINT tools are rapidly being applied in other fields. In conservation biology, OSINT is sometimes used to evaluate wildlife crime, human-wildlife and human-environment interactions, animal behavior, and questions of distribution and abundance. Recognizing the conceptual foundations of the field would allow expansion of conservation biology, not only in the areas noted above, but also, for example, in study of habitat use, habitat change, and animal behavior. This recognition would also provide frameworks for conceptual advancement, especially in terms of data and privacy management. Failure to recognize the underpinnings of OSINT tools in conservation biology harms the field because it limits how research is framed, thought about, and implemented. Likewise, taking an OSINT perspective to conservation problems, rather than simply thinking in terms of big data, can enrich the field, expand science, and increase knowledge and understanding of biology and biodiversity.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13988","usgsCitation":"Katzner, T., Thomason, E.C., Huhmann, K., Conkling, T., Concepcion, C.B., Slabe, V., and Poessel, S.A., 2022, Open-source intelligence for conservation biology: Conservation Biology, v. 36, no. 6, e13988, 9 p., https://doi.org/10.1111/cobi.13988.","productDescription":"e13988, 9 p.","ipdsId":"IP-141281","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":405986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":850414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomason, Eve C. 0000-0002-9141-9397","orcid":"https://orcid.org/0000-0002-9141-9397","contributorId":245270,"corporation":false,"usgs":false,"family":"Thomason","given":"Eve","email":"","middleInitial":"C.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":850415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huhmann, Karrin","contributorId":296027,"corporation":false,"usgs":false,"family":"Huhmann","given":"Karrin","email":"","affiliations":[{"id":63970,"text":"Conservation Science Global","active":true,"usgs":false}],"preferred":false,"id":850416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conkling, Tara 0000-0003-1926-8106","orcid":"https://orcid.org/0000-0003-1926-8106","contributorId":217915,"corporation":false,"usgs":true,"family":"Conkling","given":"Tara","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":850417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Concepcion, Camille B.","contributorId":190164,"corporation":false,"usgs":false,"family":"Concepcion","given":"Camille","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":850418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slabe, Vincent","contributorId":205309,"corporation":false,"usgs":false,"family":"Slabe","given":"Vincent","affiliations":[{"id":37080,"text":"West Virginia University, Division of Forestry and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":850419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":850420,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235724,"text":"sir20225043 - 2022 - Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","interactions":[],"lastModifiedDate":"2026-04-09T17:35:58.64895","indexId":"sir20225043","displayToPublicDate":"2022-08-17T19:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5043","displayTitle":"Water-Quality Conditions and Constituent Loads, Water Years 2013–19, and Water-Quality Trends, Water Years 1983–2019, in the Scituate Reservoir Drainage Area, Rhode Island","title":"Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","docAbstract":"The Scituate Reservoir is the primary source of drinking water for more than 60 percent of the population of Rhode Island. From October 1, 1982, to September 30, 2019, water years (WYs) 1983–2019 (a water year is the period between October 1 and September 30 and is designated by the year in which it ends), the Providence Water Supply Board maintained a fixed-frequency sampling program at 37 stations to monitor water quality in tributaries to the Scituate Reservoir. The U.S. Geological Survey (USGS), in cooperation with the Providence Water Supply Board, has measured streamflow at selected streamgages in the Scituate Reservoir drainage area since WY 1994, monitored water quality at selected stations since WY 2009, and conducted targeted base-flow and stormflow sampling at five stations in WYs 2016–19. Daily loads and yields of constituents (chloride, nitrite, nitrate, total coliform bacteria, Escherichia coli, and orthophosphate) were determined for sampled days during WYs 2013–19, and trends were examined for the entire period of record, predominantly WYs 1983–2019. USGS water-quality data were used to determine annual loads and yields of chloride and sodium for WYs 2013–19 at 14 stations, and nutrients and suspended sediment for WYs 2016–19 at 5 stations.
Tributaries in the Scituate Reservoir drainage area for WYs 2013–19 were slightly acidic (pH values less than 7.0 standard units) and often below the recommended pH range of 6.5 to 8.5 standard units, as described by the U.S. Environmental Protection Agency (EPA) in the secondary drinking-water regulations. Most measurements of water color in the tributaries were greater than the EPA secondary drinking-water regulation of 15 platinum-cobalt units. Chloride concentrations in Providence Water Supply Board samples rarely exceeded the EPA secondary drinking-water regulation for chloride (250 milligrams per liter); however, chloride concentrations estimated from continuous measurements of specific conductance exceeded the EPA criterion continuous concentration recommended for freshwater (230 milligrams per liter) for short periods ranging from 10 minutes to 26 hours at two streamgages.
Positive trends in pH, color, alkalinity, and chloride at more than half of the monitoring stations were identified for WYs 1983–2019. Fewer than half of the stations had significant trends in turbidity values, and significant trends varied in direction (positive or negative trends). Trend tests were not performed on total coliform bacteria, Escherichia coli, and nitrate concentrations because of analytical method changes that coincide with abrupt shifts in the magnitude and distribution of concentration data.
The median of daily loads and yields of chloride, nitrite, nitrate, orthophosphate, and bacteria determined for each Providence Water Supply Board sample in WYs 2013–19 varied across the 37 monitoring stations, but yields were generally greater at stations in the Moswansicut and Regulating Reservoir subbasins. Average daily yields of chloride and sodium estimated from continuous records of specific-conductance and streamflow data at 14 stations ranged from 42 to 310 kilograms per square mile per day and 28 to 180 kilograms per square mile per day, respectively. The mean annual yields of total phosphorus, total nitrogen, and suspended sediment determined for five stations ranged from 16 to 78 kilograms per square mile, from 370 to 2,100 kilograms per square mile, and from 5,000 to 13,000 kilograms per square mile, respectively. More than half of the nutrient and suspended sediment loads occurred during stormflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225043","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Spaetzel, A.B., and Smith, K.P., 2022, Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2022–5043, 102 p., https://doi.org/10.3133/sir20225043.","productDescription":"Report: xiv, 102 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128796","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":405192,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5043/images/"},{"id":405190,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98XCK0R","text":"USGS data release","linkHelpText":"Water-quality, streamflow, and quality-control data supporting estimation of nutrient and sediment loads in the Scituate Reservoir drainage area, Rhode Island, water years 2016–19"},{"id":502398,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113397.htm","linkFileType":{"id":5,"text":"html"}},{"id":405191,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.XML"},{"id":405188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5043"},{"id":405187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5043/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir drainage area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.79840087890625,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.724180549563606\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Regulatory entities, such as the Minnesota Department of Health, monitor public water systems for conformance with Federal and State monitoring requirements and water-quality standards. Although some contaminants have Federal and (or) State regulations and guidance values, many contaminants, such as pesticides and pharmaceuticals, are unregulated in that only non-enforceable health-based guidance values have been assigned to them. Furthermore, because these contaminants are not regulated, commonly only limited resources are available to public water systems or regulatory entities to monitor them in drinking water. Focused screening efforts on contaminants that are frequently detected in the environment can provide information to help monitoring entities prioritize their sampling efforts.
Here we assess the use of enzyme-linked immunosorbent assay (ELISA) method, a rapid, inexpensive screening method, as an alternative to more expensive methods to analyze source and finished drinking-water samples collected from public water systems throughout Minnesota for three commonly detected pesticides (atrazine, imidacloprid, and pyrethroids) and three commonly detected pharmaceuticals (caffeine, carbamazepine, and sulfamethoxazole). The ELISA results were compared to results provided by more advanced mass-spectrometry analytical methods at the U.S. Geological Survey National Water Quality Laboratory (NWQL) and SGS AXYS Analytical Services Ltd. (AXYS).
Overall, these datasets are highly censored (>80 percent) and contain multiple reporting limits within and between laboratories. To discern agreement between paired contaminant group results (target contaminant plus immunologically similar contaminants) by ELISA and the advanced analytical methods at NWQL and AXYS, presence-absence agreement analysis was coupled with false negative and false positive analysis. Analysis of presence-absence agreement shows that ELISA has generally good agreement (77.9 to 100 percent) with both NWQL and AXYS for all unregulated contaminant groups. Imidicloprid, pyrethroids, and caffeine contaminant groups have relatively low false positivity rates (16, 6, and 5 percent, respectively) when analyzed by ELISA, which indicates the ELISA method, for these contaminant groups, could be experiencing low-level interference attributed to the detection of immunologically similar contaminants. Similarly, sulfamethoxazole has a low false positivity rate (0.8 percent), which indicates ELISA is likely not overestimating results for this contaminant group. Analyses for carbamazepine and sulfamethoxazole by ELISA resulted in low false negativity rates (1.6 and 0.8 percent, respectively), which indicates the ELISA method is likely not underestimating the results for this contaminant group. Conversely, the atrazine contaminant group has a high false negativity rate (84 percent), which indicates the method has a strong negative bias and that ELISA underestimates results for this contaminant. These qualitative results indicate that the ELISA method could potentially serve as a reliable and cost-effective screening method to help drinking water monitoring entities prioritize sampling efforts for analyzing carbamazepine and sulfamethoxazole in source and finished drinking-water samples collected from public water systems. At the same time, although ELISA did not prove to be a good screening method for atrazine, evaluation of ELISA results indicated that its use for screening imidacloprid, pyrethroids, and caffeine could be beneficial for water testing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225066","collaboration":"Prepared in cooperation with the Minnesota Department of Health","usgsCitation":"Krall, A.L., Elliott, S.M., de Lambert, J.R., and Robertson, S.W., 2022, Comparison of the results of enzyme-linked immunosorbent assay (ELISA) to mass-spectrometry based analytical methods for six unregulated contaminants in source water and finished drinking-water samples: U.S. Geological Survey Scientific Investigations Report 2022–5066, 29 p., https://doi.org/10.3133/sir20225066.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-129231","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":405279,"rank":6,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Delineating conservation units (CUs, e.g., evolutionarily significant units, ESUs, and management units, MUs) is critical to the recovery of declining species because CUs inform both listing status and management actions. Genomic data have strengths and limitations in informing CU delineation and related management questions in natural systems. We illustrate the value of using genomic data in combination with landscape, dispersal, and occupancy data, to inform CU delineation in Nevada populations of the Great Basin Distinct Population Segment of the Columbia spotted frog (Rana luteiventris). R. luteiventris occupies naturally fragmented aquatic habitats in this xeric region, but beaver removal, climate change, and other factors have put many of these populations at high risk of extirpation without management intervention. We addressed three objectives: (1) assessing support for ESUs within Nevada; (2) evaluating and revising, if warranted, the current delineation of MUs; and (3) evaluating genetic diversity, effective population size, adaptive differentiation, and functional connectivity to inform ongoing management actions. We found little support for ESUs within Nevada but did identify potential revisions to MUs based on unique landscape drivers of connectivity that distinguish these desert populations from those in the northern portion of the species range. Effective sizes were uniformly small, with low genetic diversity and weak signatures of adaptive differentiation. Our findings suggest that management actions, including translocations and genetic rescue, might be warranted. Our study illustrates how a carefully planned genetic study, designed to address priority management goals that include CU delineation, can provide multiple insights to inform conservation action.
","language":"English","publisher":"Wiley","doi":"10.1111/mec.16660","usgsCitation":"Forester, B.R., Murphy, M., Mellison, C., Petersen, J., Pilliod, D., Van Horne, R., Harvey, J., and Funk, W.C., 2022, Genomics-informed delineation of conservation units in a desert amphibian: Molecular Ecology, v. 31, no. 20, p. 5249-5269, https://doi.org/10.1111/mec.16660.","productDescription":"21 p.","startPage":"5249","endPage":"5269","ipdsId":"IP-133553","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mec.16660","text":"Publisher Index Page"},{"id":405579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"20","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Forester, Brenna R.","contributorId":261215,"corporation":false,"usgs":false,"family":"Forester","given":"Brenna","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":849678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Melanie","contributorId":88239,"corporation":false,"usgs":true,"family":"Murphy","given":"Melanie","affiliations":[],"preferred":false,"id":849679,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mellison, Chad","contributorId":28873,"corporation":false,"usgs":true,"family":"Mellison","given":"Chad","affiliations":[],"preferred":false,"id":849680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petersen, Jeffrey","contributorId":295567,"corporation":false,"usgs":false,"family":"Petersen","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":849681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":849633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Horne, Rachel","contributorId":216072,"corporation":false,"usgs":false,"family":"Van Horne","given":"Rachel","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":849682,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harvey, Jim","contributorId":203502,"corporation":false,"usgs":false,"family":"Harvey","given":"Jim","email":"","affiliations":[],"preferred":false,"id":849683,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":97589,"corporation":false,"usgs":false,"family":"Funk","given":"W.","email":"","middleInitial":"Chris","affiliations":[{"id":6998,"text":"Department of Biology, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":849684,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236648,"text":"70236648 - 2022 - Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","interactions":[],"lastModifiedDate":"2023-01-19T19:24:00.787879","indexId":"70236648","displayToPublicDate":"2022-08-17T09:52:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","docAbstract":"Roadway deicing agents, including rock salt and brine containing NaCl, have had a profound impact on the water quality and aquatic health of rivers and streams in urbanized areas with temperate climates. Yet, few studies evaluate impacts to watersheds characterized by relatively low impervious surface cover (ISC; < 15 %). Here, we use long-term (1997-2019), monthly streamwater quality data combined with daily streamflow for six exurban and suburban watersheds in southeastern Pennsylvania to examine the relations among chloride (Cl−) concentrations and ISC. Both flow-normalized Cl− concentrations and ISC increased over time in each of the six watersheds, consistent with changes in watershed management (e.g., ISC, road salt application, etc.). The watersheds that experienced the greatest changes in percent ISC (e.g., agriculture replaced by residential and commercial development) experienced the greatest changes in flow-normalized Cl− concentrations. We also utilized a comprehensive mass-balance model (2011–2018) that indicated Cl− inputs exceeded the outputs for the study watersheds. Road salt applied to state roads, non-state roads, and other impervious surfaces accounted for the majority of Cl− inputs to the six watersheds. Furthermore, increasing Cl− concentrations during baseflow conditions confirm impacts to shallow groundwater. Although flow-normalized Cl− concentrations are below the U.S. Environmental Protection Agency's chronic threshold value for impacts to aquatic organisms, year-round exceedances may result before the end of this century based on current trends. Though reduced Cl− loading to streams may be achieved by limiting the expansion of impervious surfaces in exurban and suburban watersheds, changes in baseflow concentrations are likely to be gradual because of the accumulated Cl− in groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157933","usgsCitation":"Rossi, M., Kremer, P., Cravotta, C., Scheirer, K.E., and Goldsmith, S.T., 2022, Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds: Science of the Total Environment, v. 851, no. Part 2, 157933, 13 p., https://doi.org/10.1016/j.scitotenv.2022.157933.","productDescription":"157933, 13 p.","ipdsId":"IP-139821","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":446757,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157933","text":"Publisher Index Page"},{"id":406679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Berks County, Bucks County, Chester County, Delaware County, Lehigh County, Montgomery County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 40.2\n ],\n [\n -75.6,\n 40.2\n ],\n [\n -75.6,\n 39.812755695478124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"851","issue":"Part 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Marissa L. 0000-0003-2341-0312","orcid":"https://orcid.org/0000-0003-2341-0312","contributorId":296518,"corporation":false,"usgs":false,"family":"Rossi","given":"Marissa L.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kremer, Peleg","contributorId":296521,"corporation":false,"usgs":false,"family":"Kremer","given":"Peleg","email":"","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheirer, Krista E.","contributorId":296524,"corporation":false,"usgs":false,"family":"Scheirer","given":"Krista","email":"","middleInitial":"E.","affiliations":[{"id":64093,"text":"Aqua Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":851698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldsmith, Steven T.","contributorId":193458,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":851699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235725,"text":"sim3493 - 2022 - Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","interactions":[],"lastModifiedDate":"2026-04-01T15:26:29.678467","indexId":"sim3493","displayToPublicDate":"2022-08-16T12:19:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3493","displayTitle":"Colored Shaded-Relief Bathymetric Map and Surrounding Aerial Imagery of Whiskeytown Lake, California","title":"Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","docAbstract":"The Carr wildfire began on July 23, 2018, and burned almost 300,000 acres (approximately half on Federal lands) in northern California during the subsequent 6-week period. Over 97 percent of the area within Whiskeytown National Recreation Area, California, burned during the 2018 Carr wildfire, including the entire landscape that surrounds and drains into Whiskeytown Lake. Shortly after the Carr wildfire ended, the U.S. Geological Survey began investigations into the landscape responses, such as changes in erosion and sediment deposition, that occurred after the fire. This study focused on the collection and processing of bathymetric data and onshore aerial imagery in and around Whiskeytown Lake, California, to support wildfire science after the fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3493","usgsCitation":"Dartnell, P., Logan, J.B., and East, A.E., 2022, Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California: U.S. Geological Survey Scientific Investigations Map 3493, scale 1:8,900, https://doi.org/10.3133/sim3493.","productDescription":"1 Sheet: 35.00 x 35.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132510","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501934,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113395.htm","linkFileType":{"id":5,"text":"html"}},{"id":405195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HEDYNT","text":"Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021)","description":"Logan, J.B., Dartnell, P., East, A.E., and Ritchie, A.C., 2020, Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9HEDYNT."},{"id":405194,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3493/sim3493.pdf","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3493"},{"id":405193,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3493/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Whiskeytown Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63214111328125,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.59257812608644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Stable or growing populations may go extinct when their sizes cannot withstand large swings in temporal variation and stochastic forces. Hence, the minimum abundance threshold defining when populations can persist without human intervention forms a key conservation parameter. We identify this threshold for many populations of Caprinae, typically threatened species lacking demographic data. Doing so helps triage conservation and management actions for threatened or harvested populations. Methodologically, we used population projection matrices and simulations, with starting abundance, recruitment, and adult female survival predicting future abundance, growth rate (λ), and population trend. We incorporated mean demographic rates representative of Caprinae populations and corresponding variances from desert bighorn sheep (Ovis canadensis nelsoni), as a proxy for Caprinae sharing similar life histories. We found a population’s minimum abundance resulting in ≤ 0.01 chance of quasi-extinction (QE; population ≤ 5 adult females) in 10 years and ≤ 0.10 QE in 30 years as 50 adult females, or 70 were translocation (removals) pursued. Discovering the threshold required 3 demographic parameters. We show, however, that monitoring populations’ relationships to this threshold requires only abundance and recruitment data. This applied approach avoids the logistical and cost hurdles in measuring female survival, making assays of population persistence more practical.
The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14163937","usgsCitation":"Gonzalez Perez, A., Abd-Elrahman, A., Wilkinson, B., Johnson, D.J., and Carthy, R., 2022, Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets: Remote Sensing, v. 14, no. 16, 3937, 41 p., https://doi.org/10.3390/rs14163937.","productDescription":"3937, 41 p.","ipdsId":"IP-141836","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14163937","text":"Publisher Index Page"},{"id":433203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Wolf Branch Creek Coastal Nature Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.43286683042422,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.759840853994277\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez Perez, Ali","contributorId":341416,"corporation":false,"usgs":false,"family":"Gonzalez Perez","given":"Ali","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abd-Elrahman, Amr","contributorId":341417,"corporation":false,"usgs":false,"family":"Abd-Elrahman","given":"Amr","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkinson, Benjamin","contributorId":239953,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Daniel J.","contributorId":197828,"corporation":false,"usgs":false,"family":"Johnson","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908384,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238501,"text":"70238501 - 2022 - Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","interactions":[],"lastModifiedDate":"2022-11-28T12:26:40.467915","indexId":"70238501","displayToPublicDate":"2022-08-13T06:21:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","docAbstract":"Identifying how natural (i.e., unaltered by human activity) and anthropogenic landscape variables influence contemporary functional connectivity in terrestrial organisms can elucidate the genetic consequences of environmental change. We examine population genetic structure and functional connectivity among populations of a declining species, the Blainville's horned lizard (Phrynosoma blainvillii), in the urbanized landscape of the Greater Los Angeles Area in Southern California, USA. Using single nucleotide polymorphism data, we assessed genetic structure among populations occurring at the interface of two abutting evolutionary lineages, and at a fine scale among habitat fragments within the heavily urbanized area. Based on the ecology of P. blainvillii, we predicted which environmental variables influence population structure and gene flow and used gravity models to distinguish among hypotheses to best explain population connectivity. Our results show evidence of admixture between two evolutionary lineages and strong population genetic structure across small habitat fragments. We also show that topography, microclimate, and soil and vegetation types are important predictors of functional connectivity, and that anthropogenic disturbance, including recent fire history and urban development, are key factors impacting contemporary population dynamics. Examining how natural and anthropogenic sources of landscape variation affect contemporary population genetics is critical to understanding how to best manage sensitive species in a rapidly changing landscape.
Habitat loss due to increasing anthropogenic disturbance is the major driver for bird population declines across the globe. Within the Eastern Ghats of India, shrubland bird communities are threatened by shrinking of suitable habitats due to increased anthropogenic disturbance and climate change. The development of an effective habitat management strategy is hampered by the absence of data for this bird community. To address this knowledge gap, we examined foraging sites for 14 shrubland bird species, including three declining species, in three study areas representing the shrubland type of forest community in the Eastern Ghats. We recorded microhabitat features within an 11 m radius of observed foraging points and compared these data with similar data from random plots. We used chi-square to test the association between plant species and bird species for sites where they were observed foraging. We observed significant differences between foraging sites of all the study species and random plots, thus indicating selection for foraging habitat. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were shrub density, vegetational height, vertical foliage stratification, grass height, and percent rock cover. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species (e.g., ground-foraging species differed significantly from other species). Except for two species, all focal birds were associated with at least one plant species. The plant-bird association was based on foraging, structural, or behavioral preferences. Several key factors affecting foraging habitat such as shrub density can be actively managed at the local scale. Strategic and selective harvesting of forest products and a spatially and temporally controlled livestock grazing regime may allow regeneration of scrubland and create conditions favorable to birds.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9192","usgsCitation":"Deshwall, A., Stephenson, S., Panwar, P., DeGregorio, B.A., Kannan, R., and Willson, J., 2022, Foraging habitat selection of shrubland bird community in tropical dry forest: Ecology and Evolution, v. 12, no. 8, e9192, 12 p., https://doi.org/10.1002/ece3.9192.","productDescription":"e9192, 12 p.","ipdsId":"IP-119426","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":486945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9192","text":"Publisher Index Page"},{"id":433371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Andhra Pradesh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 79.37736382663809,\n 13.491137039228363\n ],\n [\n 78.55592471748383,\n 13.448724063058705\n ],\n [\n 78.57234419763398,\n 13.03288769532432\n ],\n [\n 79.44277297167974,\n 13.149403444594242\n ],\n [\n 79.37736382663809,\n 13.491137039228363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Deshwall, A.","contributorId":341560,"corporation":false,"usgs":false,"family":"Deshwall","given":"A.","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":908618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, S.L.","contributorId":341562,"corporation":false,"usgs":false,"family":"Stephenson","given":"S.L.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panwar, P.","contributorId":341564,"corporation":false,"usgs":false,"family":"Panwar","given":"P.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kannan, R.","contributorId":341561,"corporation":false,"usgs":false,"family":"Kannan","given":"R.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willson, J.D.","contributorId":341563,"corporation":false,"usgs":false,"family":"Willson","given":"J.D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908621,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236729,"text":"70236729 - 2022 - Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","interactions":[],"lastModifiedDate":"2022-09-16T12:22:53.172839","indexId":"70236729","displayToPublicDate":"2022-08-12T07:16:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7514,"text":"Journal of Geophysical Research - Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","docAbstract":"Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.
Latest climate models project conditions for the end of this century that are generally outside of the human experience. These future conditions affect the resilience and sustainability of ecosystems, alter biogeographic zones, and impact biodiversity. Deep-time records of paleoclimate provide insight into the climate system over millions of years and provide examples of conditions very different from the present day, and in some cases similar to model projections for the future. In addition, the deep-time paleoecologic and sedimentologic archives provide insight into how species and habitats responded to past climate conditions. Thus, paleoclimatology provides essential context for the scientific understanding of climate change needed to inform resource management policy decisions. The Pliocene Epoch (5.3–2.6 Ma) is the most recent deep-time interval with relevance to future global warming. Analysis of marine sediments using a combination of paleoecology, biomarkers, and geochemistry indicates a global mean annual temperature for the Late Pliocene (3.6–2.6 Ma) ∼3°C warmer than the preindustrial. However, the inability of state-of-the-art climate models to capture some key regional features of Pliocene warming implies future projections using these same models may not span the full range of plausible future climate conditions. We use the Late Pliocene as one example of a deep-time interval relevant to management of biodiversity and ecosystems in a changing world. Pliocene reconstructed sea surface temperatures are used to drive a marine ecosystem model for the North Atlantic Ocean. Given that boundary conditions for the Late Pliocene are roughly analogous to present day, driving the marine ecosystem model with Late Pliocene paleoenvironmental conditions allows policymakers to consider a future ocean state and associated fisheries impacts independent of climate models, informed directly by paleoclimate information.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.972179","usgsCitation":"Dowsett, H.J., Jacobs, P., and de Mutsert, K., 2022, Using paleoecological data to inform decision making: A deep-time perspective: Frontiers in Ecology and Evolution, v. 10, 972179, 8 p., https://doi.org/10.3389/fevo.2022.972179.","productDescription":"972179, 8 p.","ipdsId":"IP-141870","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":446807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.972179","text":"Publisher Index Page"},{"id":407619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":269579,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":848146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobs, Peter","contributorId":248861,"corporation":false,"usgs":false,"family":"Jacobs","given":"Peter","email":"","affiliations":[],"preferred":false,"id":848147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Mutsert, Kim","contributorId":194503,"corporation":false,"usgs":false,"family":"de Mutsert","given":"Kim","email":"","affiliations":[],"preferred":false,"id":853377,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254115,"text":"70254115 - 2022 - Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context","interactions":[],"lastModifiedDate":"2024-05-08T11:53:41.104313","indexId":"70254115","displayToPublicDate":"2022-08-11T06:49:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context","docAbstract":"Power line corridors have been repeatedly assessed as habitat for wild bees; however, few studies have examined them as bee habitat relative to nearby crop fields and surrounding landscape context.
We surveyed bee communities in power line corridors near to and isolated from lowbush blueberry fields in two landscape contexts in Maine, U.S.A. We examined the influences of blooming plant abundance and diversity and bee life-history traits including sociality, nesting preference, and body size.
We surveyed wild bees and blooming plants in power line corridors from 2013 to 2015. We calculated landscape composition surrounding sites at multiple scales and gathered bee trait information from the literature. We assessed differences in bee communities owing to landscape context with generalized linear models.
We collected 125 wild bee species and observed a rare plant-pollinator relationship within power line corridors. We found greater bee abundance and species richness throughout a complex, resource-rich landscape, while mass-flowering lowbush blueberry fields enhanced bee species richness only in a simple, resource-poor landscape. Landscape composition and blooming plant diversity varied with landscape context, though only landscape composition influenced bee communities. Solitary and ground-nesting species were more sensitive to landscape context than social or cavity-nesting species.
Power line corridors provide crucial refugia for crop pollinating wild bees in agricultural landscapes with resource-poor natural habitat, while bees may selectively forage in power line corridors within agricultural landscapes containing resource-rich natural habitat. We found high-quality forage within corridors; quantifying nesting resources could clarify corridor use by wild bees.
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\"}}]}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Du Clos, Brianne","contributorId":336548,"corporation":false,"usgs":false,"family":"Du Clos","given":"Brianne","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Francis A.","contributorId":336549,"corporation":false,"usgs":false,"family":"Drummond","given":"Francis","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":900269,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235730,"text":"70235730 - 2022 - Lacunarity as a tool for assessing landscape configuration over time and informing long-term monitoring: An example using seagrass","interactions":[],"lastModifiedDate":"2023-06-08T14:56:37.694542","indexId":"70235730","displayToPublicDate":"2022-08-11T06:42:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Lacunarity as a tool for assessing landscape configuration over time and informing long-term monitoring: An example using seagrass","docAbstract":"Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns.
Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation.
We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014.
Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring.
Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01499-5","usgsCitation":"Enwright, N., Darnell, K.M., and Carter, G.A., 2022, Lacunarity as a tool for assessing landscape configuration over time and informing long-term monitoring: An example using seagrass: Landscape Ecology, v. 37, p. 2689-2705, https://doi.org/10.1007/s10980-022-01499-5.","productDescription":"17 p.; Data Release","startPage":"2689","endPage":"2705","ipdsId":"IP-138701","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":435734,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QT07CZ","text":"USGS data release","linkHelpText":"Seagrass map, Cat Island and Ship Island, Mississippi, 2014"},{"id":405251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417831,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TO5P3R"}],"country":"United States","state":"Mississippi","otherGeospatial":"Cat Island, Ship Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.15748596191406,\n 30.19439868711761\n ],\n [\n -88.85948181152344,\n 30.19439868711761\n ],\n [\n -88.85948181152344,\n 30.261439550638762\n ],\n [\n -89.15748596191406,\n 30.261439550638762\n ],\n [\n -89.15748596191406,\n 30.19439868711761\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217766,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":849155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darnell, Kelly M.","contributorId":272888,"corporation":false,"usgs":false,"family":"Darnell","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":849156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Greg A. 0000-0001-8033-0090","orcid":"https://orcid.org/0000-0001-8033-0090","contributorId":295311,"corporation":false,"usgs":false,"family":"Carter","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":12460,"text":"The University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":849157,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239771,"text":"70239771 - 2022 - Navigating the space between policy and practice: Toward a typology of collaborators in a federal land management agency","interactions":[],"lastModifiedDate":"2023-01-19T12:41:47.667595","indexId":"70239771","displayToPublicDate":"2022-08-11T06:40:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3405,"text":"Society and Natural Resources","active":true,"publicationSubtype":{"id":10}},"title":"Navigating the space between policy and practice: Toward a typology of collaborators in a federal land management agency","docAbstract":"Navigating the space between policy and on-the-ground natural resource management presents unique challenges. We interviewed 22 U.S. Bureau of Land Management Field Office Managers to understand their perceptions toward, and applications of, collaboration with public and private stakeholders. Interviews were transcribed and open-coded using qualitative data analysis software. Then, each interview was represented visually using the MaxQDA MaxMaps feature. We deductively coded each visual model and created a typology based on a mix of salient traits exhibited by each group. Differences emerged in each group’s approach to teaching and learning; communication style; attitude toward collaboration; attention to relational and substantive outcomes; and the ability to create space within the agency mission to achieve mutually beneficial goals. Findings can help agencies navigate the challenges associated with aligning agency directives with on-the-ground realities in different contexts when collaborators exhibit different traits.
The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.
Mineral commodity supply disruptions have the potential to ripple through and impact the economy in many ways. Industrial vulnerability is a crucial component of mineral commodity criticality tools as it provides guidance on the economic importance of these commodities to regional criticality indices. Using an economic model that links mineral commodity end-use data to input-output tables and a linear optimization routine, reductions in economic output of individual industries and of the overall economy may be calculated. Such a model can also help to identify industries, be they direct or indirect consumers of the mineral commodities in question, that are most vulnerable to specific mineral commodity supply disruptions at different disruption magnitudes. In this assessment, 56 commodities’ end-use data for the year 2012 were paired with the United States’ detail-level Benchmark Input-Output accounts to build an industrial vulnerability model. The model does not evaluate the likelihood of specific supply disruptions but can be used to assess potential industry impacts for a range of scenarios. The model findings indicate that when the supplies of mineral commodities such as mica, lithium, and fluorspar were disrupted, large overall economic decline was paired with a large decline in many industries. On the other hand, gold, lead, and rhenium disruptions resulted in low declines and few disrupted industries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2022.102889","usgsCitation":"Manley, R., Alonso, E., and Nassar, N.T., 2022, A model to assess industry vulnerability to disruptions in mineral commodity supplies: Resources Policy, v. 78, 102889, 10 p., https://doi.org/10.1016/j.resourpol.2022.102889.","productDescription":"102889, 10 p.","ipdsId":"IP-135103","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":446828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2022.102889","text":"Publisher Index Page"},{"id":408479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manley, Ross 0000-0002-3341-4766","orcid":"https://orcid.org/0000-0002-3341-4766","contributorId":223012,"corporation":false,"usgs":true,"family":"Manley","given":"Ross","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234395,"text":"70234395 - 2022 - Assembling a safe and effective toolbox for integrated flea control and plague mitigation: Fipronil experiments with prairie dogs","interactions":[],"lastModifiedDate":"2022-08-10T13:38:33.781642","indexId":"70234395","displayToPublicDate":"2022-08-10T08:21:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assembling a safe and effective toolbox for integrated flea control and plague mitigation: Fipronil experiments with prairie dogs","docAbstract":"Background
Plague, a widely distributed zoonotic disease of mammalian hosts and flea vectors, poses a significant risk to ecosystems throughout much of Earth. Conservation biologists use insecticides for flea control and plague mitigation. Here, we evaluate the use of an insecticide grain bait, laced with 0.005% fipronil (FIP) by weight, with black-tailed prairie dogs (BTPDs, Cynomys ludovicianus). We consider safety measures, flea control, BTPD body condition, BTPD survival, efficacy of plague mitigation, and the speed of FIP grain application vs. infusing BTPD burrows with insecticide dusts. We also explore conservation implications for endangered black-footed ferrets (Mustela nigripes), which are specialized predators of Cynomys.
Principal findings
During 5- and 10-day laboratory trials in Colorado, USA, 2016–2017, FIP grain had no detectable acute toxic effect on 20 BTPDs that readily consumed the grain. During field experiments in South Dakota, USA, 2016–2020, FIP grain suppressed fleas on BTPDs for at least 12 months and up to 24 months in many cases; short-term flea control on a few sites was poor for unknown reasons. In an area of South Dakota where plague circulation appeared low or absent, FIP grain had no detectable effect, positive or negative, on BTPD survival. Experimental results suggest FIP grain may have improved BTPD body condition (mass:foot) and reproduction (juveniles:adults). During a 2019 plague epizootic in Colorado, BTPDs on 238 ha habitat were protected by FIP grain, whereas BTPDs were nearly eliminated on non-treated habitat. Applications of FIP grain were 2–4 times faster than dusting BTPD burrows.
Significance
Deltamethrin dust is the most commonly used insecticide for plague mitigation on Cynomys colonies. Fleas on BTPD colonies exhibit the ability to evolve resistance to deltamethrin after repeated annual treatments. Thus, more tools are needed. Accumulating data show orally-delivered FIP is safe and usually effective for flea control with BTPDs, though potential acute toxic effects cannot be ruled out. With continued study and refinement, FIP might be used in rotation with, or even replace deltamethrin, and serve an important role in Cynomys and black-footed ferret conservation. More broadly, our stepwise approach to research on FIP may function as a template or guide for evaluations of insecticides in the context of wildlife conservation.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0272419","usgsCitation":"Eads, D.A., Livieri, T., Tretten, T., Hughes, J., Kaczor, N., Halsell, E., Grassel, S.M., Dobesh, P., Childers, E., Lucas, D., Noble, L., Vasquez, M., Grady, A.C., and Biggins, D.E., 2022, Assembling a safe and effective toolbox for integrated flea control and plague mitigation: Fipronil experiments with prairie dogs: PLoS ONE, v. 17, no. 8, e0272419, 19 p., https://doi.org/10.1371/journal.pone.0272419.","productDescription":"e0272419, 19 p.","ipdsId":"IP-134213","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446840,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0272419","text":"Publisher Index Page"},{"id":435737,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QTWGP4","text":"USGS data release","linkHelpText":"Data on 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technician","active":true,"usgs":false}],"preferred":false,"id":848790,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Grady, Anna Catherine","contributorId":294735,"corporation":false,"usgs":false,"family":"Grady","given":"Anna","email":"","middleInitial":"Catherine","affiliations":[{"id":63635,"text":"Was USGS seasonal, no affiliation at this time","active":true,"usgs":false}],"preferred":false,"id":848791,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":294736,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848792,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70235712,"text":"70235712 - 2022 - Global dataset of species-specific inland recreational fisheries harvest for consumption","interactions":[],"lastModifiedDate":"2022-08-16T12:10:25.836446","indexId":"70235712","displayToPublicDate":"2022-08-10T07:06:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Global dataset of species-specific inland recreational fisheries harvest for consumption","docAbstract":"Inland recreational fisheries, found in lakes, rivers, and other landlocked waters, are important to livelihoods, nutrition, leisure, and other societal ecosystem services worldwide. Although recreationally-caught fish are frequently harvested and consumed by fishers, their contribution to food and nutrition has not been adequately quantified due to lack of data, poor monitoring, and under-reporting, especially in developing countries. Beyond limited global harvest estimates, few have explored species-specific harvest patterns, although this variability has implications for fisheries management and food security. Given the continued growth of the recreational fishery sector, understanding inland recreational fish harvest and consumption rates represents a critical knowledge gap. Based on a comprehensive literature search and expert knowledge review, we quantified multiple aspects of global inland recreational fisheries for 81 countries spanning ~192 species. For each country, we assembled recreational fishing participation rate and estimated species-specific harvest and consumption rate. This dataset provides a foundation for future assessments, including understanding nutritional and economic contributions of inland recreational fisheries.
Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well-studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines.
Eastern United States.
2001–2015.
Anurans.
We used large-scale citizen science data from the eastern half of the United States, a diverse biogeographic and climatic region, to assess how variation in winter severity influenced occupancy dynamics (i.e. presence or absence of species across sites and years) of 11 spring and summer breeding anuran species.
Most species had increased occupancy in years with greater than average snow cover and warmer than average mean winter temperatures. Surprisingly, climatic conditions in winter affected occupancy dynamics of species with varying life history characteristics, including both spring and summer breeding species, those that overwinter under the soil, and those that overwinter in ponds and stream beds. For two wide-ranging species (Lithobates catesbeianus and Lithobates clamitans), colder winter temperatures reduced occupancy more at northern latitudes, while the association between days of snow cover and latitude was equivocal.
As the climate continues to change, expected reductions in snowpack may reduce occupancy of already declining anuran populations, while milder winters may improve overwinter survival for some species. The contradictory impacts of temperature and snow cover illustrate the importance of considering multi-dimensional impacts of climate change on anuran populations.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13620","usgsCitation":"Weiskopf, S.R., Shiklomanov, A.N., Thompson, L., Wheedleton, S., and Campbell Grant, E.H., 2022, Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States: Diversity and Distributions, v. 28, no. 10, p. 2187-2199, https://doi.org/10.1111/ddi.13620.","productDescription":"13 p.","startPage":"2187","endPage":"2199","ipdsId":"IP-127531","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":446854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13620","text":"Publisher Index Page"},{"id":405526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.28320312499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 25.24469595130604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shiklomanov, Alexey N. 0000-0003-4022-5979","orcid":"https://orcid.org/0000-0003-4022-5979","contributorId":245541,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexey","email":"","middleInitial":"N.","affiliations":[{"id":49218,"text":"Boston University Department of Earth and Environment","active":true,"usgs":false}],"preferred":false,"id":849615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheedleton, Sarah","contributorId":295508,"corporation":false,"usgs":false,"family":"Wheedleton","given":"Sarah","email":"","affiliations":[{"id":63897,"text":"Smithsonian Conservation Commons","active":true,"usgs":false}],"preferred":false,"id":849617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849618,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234273,"text":"fs20223050 - 2022 - U.S. Geological Survey Benchmark Glacier Project","interactions":[],"lastModifiedDate":"2022-09-27T13:34:54.990937","indexId":"fs20223050","displayToPublicDate":"2022-08-08T12:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3050","displayTitle":"U.S. Geological Survey Benchmark Glacier Project","title":"U.S. Geological Survey Benchmark Glacier Project","docAbstract":"The U.S. Geological Survey Benchmark Glacier Project combines decades of direct glaciological data with remote sensing data to advance the quantitative understanding of glacier-climate interactions. The global loss of glaciers, and consequent implications for water resources, sea level rise, and ecosystem function underscores the importance of U.S. Geological Survey glaciology research to facilitate adaptive strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223050","usgsCitation":"Florentine, C., and McKeon, L.A., 2022, U.S. Geological Survey Benchmark Glacier Project: U.S. Geological Survey Fact Sheet 2022-3050, 2 p., https://doi.org/10.3133/fs20223050.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-135923","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":404997,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223050/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3050"},{"id":404895,"rank":5,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/programs/climate-research-and-development-program/science/glaciers-and-climate-project#data","text":"Glaciers and Climate Project"},{"id":404894,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3050/images"},{"id":404893,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3050/fs20223050.xml"},{"id":404892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3050/fs20223050.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3050"},{"id":404891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3050/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.54150390625,\n 48.16608541901253\n ],\n [\n -120.12451171875,\n 48.16608541901253\n ],\n [\n -120.12451171875,\n 49.095452162534826\n ],\n [\n -122.54150390625,\n 49.095452162534826\n ],\n [\n -122.54150390625,\n 48.16608541901253\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.89501953124999,\n 47.945786463687185\n ],\n [\n -113.13720703125,\n 47.945786463687185\n ],\n [\n -113.13720703125,\n 48.980216985374994\n ],\n [\n -114.89501953124999,\n 48.980216985374994\n ],\n [\n -114.89501953124999,\n 47.945786463687185\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -134.5166015625,\n 58.286395482881034\n ],\n [\n -132.0556640625,\n 58.286395482881034\n ],\n [\n -132.0556640625,\n 59.64554025144323\n ],\n [\n -134.5166015625,\n 59.64554025144323\n ],\n [\n -134.5166015625,\n 58.286395482881034\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.35888671875,\n 60.4788788301667\n ],\n [\n -147.65625,\n 60.4788788301667\n ],\n [\n -147.65625,\n 62.01121819833755\n ],\n [\n -150.35888671875,\n 62.01121819833755\n ],\n [\n -150.35888671875,\n 60.4788788301667\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.66796875,\n 63.03503931552975\n ],\n [\n -141.15234374999997,\n 63.03503931552975\n ],\n [\n -141.15234374999997,\n 64.47279382008166\n ],\n [\n -144.66796875,\n 64.47279382008166\n ],\n [\n -144.66796875,\n 63.03503931552975\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Early detection of dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.
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However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.