Wildlife tracking studies have become ubiquitous in ecology and now provide previously unobtainable data regarding individual movement, vital rates, and population demographics. However, tracking devices can potentially reduce survival of study subjects, generating biases in the vital rates they seek to measure. Previous studies have found that greater sage-grouse (Centrocercus urophasianus) fitted with Global Positioning System (GPS) tracking devices may experience reduced survival, relative to those tracked with traditional radio transmitters, and have documented skin abrasions and lacerations associated with typical backpack-style GPS harnesses. We implemented an experimental study comparing survival and harness retention between 2 different backpack-style GPS transmitter harnesses. We captured female sage-grouse at 3 study sites in the northwest Great Basin of Oregon, Nevada, and California during 2019–2021. We fit each individual, following previously published recommendations, with either a standard backpack harness or a modified harness hypothesized to reduce skin abrasion and laceration. We used known-fate models in Program MARK to model variation in survival and harness retention separately as a function of harness type, year, age, a linear effect of time, and the ratio of the device to individual body mass. Neither survival nor retention varied systematically by harness type, however retention decreased as a function of body mass ratio. We echo previous recommendations for standardized harness attachment protocols and studies designed to isolate and test potential mechanisms by which tracking devices and attachment methods might affect survival and well-being of sage-grouse and other tracked species.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1523","usgsCitation":"Lundblad, C.G., Anthony, C.R., Dungannon, T., Haab, K.A., Schuyler, E., Sink, C.E., Dugger, K., and Hagen, C., 2024, Evaluating satellite-transmitter backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin: The Wildlife Society Bulletin, v. 48, no. 2, e1523, 15 p., https://doi.org/10.1002/wsb.1523.","productDescription":"e1523, 15 p.","ipdsId":"IP-154308","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439479,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1523","text":"Publisher Index Page"},{"id":430129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California Nevada, Oregon","otherGeospatial":"Great 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University","active":true,"usgs":false}],"preferred":false,"id":903643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haab, Kimberly A.","contributorId":338863,"corporation":false,"usgs":false,"family":"Haab","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schuyler, Elizabeth M.","contributorId":338867,"corporation":false,"usgs":false,"family":"Schuyler","given":"Elizabeth M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sink, Chelsea E.","contributorId":338870,"corporation":false,"usgs":false,"family":"Sink","given":"Chelsea","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagen, Christian A.","contributorId":338874,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254278,"text":"fs20243015 - 2024 - Assessment of undiscovered oil and gas resources of the North Chukchi Basin, outer continental shelf of the Chukchi and East Siberian Seas, Arctic Ocean, 2023","interactions":[],"lastModifiedDate":"2026-01-27T17:55:31.16331","indexId":"fs20243015","displayToPublicDate":"2024-05-29T09:52:00","publicationYear":"2024","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":"2024-3015","displayTitle":"Assessment of Undiscovered Oil and Gas Resources of the North Chukchi Basin, Outer Continental Shelf of the Chukchi and East Siberian Seas, Arctic Ocean, 2023","title":"Assessment of undiscovered oil and gas resources of the North Chukchi Basin, outer continental shelf of the Chukchi and East Siberian Seas, Arctic Ocean, 2023","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated mean volumes of 1.8 billion barrels of oil and 119.9 trillion cubic feet of gas technically recoverable from undiscovered, conventional accumulations in Cretaceous and Cenozoic strata of the North Chukchi Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243015","programNote":"National and Global Petroleum Assessment","usgsCitation":"Houseknecht, D.W., Markey, C.P., Mercier, T.J., Schenk, C.J., Connors, C.D., Gooley, J.T., Botterell, P.J., Smith, R.A., Rouse, W.A., and Garrity, C.P., 2024, Assessment of undiscovered oil and gas resources of the North Chukchi Basin, outer continental shelf of the Chukchi and East Siberian Seas, Arctic Ocean, 2023 (ver. 1.2, July 2024): U.S. Geological Survey Fact Sheet 2024–3015, 4 p., https://doi.org/10.3133/fs20243015.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-159777","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":499118,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117018.htm","linkFileType":{"id":5,"text":"html"}},{"id":429520,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243015/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3015"},{"id":429506,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3015/fs20243015.xml"},{"id":429498,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2024/3015/version_Hist.txt","size":"4.00 KB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2024-3015 version history"},{"id":428754,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1O4VPJS","text":"USGS data release","linkHelpText":"National and Global Petroleum Assessment—North Chukchi Basin Province: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":428753,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3015/fs20243015.pdf","text":"Report","size":"1.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3015"},{"id":429505,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3015/images"},{"id":428752,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3015/coverthb3.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea, East Siberian Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 75\n ],\n [\n -179.9,\n 65\n ],\n [\n -140,\n 65\n ],\n [\n -140,\n 75\n ],\n [\n -179.9,\n 75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 160,\n 65\n ],\n [\n 179.9,\n 65\n ],\n [\n 179.9,\n 75\n ],\n [\n 160,\n 75\n ],\n [\n 160,\n 65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: May 2024; Version 1.1: June 2024; Version 1.2: July 2024","contact":"Director, Geology, Energy & Minerals Science Center
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12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Analysis of 70 years of snowfall in the Northern Hemisphere reveals that snow buffers the effect of varying precipitation levels on streamflow. The link highlights the need to rethink water-resource management as snow levels decline.
","language":"English","publisher":"Nature","doi":"10.1038/d41586-024-01239-6","usgsCitation":"Ryberg, K.R., 2024, Why snow is crucial for water supply — And what will happen when it becomes scarce: Nature, v. 629, p. 1013-1014, https://doi.org/10.1038/d41586-024-01239-6.","productDescription":"2 p.","startPage":"1013","endPage":"1014","ipdsId":"IP-164824","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":429867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"629","noUsgsAuthors":false,"publicationDate":"2024-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902947,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254782,"text":"70254782 - 2024 - Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","interactions":[],"lastModifiedDate":"2024-06-18T14:02:16.226227","indexId":"70254782","displayToPublicDate":"2024-05-29T08:30:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","title":"Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","docAbstract":"The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 and pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.121852","usgsCitation":"Skiendzielewski, K., Burch, T., Stokdyk, J.P., McGinnis, S., McLoughlin, S., Firnstahl, A.D., Spencer, S., Borchardt, M.A., and Murphy, H., 2024, Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed: Water Research, v. 259, 121852, 16 p., https://doi.org/10.1016/j.watres.2024.121852.","productDescription":"121852, 16 p.","ipdsId":"IP-159977","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439482,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2024.121852","text":"Publisher Index Page"},{"id":429633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","city":"Philadelphia","otherGeospatial":"Cobbs Creek, Tacony Creek, Wissahickon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.85988405796229,\n 40.143466276240844\n ],\n [\n -75.36578555034887,\n 40.16571325279898\n ],\n [\n -75.46114396657245,\n 39.85973677036142\n ],\n [\n -75.14696307943568,\n 39.87128371570064\n ],\n [\n -75.07770275607366,\n 39.95290291070239\n ],\n [\n -74.85988405796229,\n 40.143466276240844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skiendzielewski, K","contributorId":299292,"corporation":false,"usgs":false,"family":"Skiendzielewski","given":"K","email":"","affiliations":[],"preferred":false,"id":902522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":902523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGinnis, Shannon","contributorId":203051,"corporation":false,"usgs":false,"family":"McGinnis","given":"Shannon","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":902525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLoughlin, S","contributorId":299293,"corporation":false,"usgs":false,"family":"McLoughlin","given":"S","affiliations":[],"preferred":false,"id":902526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spencer, Sandy","contributorId":302981,"corporation":false,"usgs":false,"family":"Spencer","given":"Sandy","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902528,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":902529,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murphy, Heather","contributorId":203052,"corporation":false,"usgs":false,"family":"Murphy","given":"Heather","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":902530,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70254582,"text":"70254582 - 2024 - Temporally dense monitoring of pathogen occurrence at four drinking-water well sites – Insights and Implications","interactions":[],"lastModifiedDate":"2024-06-03T12:02:06.269876","indexId":"70254582","displayToPublicDate":"2024-05-29T06:59:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Temporally dense monitoring of pathogen occurrence at four drinking-water well sites – Insights and Implications","docAbstract":"Yearlong, event based, microbiological and chemical sampling was conducted at four public water supply well sites spanning a range of geologic settings and well depths to look for correlation between precipitation events and microbial occurrence. Near-continuous monitoring using autosamplers occurred just before, during, and after 5–7 sampling events triggered by rainfall and/or snowmelt. Microbial genetic material was noted at all four locations during all but one sampling event, but was exceedingly variable in time, where one sample would have no detections and the next sample could be a relatively high concentration. The highest microbial sums (microbial concentrations summed over an event) were observed during months in which precipitation exceeded historical averages. Extended wet conditions through the spring thaw resulted in the highest percentage of microbial positive samples, though at relatively low concentrations. Sampling events that followed drier than normal periods showed longer lag times between the onset of precipitation and microbial occurrence, as well as lower microbial detection rates. Although a general lag time pattern was observed at each site, the largest offset in time was observed at the site with the greatest depth to water. The study's temporally dense representation of drinking water pathogen characterization suggests that single event or infrequent periodic sampling of a drinking water supply cannot provide a representative characterization of the probability that pathogens are present, which likely has ramifications for calculating health risk assessments.
Species distributions are often indicative of historical biogeographical events and contemporary spatial biodiversity patterns. The Harlequin Duck Histrionicus histrionicus is a sea duck of conservation concern that has a disjunct distribution, with discrete portions of its range associated with northern Pacific and Atlantic Ocean basins. Movement data indicate migratory connectivity within regions of each ocean basin but not cross-continent dispersal, suggesting that genetic structuring could exist at multiple spatial scales. Little is known regarding the impacts of past vicariance events on the species phylogeographical structure and historical demography, or rates of gene flow at different spatial scales. We used data from microsatellite loci and mitochondrial DNA (mtDNA) sequences to quantify levels of genetic diversity within, and the extent of spatial genetic differentiation among locations sampled at multiple spatial scales across the species range. Samples were collected at nonbreeding locations, which represent groupings appropriate for characterizing genetically differentiated subgroups at regional and continental scales. Collectively, genetic data and coalescence modelling suggested that individuals colonized regions currently occupied within both ocean basins in the Holocene from a single refuge in the Atlantic. Further, it seems likely there was secondary contact with lineages derived from populations in Asia, based on the shallow species-wide mtDNA phylogeny and high incidence of recently derived private mtDNA haplotypes. Estimates of inter-location variance in microsatellite allele and mtDNA haplotype frequency were moderate and significant between western (Pacific – North America) and eastern (Atlantic – North America, Greenland and Iceland) ocean basins and among sampling groups within each ocean basin. Genetic differentiation among sampling groups was particularly evident at the species distributional margins in the Atlantic (Iceland) and the Pacific (Shemya Island) Ocean basins. Coalescent modelling results suggest that contemporary spatial genetic patterns in the species arose through the combined influences of secondary contact, shared ancestry and gene flow after the last glacial maxima.
No abstract available.
","language":"English","publisher":"Cornell University","doi":"10.7298/2nqt-1s83","usgsCitation":"Colberg, E., Morelli, T.L., and Brown-Lima, C., 2024, Guidelines for climate-smart invasive species management, 16 p., https://doi.org/10.7298/2nqt-1s83.","productDescription":"16 p.","ipdsId":"IP-166898","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Colberg, Eva M.","contributorId":355425,"corporation":false,"usgs":false,"family":"Colberg","given":"Eva M.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":937389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":937167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown-Lima, Carrie J.","contributorId":347882,"corporation":false,"usgs":false,"family":"Brown-Lima","given":"Carrie J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":937390,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254545,"text":"70254545 - 2024 - Thermal transfer rate is slower in bigger fish: How does body size affect response time of small, implantable temperature recording tags?","interactions":[],"lastModifiedDate":"2024-11-04T19:34:44.878213","indexId":"70254545","displayToPublicDate":"2024-05-28T08:27:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Thermal transfer rate is slower in bigger fish: How does body size affect response time of small, implantable temperature recording tags?","docAbstract":"The recent miniaturisation of implantable temperature recording tags has made measuring the water temperatures fish experience in the wild possible, but there may be a body size-dependent delay in implanted tag response time to changes in external temperature. To determine whether fish body size affects the response rate of implanted temperature tags, we implanted 20 Salvelinus fontinalis (127–228 mm fork length (FL), 15.1–120.4 g) with temperature recording tags and subjected them to rapid temperature changes (±8°C in less than 2 seconds) in the laboratory. We found that thermal transfer rates, and the lag in temperature tag response rate, was positively correlated with fish size, but the direction of temperature change (colder or warmer) had no significant effect. In fish exposed to a slower rate of temperature change (2°C h−1) implanted tags did not show a response lag. Understanding the limitations of this important technology is crucial to determining the utility of the data it produces and its ability to accurately measure fish thermal experience in the wild.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12794","usgsCitation":"O'Donnell, M.J., Regish, A.M., McCormick, S.D., and Letcher, B., 2024, Thermal transfer rate is slower in bigger fish: How does body size affect response time of small, implantable temperature recording tags?: Ecology of Freshwater Fish, v. 33, no. 4, e12794, 17 p., https://doi.org/10.1111/eff.12794.","productDescription":"e12794, 17 p.","ipdsId":"IP-159265","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":429398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":901833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regish, Amy M. 0000-0003-4747-4265","orcid":"https://orcid.org/0000-0003-4747-4265","contributorId":265360,"corporation":false,"usgs":true,"family":"Regish","given":"Amy","email":"","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":901834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":901835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":901836,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256078,"text":"70256078 - 2024 - MTAB 108, May 2024","interactions":[],"lastModifiedDate":"2024-07-19T13:15:13.771281","indexId":"70256078","displayToPublicDate":"2024-05-28T08:13:55","publicationYear":"2024","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":13451,"text":"Memo to All Banders (MTAB)","active":true,"publicationSubtype":{"id":30}},"title":"MTAB 108, May 2024","docAbstract":"This Memo to All Banders (MTAB 108) was released in May 2024. Subjects in this this memo are 1. The Chief’s Chirp; 2. Alerts – Highly Pathogenic Avian Influenza and reminder that banders cannot submit data through Bandit, only manage data; 3. Staff updates – meeting reports, BBL staff visits Foreman's Branch; 4. News – American Woodcock Migration, Women in Banding, and Surviving Migration Through an Urban Landscape; 5. A note from the permitting shelves – we are receiving a high volume of permit requests; 6. A note from the supply room – remove rejected band transfers from the Portal, and check your bands before banding; 7. Frequently asked questions – what's the difference between record lost, band lost, and band destroyed?; 8. Data management – helpful hint; 9. Banding and encounter highlights; 10. Auxiliary marker corner – check out our guide to submitting auxiliary marking data! and a note to shorebird banders; 11. Moments in history – World Migratory Bird Day; 12. Recent Publications; 13. Upcoming events; and 14. Request for information.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., and McKay, J.L., 2024, MTAB 108, May 2024: Memo to All Banders (MTAB), 14 p.","productDescription":"14 p.","ipdsId":"IP-166503","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":431241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":431228,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-108-may-2024"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":906627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Jennifer L. 0000-0002-8893-0231","orcid":"https://orcid.org/0000-0002-8893-0231","contributorId":296562,"corporation":false,"usgs":true,"family":"McKay","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":906670,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70254596,"text":"70254596 - 2024 - Retrospective review of the pathology of American pikas","interactions":[],"lastModifiedDate":"2024-10-07T16:07:57.929787","indexId":"70254596","displayToPublicDate":"2024-05-28T06:43:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2492,"text":"Journal of Veterinary Diagnostic Investigation","active":true,"publicationSubtype":{"id":10}},"title":"Retrospective review of the pathology of American pikas","docAbstract":"Soil erosion rates after wildfire are strongly controlled by intrinsic properties such as topography, weather, climate, soil, and vegetation. These landscape and hydroclimatic properties are important in determining post-fire erosion rates; however, their influence on post-fire erosion and their interaction with the intensity of a wildfire remains uncertain. A key limitation in resolving this uncertainty is the lack of conceptual models and frameworks for organising data related to the geomorphic sensitivity of landscapes to wildfire. Our aim is to develop a framework for consolidating understanding of post-fire erosion in the context of hydroclimatic conditions which contribute to system states, for example soil and vegetation properties, and wildfire regime. The framework is developed around a simple conceptual model where the change in erosion due to wildfire is a product of change in runoff generation and sediment supply, which is strongly related to landscape net primary productivity (NPP). We hypothesised that geomorphic sensitivity to wildfire should vary as a unimodal humped relationship across a gradient of NPP, peaking at an intermediate level. To develop this framework and to test the hypothesis, we first review intrinsic soil and vegetation properties related to the supply and transport of sediment from burned and unburned hillslopes. Net primary productivity is systematically related to these intrinsic properties because it integrates many processes involved in soil and vegetation development. Empirical data indicate a trend in the change in surface runoff generation with NPP after wildfire, peaking at an NPP of approximately 15 Mg C ha−1 y−1. A simple model of fuel availability and soil heating are correlated with a similar “humped” trend in sediment supply. These results are consistent with our conceptual model, which indicates that sediment supply and runoff contribute towards a distinct peak in wildfire effects on erosion at an intermediate level of NPP. We propose that landscapes of intermediate NPP typically have the highest quantity of fuel available to burn, which cause large changes to the soil surface properties. Landscapes at intermediate NPP also tend to produce intrinsic soil and vegetation properties that promote erosion after wildfire. The interplay between these short and long-term landscape characteristics is strongest at intermediate levels of NPP. Our proposed biogeographic model of geomorphic sensitivity to wildfire was supported by erosion data from burned hillslope and zero-order catchments studies from a range fire-prone landscapes in Australia and North America. Our proposed conceptual model will help identify areas most vulnerable to post-fire erosion changes.
The loss of tidal wetlands in the San Francisco Bay estuary have led to declines in native fish presence. Restoration of tidal wetlands in this area has intensified, with a primary goal of increasing the number of native fishes. We compared the presence of longfin smelt in naturally accreted and beneficial dredge reuse wetlands as a measure of successful restoration. We used environmental DNA (eDNA) analyses as our metric for fish presence and fish community composition, employing two different water sampling methods for comparison (standard and high-volume). Longfin smelt were present in multiple sites, but at numbers too low for accurate comparisons across sites. Community composition varied based on the water sampling method, but the presence/absence of longfin smelt was consistent across sampling methods. As this represents a pilot study, further refinement of methodology is necessary, but the use of high-volume water sampling methods is promising.
Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.
Explosive volcanic eruptions produce hazardous atmospheric plumes composed of tephra particles, hot gas and entrained air. Such eruptions are generally driven by magmatic fragmentation or steam expansion. However, an eruption mechanism outside this phreatic–magmatic spectrum was suggested by a sequence of 12 explosive eruptions in May 2018 at Kīlauea, Hawaii, that occurred during the early stages of caldera collapse and produced atmospheric plumes reaching 8 km above the vent. Here we use seismic inversions for reservoir pressure as a source condition for three-dimensional simulations of transient multiphase eruptive plume ascent through a conduit and stratified atmosphere. We compare the simulations with conduit ascent times inferred from seismic and infrasound data, and with plume heights from radar data. We find that the plumes are consistent with eruptions caused by a stomp-rocket mechanism involving the abrupt subsidence of reservoir roof rock that increased pressure in the underlying magma reservoir. In our model, the reservoir was overlain by a pocket of accumulated high-temperature magmatic gas and lithic debris, which were driven through a conduit approximately 600 m long to erupt particles at rates of around 3,000 m3 s−1. Our results reveal a distinct collapse-driven type of eruption and provide a framework for integrating diverse geophysical and atmospheric data with simulations to gain a better understanding of unsteady explosive eruptions.
Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer (Odocoileus virginianus) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110756","usgsCitation":"Bondo, K.J., Rosenberry, C., Stainbrook, D., and Walter, W., 2024, Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types: Ecological Modeling, v. 493, 110756, 16 p., https://doi.org/10.1016/j.ecolmodel.2024.110756.","productDescription":"110756, 16 p.","ipdsId":"IP-163658","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bondo, Kristin J.","contributorId":343150,"corporation":false,"usgs":false,"family":"Bondo","given":"Kristin","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":910646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Christopher S.","contributorId":343151,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stainbrook, David","contributorId":343152,"corporation":false,"usgs":false,"family":"Stainbrook","given":"David","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910649,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261997,"text":"70261997 - 2024 - Development and evaluation of public-supply community water service area boundaries for the conterminous United States","interactions":[],"lastModifiedDate":"2025-01-08T15:23:27.857215","indexId":"70261997","displayToPublicDate":"2024-05-26T09:18:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of public-supply community water service area boundaries for the conterminous United States","docAbstract":"The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13210","usgsCitation":"Buchwald, C.A., Houston, N., Stewart, J.S., Alzraiee, A.H., Niswonger, R.G., and Larsen, J., 2024, Development and evaluation of public-supply community water service area boundaries for the conterminous United States: Journal of the American Water Resources Association, v. 60, no. 4, p. 879-896, https://doi.org/10.1111/1752-1688.13210.","productDescription":"18 p.","startPage":"879","endPage":"896","ipdsId":"IP-129020","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1111/1752-1688.13210","text":"Publisher Index 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Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":922612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922613,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254929,"text":"70254929 - 2024 - Prion seeding activity in plant tissues detected by RT-QuIC","interactions":[],"lastModifiedDate":"2024-06-11T13:53:25.440832","indexId":"70254929","displayToPublicDate":"2024-05-26T08:48:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9113,"text":"Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Prion seeding activity in plant tissues detected by RT-QuIC","docAbstract":"Prion diseases such as scrapie, bovine spongiform encephalopathy (BSE), and chronic wasting disease (CWD) affect domesticated and wild herbivorous mammals. Animals afflicted with CWD, the transmissible spongiform encephalopathy of cervids (deer, elk, and moose), shed prions into the environment, where they may persist and remain infectious for years. These environmental prions may remain in soil, be transported in surface waters, or assimilated into plants. Environmental sampling is an emerging area of TSE research and can provide more information about prion fate and transport once shed by infected animals. In this study, we have developed the first published method for the extraction and detection of prions in plant tissue using the real-time quaking-induced conversion (RT-QuIC) assay. Incubation with a zwitterionic surfactant followed by precipitation with sodium phosphotungstate concentrates the prions within samples and allows for sensitive detection of prion seeding activity. Using this protocol, we demonstrate that prions can be detected within plant tissues and on plant surfaces using the RT-QuIC assay.
","language":"English","publisher":"MDPI","doi":"10.3390/pathogens13060452","usgsCitation":"Burgener, K., Lichtenberg, S.S., Walsh, D.P., Inzalaco, H., Lomax, A., and Pedersen, J., 2024, Prion seeding activity in plant tissues detected by RT-QuIC: Pathogens, v. 13, no. 6, 452, 12 p., https://doi.org/10.3390/pathogens13060452.","productDescription":"452, 12 p.","ipdsId":"IP-163951","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/pathogens13060452","text":"Publisher Index Page"},{"id":429868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Burgener, Kate","contributorId":338037,"corporation":false,"usgs":false,"family":"Burgener","given":"Kate","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lichtenberg, Stuart Siegfried","contributorId":338040,"corporation":false,"usgs":false,"family":"Lichtenberg","given":"Stuart","email":"","middleInitial":"Siegfried","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":902916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Inzalaco, Heather","contributorId":338043,"corporation":false,"usgs":false,"family":"Inzalaco","given":"Heather","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lomax, Aaron","contributorId":338045,"corporation":false,"usgs":false,"family":"Lomax","given":"Aaron","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pedersen, Joel","contributorId":338048,"corporation":false,"usgs":false,"family":"Pedersen","given":"Joel","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":902920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255784,"text":"70255784 - 2024 - Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","interactions":[],"lastModifiedDate":"2024-07-09T14:40:37.188664","indexId":"70255784","displayToPublicDate":"2024-05-26T06:43:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9121,"text":"Frontiers Earth Science Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","title":"Viral pathogen detection in U.S. game-farm mallard (Anas platyrhynchos) flags spillover risk to wild birds","docAbstract":"The threat posed by emerging infectious diseases is a major concern for global public health, animal health and food security, and the role of birds in transmission is increasingly under scrutiny. Each year, millions of mass-reared game-farm birds are released into the wild, presenting a unique and a poorly understood risk to wild and susceptible bird populations, and to human health. In particular, the shedding of enteric pathogens through excrement into bodies of water at shared migratory stop-over sites, and breeding and wintering grounds, could facilitate multi-species long-distance pathogen dispersal and infection of high numbers of naive endemic birds annually. The Mallard (Anas platyrhynchos) is the most abundant of all duck species, migratory across much of its range, and an important game species for pen-rearing and release. Major recent population declines along the US Atlantic coast has been attributed to game-farm and wild mallard interbreeding and the introduction maladaptive traits into wild populations. However, pathogen transmission and zoonosis among game-farms Mallard may also impact these populations, as well as wildlife and human health. Here, we screened 16 game-farm Mallard from Wisconsin, United States, for enteric viral pathogens using metatranscriptomic data. Four families of viral pathogens were identified – Picobirnaviridae (Genogroup I), Caliciviridae (Duck Nacovirus), Picornaviridae (Duck Aalivirus) and Sedoreoviridae (Duck Rotavirus G). To our knowledge, this is the first report of Aalivirus in the Americas, and the first report of Calicivirus outside domestic chicken and turkey flocks in the United States. Our findings highlight the risk of viral pathogen spillover from peri-domestically reared game birds to naive wild bird populations.
Predator non-consumptive effects (NCE) can alter prey foraging time and habitat use, potentially reducing fitness. Prey can mitigate NCEs by increasing vigilance, chewing-vigilance synchronization, and spatiotemporal avoidance of predators. We quantified the relationship between Mexican wolf (Canis lupus baileyi) predation risk and elk (Cervus canadensis) behavior. We conducted behavioral observations on adult female elk and developed predation risk indices using GPS collar data from Mexican wolves, locations of elk killed by wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking. Metrics that quantified both spatial and temporal predation risk were the most predictive. Vigilance was positively associated with increased predation risk. The effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was lower, the probability of foraging increased while resting decreased in high-risk areas. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. Probability of multitasking depended on canopy openness and an interaction between maternal period and predation risk; multitasking decreased prior to parturition and increased post parturition in high-risk areas. Openness was inversely related to multitasking. These results suggest adult female elk are altering the type of vigilance used depending on resource availability/quality, current energetic needs, and predation risk. Our results highlight potentially important, but often-excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator–prey dynamics.
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.
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Rossi, Beauprez, G., Kuklinski, K.E., Martin, R., Koslovsky, M.D., Rintz, T.B., and Hooten, M., 2024, Melded integrated population models: Journal of Agricultural, Biological and Environmental Statistics, v. 5, 31 p., https://doi.org/10.1007/s13253-024-00620-2.","productDescription":"31 p.","ipdsId":"IP-157570","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.41733481268557,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 38.10618949808156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Ee, Justin J.","contributorId":341159,"corporation":false,"usgs":false,"family":"Van Ee","given":"Justin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagen, Christian A.","contributorId":341160,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[],"preferred":false,"id":908024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavlacky, David C. 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The scenarios included historical and future drought conditions, and a land-cover condition where shrubland and forest within the cloud zone were converted to grassland. For the historical drought condition, island-wide mean annual recharge estimates range from a decrease of 30 percent (239 million gallons per day [Mgal/d]) for Kauaʻi to a decrease of 39 percent (2,706 Mgal/d) for the Island of Hawaiʻi, relative to the reference condition consisting of 1978–2007 rainfall and 2020 land cover. For the future drought condition, estimates of island-wide mean annual recharge range from a decrease of 40 percent (477 Mgal/d) on Maui to a decrease of 51 percent (116 Mgal/day) on Molokaʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition produces estimated land-cover-related decreases in island-wide mean annual recharge (in addition to the drought-related decreases) of 11–12 Mgal/d on Oʻahu, 119–135 Mgal/day on Maui, and 689–849 Mgal/d on the Island of Hawaiʻi. The spatial distributions of increases in conditions indicative of moisture stress and potential wildfire hazard were quantified using the relative frequency of soil moisture less than a selected threshold value (monthly mean soil moisture less than 0.074, expressed as a fraction of available water capacity), evapotranspiration less than a selected threshold value (monthly evapotranspiration less than 0.96 inches), and climatic water deficit greater than a selected threshold value (monthly climatic water deficit greater than 0.77, expressed as fraction of potential evapotranspiration). For the historical drought condition, the greatest increases in the relative frequency for the moisture-stress indicators occur across parts of east and southwest Kauaʻi; central, east, and west Oʻahu; central Molokaʻi; central Maui and low- to mid-altitude parts of West Maui volcano; and the northwestern and southern parts of the Island of Hawaiʻi. For the future drought condition, the greatest increases in the relative frequency of the moisture-stress indicators occur across parts of west Kauaʻi; central and west Oʻahu and Molokaʻi; a band of mid-altitude area on the southern slope of West Maui volcano and across the southwestern slope of Haleakalā; and mid-altitude areas of the northwestern and southern parts of the Island of Hawaiʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition results in land-cover-related increases in the relative frequency of moisture-stress indicators around Kaʻala in the Waiʻanae Range and the southeastern part of the Koʻolau Range on Oʻahu, the southern part of West Maui volcano and the southwestern slope of Haleakalā on Maui, and the upland regions of the western and southern parts of the Island of Hawaiʻi.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235141","collaboration":"Prepared in cooperation with the Pacific Islands Climate Adaptation Science Center","usgsCitation":"Mair, A., Oki, D.S., Kāne, H.L., Johnson, A.G., and Rotzoll, K., 2024, Effects of drought and cloud-water interception on groundwater recharge and wildfire hazard for recent and future climate conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2023–5141, 98 p., https://doi.org/10.3133/sir20235141","productDescription":"Report: viii, 98 p.; 2 Data Releases","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-139810","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":429177,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235130","text":"Scientific Investigations Report 2023-5130","linkHelpText":"- Estimated Groundwater Recharge for Mid-Century and End-of-Century Climate Projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i"},{"id":429174,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGHWS4","text":"USGS Data Release","description":"Mair, A., 2024, Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9HGHWS4.","linkHelpText":"Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions"},{"id":429173,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDP1C6","text":"USGS Data Release","description":"Mair, A., 2024, Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9DDP1C6.","linkHelpText":"Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions"},{"id":429175,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5141/sir20235141.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499403,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117007.htm","text":"Island of Hawaii","linkFileType":{"id":5,"text":"html"}},{"id":499402,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117006.htm","text":"Islands of Kauai, Oahu, Molokai, and 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.
","language":"English","publisher":"Copernicus","doi":"10.5194/os-20-661-2024","usgsCitation":"Yates, K., Fehr, Z., Johnson, S.A., and Zawada, D., 2024, Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA: Ocean Science, v. 20, no. 3, p. 661-688, https://doi.org/10.5194/os-20-661-2024.","productDescription":"28 p.","startPage":"661","endPage":"688","ipdsId":"IP-157460","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/os-20-661-2024","text":"Publisher Index Page"},{"id":429348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Looe Key Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.84282237828644,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.502714814781257\n ],\n [\n -81.66555054932407,\n 24.603571498742696\n ],\n [\n -81.84282237828644,\n 24.603571498742696\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":217808,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fehr, Zachery","contributorId":336991,"corporation":false,"usgs":false,"family":"Fehr","given":"Zachery","affiliations":[{"id":64427,"text":"Cherokee Nation System Solutions","active":true,"usgs":false}],"preferred":false,"id":901671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Selena Anne-Marie 0000-0003-1015-1788","orcid":"https://orcid.org/0000-0003-1015-1788","contributorId":296373,"corporation":false,"usgs":true,"family":"Johnson","given":"Selena","email":"","middleInitial":"Anne-Marie","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":901673,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254427,"text":"sir20235130 - 2024 - Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","interactions":[],"lastModifiedDate":"2026-01-30T19:28:16.899695","indexId":"sir20235130","displayToPublicDate":"2024-05-24T09:48:23","publicationYear":"2024","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":"2023-5130","displayTitle":"Estimated Groundwater Recharge for Mid-Century and End-of-Century Climate Projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","title":"Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i","docAbstract":"Demand for freshwater in the State of Hawaiʻi is expected to increase by roughly 13 percent from 2020 to 2035. Groundwater availability in Hawaiʻi is affected by a number of factors, including land cover, rainfall, runoff, evapotranspiration, and climate change. To evaluate the availability of fresh groundwater under projected future-climate conditions, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of groundwater recharge for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, Maui, and the Island of Hawaiʻi for recent climate conditions and three future-climate scenarios. Climate conditions from 1978 to 2007 were used as the reference period for recent climate conditions on each island. The three future-climate scenarios were developed using available high-resolution downscaled climate projections that include (1) a mid-century scenario using projected rainfall conditions for the Representative Concentration Pathway (RCP) scenario during 2041–71 with a total radiative forcing of 8.5 watts per square meter by the year 2100 (RCP8.5 2041–71 scenario), (2) a dry-climate scenario using projected rainfall conditions for the RCP8.5 scenario during 2071–99, and (3) a wet-climate scenario using projected rainfall conditions for the “Special Report on Emissions Scenarios” A1B scenario during 2080–99 for Maui, the RCP4.5 scenario during 2080–99 for Kauaʻi, Lānaʻi, and the Island of Hawaiʻi, and the RCP8.5 scenario during 2080–99 for Oʻahu and Molokaʻi. An additional drought scenario was added for Lānaʻi to assess the effect of extreme drought conditions during 2008–12 on groundwater recharge. All scenarios used 2020 land cover.
Mean annual groundwater recharge is estimated to decrease between 5 and 55 percent on all six islands in this study for the mid-century and dry-climate scenarios relative to the reference-period recharge. Recharge is estimated to increase for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, and Maui between 2 and 43 percent and decrease for the Island of Hawaiʻi by about 4 percent for the wet-climate scenario. Comparing the mid-century and dry-climate scenarios, all 110 aquifer systems (management areas defined by the State of Hawaiʻi Commission on Water Resource Management) from all six islands show similar direction in drying (104 aquifer systems) or wetting (6 aquifer systems) changes for recharge. However, among the three future scenarios, only 35 of 110 aquifer systems show similar direction in drying (30 aquifer systems) or wetting (5 aquifer systems) changes for recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235130","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management and the Pacific Islands Climate Adaptation Science Center and in collaboration with Pūlama Lāna‘i","usgsCitation":"Kāne, H.L., Mair, A., Johnson, A.G., Rotzoll, K., Mifflin, J., and Oki, D.S., 2024, Estimated groundwater recharge for mid-century and end-of-century climate projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2023–5130, 133 p., https://doi.org/10.3133/sir20235130.","productDescription":"Report: viii, 133 p.; Data Release","numberOfPages":"133","onlineOnly":"Y","ipdsId":"IP-136143","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499393,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117004.htm","linkFileType":{"id":5,"text":"html"}},{"id":429217,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235141","text":"Scientific Investigations Report 2023-5141","linkHelpText":"- Effects of Drought and Cloud-Water Interception on Groundwater Recharge and Wildfire Risk for Recent and Future Climate Conditions, Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi"},{"id":429216,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5130/sir20235130.pdf","text":"Report","size":"33 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P972KMSL","text":"USGS Data Release","description":"Kāne, H.L., Mair, A., and Mifflin, J., 2024, Mean annual water-budget components for Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i, for a set of recent and future-climate conditions, 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Shriver, L.C., 2024, Spring 2024 edition: RAMPS Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-166294","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":430572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430571,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cms.usgs.gov/centers/southwest-biological-science-center/news/ramps-newsletter-spring-2024","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Laura Cecilia 0009-0008-5567-0868","orcid":"https://orcid.org/0009-0008-5567-0868","contributorId":334175,"corporation":false,"usgs":true,"family":"Shriver","given":"Laura","email":"","middleInitial":"Cecilia","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":905032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}