Avian populations must mount effective immune responses upon exposure to environmental stressors such as avian influenza and xenobiotics. Although multiple immune assays have been tested and applied to various avian species, antibody-mediated immune responses in non-model avian species are not commonly reported due to the lack of commercially available species-specific antibodies. The objectives of the present study were to advance methods for studying wild bird immune responses and to apply these to the evaluation of cytological responses after exposure of American kestrels, Falco sparverius, to a commercial flame retardant mixture containing isopropylated triarylphosphate isomers (ITP). Hatchlings were gavaged daily with safflower oil or 1.5 ug/g bw/day of ITP suspended in safflower oil, then bled on days 9, 17, and 21. The ITP treatment group (n = 18) and a subset of controls (Poly I:C treatment group; n = 10) were injected on days 9 and 15 with a synthetic analog of viral double-stranded RNA, polyinosinic:polycytidylic acid (Poly I:C), a toll-like receptor ligand and synthetic viral mimic, and responses compared to a sham injected control group (n = 8). The hypotheses tested whether kestrels showed immunological differences among treatment groups, genetic sex, and/or white blood cell (WBC) subpopulation type over time. A flow cytometry (FCM) gating strategy categorized heterophils (H), lymphocytes (L), and monocytes (M) and their proportions, and measured relative fluorescence in response to anti-chicken CD4 binding. Fluorescent cell surfaces and some granular/vacuolar inclusions were visualized by epifluorescence microscopy. A fourth subpopulation with higher levels of granularity than M but less than H became increasingly apparent with time and was gated along with the H subpopulation; its frequency of occurrence was lowest in the ITP group (P = 0.0023). The percentages of cells differed among treatment groups, days, and sexes (P = 0.0001). For both sexes, percentages of H and L were higher than M in control and Poly I:C. In the ITP group, L percentages were higher than H and M (P = 0.0457), and H and L were higher than M on days 9 and 21 (P = 0.0001). The ratios of H:L and H:WBC, indicators of robust immunity, were also higher on days 9 and 21 than on 17 (P = 0.0079). For each sex, the highest levels of activity measured by FCM geometric means (GEO) of fluorescence (indicative of antibody binding) were observed on day 9 (P = 0.0001 female, and P = 0.0011 male) in H over both L and M (P < 0.0001 for each). In males, GEO of the Poly I:C group was higher than that of the ITP group (P = 0.0374), with no difference observed among females over all days. By using a FCM algorithm for population comparisons of fluorescence to investigate binding within H, the T(x) scores indicated higher fluorescence in control and Poly I:C groups over ITP (P = 0.0001). Unlike chickens, Gallus gallus, which express CD4 primarily on L, kestrels bound the commercial antibody primarily within the gated H subpopulation, suggesting an immunophenotypic difference between taxa, despite a ~60% identity of Falco CD4 amino acid sequences with chicken CD4. The emergent cell subset within the gated H presented dendritic-like cell (DLC) morphological and functional properties, apparently serving as an effector cell. This study adds interpretive context to ecological investigations of infection and of potential immunomodulation by emerging compounds, whereby the early innate responses are mediated by the various cell subsets serving as useful quantitative markers of immunological condition. Data showed that dietary exposure to ITP was immunosuppressive for male and female kestrels over the course of the experiment, reducing DLC frequency compared to the Poly I:C controls. Heterophils and DLC were important in facilitating innate immunological responses.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envint.2021.106779","usgsCitation":"Jenkins, J., Baudoin, B.A., Johnson, D., Fernie, K.J., Stapelton, H.M., and Karouna-Renier, N., 2021, Establishment of baseline cytology metrics in nestling American kestrels (Falco sparverius): Immunomodulatory effects of the flame retardant isopropylated triarylphosphate isomers: Environment International, v. 157, 106779, 15 p.; Data Release, https://doi.org/10.1016/j.envint.2021.106779.","productDescription":"106779, 15 p.; Data Release","ipdsId":"IP-116785","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450748,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envint.2021.106779","text":"Publisher Index Page"},{"id":436196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P7ZTMU","text":"USGS data release","linkHelpText":"Laboratory analysis assessing immune response after flame retardant exposure in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":436195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SGX37F","text":"USGS data release","linkHelpText":"Discerning innate immunity in American kestrels, Falco sparverius, through 21 days post-hatch"},{"id":390889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417862,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9sgx37f"}],"volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Jill 0000-0002-5087-0894","orcid":"https://orcid.org/0000-0002-5087-0894","contributorId":206575,"corporation":false,"usgs":true,"family":"Jenkins","given":"Jill","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baudoin, Brooke A 0000-0003-2874-1604","orcid":"https://orcid.org/0000-0003-2874-1604","contributorId":267938,"corporation":false,"usgs":true,"family":"Baudoin","given":"Brooke","email":"","middleInitial":"A","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":825644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernie, Kim J.","contributorId":211241,"corporation":false,"usgs":false,"family":"Fernie","given":"Kim","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":825645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stapelton, Heather M. 0000-0002-9995-6517","orcid":"https://orcid.org/0000-0002-9995-6517","contributorId":267940,"corporation":false,"usgs":false,"family":"Stapelton","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":825646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":825647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224957,"text":"70224957 - 2021 - Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","interactions":[],"lastModifiedDate":"2021-10-11T15:55:41.405172","indexId":"70224957","displayToPublicDate":"2021-09-20T10:49:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","docAbstract":"RRestoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30–60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108213","usgsCitation":"Mejia, F.H., Connor, J.M., Kaufmann, P.R., Torgersen, C.E., Berntsen, E.K., and Andersen, T., 2021, Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration: Ecological Indicators, no. 131, 108213, 14 p., https://doi.org/10.1016/j.ecolind.2021.108213.","productDescription":"108213, 14 p.","ipdsId":"IP-119748","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108213","text":"Publisher Index Page"},{"id":390391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Washington","otherGeospatial":"Priest River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.191725575618726\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":824849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Jason M","contributorId":267258,"corporation":false,"usgs":false,"family":"Connor","given":"Jason","email":"","middleInitial":"M","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaufmann, Phil R","contributorId":267259,"corporation":false,"usgs":false,"family":"Kaufmann","given":"Phil","email":"","middleInitial":"R","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":824851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":824852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berntsen, Eric K","contributorId":214885,"corporation":false,"usgs":false,"family":"Berntsen","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":39131,"text":"Kalispel Tribe of Indians","active":true,"usgs":false}],"preferred":false,"id":824853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andersen, Todd","contributorId":243418,"corporation":false,"usgs":false,"family":"Andersen","given":"Todd","email":"","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824854,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229187,"text":"70229187 - 2021 - Honey bee (Apis mellifera) colonies benefit from grassland/ pasture while bumble bee (Bombus impatiens) colonies in the same landscapes benefit from non-corn/soybean cropland","interactions":[],"lastModifiedDate":"2022-03-02T16:44:23.45246","indexId":"70229187","displayToPublicDate":"2021-09-20T10:30:41","publicationYear":"2021","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":"Honey bee (Apis mellifera) colonies benefit from grassland/ pasture while bumble bee (Bombus impatiens) colonies in the same landscapes benefit from non-corn/soybean cropland","docAbstract":"Agriculturally important commercially managed pollinators including honey bees (Apis mellifera L., 1758) and bumble bees (Bombus impatiens Cresson, 1863) rely on the surrounding landscape to fulfill their dietary needs. A previous study in Europe demonstrated that managed honey bee foragers and unmanaged native bumble bee foragers are associated with different land uses. However, it is unclear how response to land use compares between managed honey bees and a managed native bumble bee species in the United States, where honey bees are an imported species. Furthermore, to our knowledge, no such direct comparisons of bee responses to land use have been made at the colony level. To better understand how two different social bees respond to variation in land use, we monitored the weights of A. mellifera and B. impatiens colonies placed in 12 apiaries across a range of land use in Michigan, United States in 2017. Bombus impatiens colonies gained more weight and produced more drones when surrounded by diverse agricultural land (i.e., non-corn/soybean cropland such as tree fruits and grapes), while honey bee colonies gained more weight when surrounded by more grassland/pasture land. These findings add to our understanding of how different bee species respond to agricultural landscapes, highlighting the need for further species-specific land use studies to inform tailored land management.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0257701","usgsCitation":"Quinlan, G., Milbrath, M., Otto, C., and Isaacs, R., 2021, Honey bee (Apis mellifera) colonies benefit from grassland/ pasture while bumble bee (Bombus impatiens) colonies in the same landscapes benefit from non-corn/soybean cropland: PLoS ONE, v. 16, no. 9, p. 1-12, https://doi.org/10.1371/journal.pone.0257701.","productDescription":"e0257701, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-130366","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":450754,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0257701","text":"Publisher Index Page"},{"id":396654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.05639648437499,\n 41.705728515237524\n ],\n [\n -84.00146484374999,\n 43.32517767999296\n ],\n [\n -86.41845703124999,\n 43.27720532212024\n ],\n [\n -86.253662109375,\n 43.004647127794435\n ],\n [\n -86.2646484375,\n 42.67435857693381\n ],\n [\n -86.319580078125,\n 42.45588764197166\n ],\n [\n -86.517333984375,\n 42.17968819665961\n ],\n [\n -86.671142578125,\n 41.95131994679697\n ],\n [\n -86.934814453125,\n 41.763117447005875\n ],\n [\n -84.825439453125,\n 41.77131167976407\n ],\n [\n -84.825439453125,\n 41.705728515237524\n ],\n [\n -84.05639648437499,\n 41.705728515237524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"editors":[{"text":"Dolezal, Adam","contributorId":210716,"corporation":false,"usgs":false,"family":"Dolezal","given":"Adam","email":"","affiliations":[{"id":38135,"text":"Illinois","active":true,"usgs":false}],"preferred":false,"id":836923,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Quinlan, Gabriela","contributorId":287574,"corporation":false,"usgs":false,"family":"Quinlan","given":"Gabriela","email":"","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":836895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milbrath, Megan","contributorId":287575,"corporation":false,"usgs":false,"family":"Milbrath","given":"Megan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":836896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":836897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isaacs, Rufus","contributorId":287577,"corporation":false,"usgs":false,"family":"Isaacs","given":"Rufus","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":836898,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224266,"text":"sir20215062 - 2021 - Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","interactions":[],"lastModifiedDate":"2021-09-21T11:32:14.387182","indexId":"sir20215062","displayToPublicDate":"2021-09-20T09:49:44","publicationYear":"2021","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":"2021-5062","displayTitle":"Development of Regression Equations for the Estimation of the Magnitude and Frequency of Floods at Rural, Unregulated Gaged and Ungaged Streams in Puerto Rico Through Water Year 2017","title":"Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","docAbstract":"The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area.
The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection.
Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study.
The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats (https://streamstats.usgs.gov/ss/). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215062","usgsCitation":"Ryan, P.J., Gotvald, A.J., Hazelbaker, C.L., Veilleux, A.G., and Wagner, D.M., 2021, Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017: U.S. Geological Survey Scientific Investigations Report 2021–5062, 37 p., https://doi.org/10.3133/sir20215062.","productDescription":"Report: v, 37 p.; Appendix Tables: 3; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123614","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":389343,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91XT14B","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data files for the development of regression equations for estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017"},{"id":389335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5062/coverthb.jpg"},{"id":389336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062.pdf","text":"Report","size":"4.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5062"},{"id":389337,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.csv","text":"Appendix Table 2.1 (.csv format)","size":"5.89 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389340,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.xlsx","text":"Appendix 1 (.xlsx format)","size":"30.9 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389338,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.xlsx","text":"Appendix Table 2.1 (.xlsx format)","size":"19.6 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389339,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.csv","text":"Appendix 1 (.csv format)","size":"20.7 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389341,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.csv","text":"Appendix 3 (.csv format)","size":"80.4 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"},{"id":389342,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.xlsx","text":"Appendix 3 (.xlsx format)","size":"134 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"}],"country":"United States","otherGeospatial":"Puerto 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4446 Pet Lane, Suite 108
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Coastal research frequently involves the use of a GIS to analyze large areas for changes in response to major weather events, human action, and other factors. The GIS workflows used to conduct these analyses can be complex and sometimes require multiple days to complete. Long runtimes often exist even on modern high-powered workstations if the GIS software does not use parallel computing techniques, which prevents it from fully utilizing the capabilities of multicore processors. If a GIS application supports a programming interface that allows geoprocessing tools to be called from an external program, then GIS workflows can use parallel functionality embedded in that programming language to divide the load of a large workflow among multiple child processes. In ArcMap and ArcGIS Pro, this technique can be implemented by using the Python programming interface and the multiprocessing module in Python to run geoprocessing tools in child processes. This method was used in the Seafloor Elevation Change Analysis Tool (SECAT), a Python script written for ArcMap and ArcGIS Pro that calculates changes in seafloor elevation over time using two different digital elevation models. Running SECAT with between one and eight child processes on two different datasets improved execution times by at least a factor of 2.4. These results demonstrate that using the Python multiprocessing module can significantly accelerate a variety of time-consuming workflows.
","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-21-00026.1","usgsCitation":"Zieg, J.A., and Zawada, D., 2021, Improving ESRI ArcGIS performance of coastal and seafloor analysis with the Python multiprocessing module: Journal of Coastal Research, v. 37, no. 6, p. 1288-1293, https://doi.org/10.2112/JCOASTRES-D-21-00026.1.","productDescription":"6 p.","startPage":"1288","endPage":"1293","ipdsId":"IP-117051","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":397108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zieg, Jonathan Andrew 0000-0002-4590-9328","orcid":"https://orcid.org/0000-0002-4590-9328","contributorId":288476,"corporation":false,"usgs":true,"family":"Zieg","given":"Jonathan","email":"","middleInitial":"Andrew","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":837980,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230277,"text":"70230277 - 2021 - Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","interactions":[],"lastModifiedDate":"2023-06-09T14:07:06.207792","indexId":"70230277","displayToPublicDate":"2021-09-20T09:00:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5094,"text":"Regional Studies in Marine Science","onlineIssn":"2352-4855","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","title":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","docAbstract":"Evaluating resource use patterns for imperiled species is critical for understanding what supports their populations. Here we established stable isotope (
While the period from fledging through first breeding for waterbird species such as terns (e.g., genus Sterna, Sternula) is of great interest to researchers and conservationists, this period remains understudied due in large part to the difficulty of marking growing juveniles with radio transmitters that remain attached for extended periods.
In an effort to facilitate such research, we examined the impact of various combinations of harness types (backpack, leg-loop, and 3D-printed harnesses), harness materials (Automotive ribbon, Elastic cord, and PFTE ribbon), and transmitter types (center-weighted and rear-weighted) on a surrogate for juvenile terns, 28-day-old Japanese quail (Coturnix japonica; selected due to similarities in adult mass and downy feathering of juveniles), in a 30-day experiment. We monitored for abrasion at points of contact and tag gap issues via daily exams while also recording mass and wing cord as indices of growth. This study was designed to serve as an initial examination of the impacts of marking on the growth and development of young birds and does not account for any impacts of tags on movement or behavior.
While we found that treatment (the specific combination of the transmitter type, harness type, and harness material) had no impact on bird growth relative to unmarked control birds (P ≥ 0.05), we did observe differences in abrasion and tag gap between treatments (P ≤ 0.05). Our results suggest that leg-loop harnesses constructed from elastic cord and backpack harnesses from PFTE ribbon are suitable options for long-term attachment to growing juveniles. Conversely, we found that automotive ribbon led to extensive abrasion with these small-bodied birds, and that elastic cord induced blisters when used to make a backpack harness.
While these results indicate that long-term tagging of juvenile birds is possible with limited impacts on growth, this work does not preclude the need for small-scale studies with individual species. Instead, we hope this provides an informed starting point for further exploration of this topic.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-021-00257-9","usgsCitation":"Buck, E., Sullivan, J.D., Kent, C.M., Mullinax, J.M., and Prosser, D., 2021, A comparison of methods for the long-term harness-based attachment of radio-transmitters to juvenile Japanese quail (Coturnix japonica): Animal Biotelemetry, v. 9, 32, 16 p., https://doi.org/10.1186/s40317-021-00257-9.","productDescription":"32, 16 p.","ipdsId":"IP-126974","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450759,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-021-00257-9","text":"Publisher Index Page"},{"id":436197,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LZD1V0","text":"USGS data release","linkHelpText":"Testing transmitter types, harness types, and harness materials for attachment of radio transmitters onto avian chicks"},{"id":389472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":823482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":823483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":823484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":823485,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823486,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226195,"text":"70226195 - 2021 - USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition","interactions":[],"lastModifiedDate":"2021-11-16T13:11:54.650412","indexId":"70226195","displayToPublicDate":"2021-09-20T07:11:31","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"displayTitle":"USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) Newsletter – Summer 2021 Edition","title":"USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Munson, S.M., and McCormick, M.L., 2021, USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-133237","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":391726,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/center-news/ramps-newsletter-summer-2021-edition"},{"id":391745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":826841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Molly L. 0000-0002-4361-7567 mmccormick@usgs.gov","orcid":"https://orcid.org/0000-0002-4361-7567","contributorId":196257,"corporation":false,"usgs":true,"family":"McCormick","given":"Molly","email":"mmccormick@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":826840,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224270,"text":"sir20215075 - 2021 - Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","interactions":[],"lastModifiedDate":"2021-09-20T14:34:44.807541","indexId":"sir20215075","displayToPublicDate":"2021-09-20T06:57:11","publicationYear":"2021","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":"2021-5075","displayTitle":"Development of a Screening Tool To Examine Lake and Reservoir Susceptibility to Eutrophication in Selected Watersheds of the Eastern and Southeastern United States","title":"Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","docAbstract":"This report describes a new screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States using estimated nutrient loading and flushing rates with measures of waterbody morphometry. To that end, the report documents the compiled data and methods (R-script) used to categorize waterbodies by Carlson’s Trophic State Index. Assessments were completed for 232 lakes and reservoirs having a surface area greater than or equal to 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Waterbodies were categorized by type—natural lakes, headwater reservoirs, and downstream reservoirs—and were assessed independently. Recursive partitioning and the model-based boosting routine were used to create four-node regression trees to group waterbodies into five endpoints from low-to-high measures of Secchi depth, and concentrations of chlorophyll a and microcystin according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned based on the average value within each of the five endpoints. An application (procedure) is provided using the tool to examine the susceptibility of a given waterbody of interest to eutrophication. Results of this study can aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication relative to nutrient loading, flushing rate, and morphometric characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215075","usgsCitation":"Green, W.R., Hoos, A.B., Wilson, A.E., and Heal, E.N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5075, 59 p., https://doi.org/10.3133/sir20215075.","productDescription":"Report: vi, 59 p.; Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-097274","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5075/coverthb.jpg"},{"id":389349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5075/sir20215075.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5075"},{"id":389350,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7EOH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States"}],"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 -73.916015625,\n 40.51379915504413\n ],\n [\n -73.71826171874999,\n 40.896905775860006\n ],\n [\n -73.05908203125,\n 41.19518982948959\n ],\n [\n -71.60888671875,\n 41.343824581185686\n ],\n [\n -70.48828125,\n 41.21172151054787\n ],\n [\n -69.7412109375,\n 41.83682786072714\n ],\n [\n -70.48828125,\n 42.69858589169842\n ],\n [\n -70.09277343749999,\n 43.54854811091286\n ],\n [\n -67.1044921875,\n 44.574817404670306\n ],\n [\n -66.884765625,\n 44.824708282300236\n ],\n [\n -67.236328125,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.259422036351694\n ],\n [\n -67.39013671875,\n 45.583289756006316\n ],\n [\n -67.8515625,\n 45.706179285330855\n ],\n [\n -67.87353515625,\n 47.15984001304432\n ],\n [\n -68.22509765625,\n 47.338822694822\n ],\n [\n -68.90625,\n 47.234489635299184\n ],\n [\n -69.08203125,\n 47.45780853075031\n ],\n [\n -69.2138671875,\n 47.487513008956554\n ],\n [\n -70.048828125,\n 46.694667307773116\n ],\n [\n -70.224609375,\n 46.17983040759436\n ],\n [\n -70.55419921875,\n 45.66012730272194\n ],\n [\n -70.90576171875,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.321254361171476\n ],\n [\n -71.47705078125,\n 44.99588261816546\n ],\n [\n -74.86083984375,\n 45.01141864227728\n ],\n [\n -75.73974609375,\n 44.5435052132082\n ],\n [\n -74.8828125,\n 44.18220395771566\n ],\n [\n -74.33349609375,\n 43.91372326852401\n ],\n [\n -75.12451171875,\n 43.46886761482925\n ],\n [\n -75.9375,\n 43.29320031385282\n ],\n [\n -76.88232421875,\n 42.4234565179383\n ],\n [\n -77.431640625,\n 42.65012181368022\n ],\n [\n -77.9150390625,\n 42.22851735620852\n ],\n [\n -77.76123046875,\n 41.902277040963696\n ],\n [\n -78.57421875,\n 40.64730356252251\n ],\n [\n -79.4970703125,\n 39.791654835253425\n ],\n [\n -79.95849609375,\n 38.013476231041935\n ],\n [\n -80.52978515625,\n 37.24782120155428\n ],\n [\n -81.40869140625,\n 35.37113502280101\n ],\n [\n -83.43017578125,\n 35.35321610123823\n ],\n [\n -85.10009765625,\n 35.38904996691167\n ],\n [\n -86.90185546874999,\n 35.38904996691167\n ],\n [\n -87.802734375,\n 35.17380831799959\n ],\n [\n -89.07714843749999,\n 34.63320791137959\n ],\n [\n -89.05517578125,\n 33.37641235124676\n ],\n [\n -88.87939453125,\n 32.80574473290688\n ],\n [\n -89.8681640625,\n 31.85889704445453\n ],\n [\n -89.93408203124999,\n 31.316101383495624\n ],\n [\n -89.736328125,\n 30.694611546632277\n ],\n [\n -89.45068359374999,\n 30.14512718337613\n ],\n [\n -86.8359375,\n 30.088107753367257\n ],\n [\n -85.62744140625,\n 29.935895213372444\n ],\n [\n -84.9462890625,\n 29.401319510041485\n ],\n [\n -84.287109375,\n 29.878755346037977\n ],\n [\n -82.85888671875,\n 29.036960648558267\n ],\n [\n -83.0126953125,\n 27.97499795326776\n ],\n [\n -81.5625,\n 25.403584973186703\n ],\n [\n -80.88134765625,\n 24.607069137709683\n ],\n [\n -79.89257812499999,\n 25.005972656239187\n ],\n [\n -79.5849609375,\n 26.92206991673282\n ],\n [\n -81.2109375,\n 30.939924331023445\n ],\n [\n -77.431640625,\n 34.21634468843463\n ],\n [\n -75.65185546874999,\n 34.97600151317588\n ],\n [\n -75.60791015625,\n 37.142803443716836\n ],\n [\n -75.12451171875,\n 38.35888785866677\n ],\n [\n -73.80615234375,\n 39.99395569397331\n ],\n [\n -73.916015625,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.
Louisiana contains nearly 40% of estuarine herbaceous wetlands in the contiguous United States, supporting valuable ecosystem services and providing significant economic benefits to the state and the entire United States. However, coastal Louisiana is a hotspot for rapid land loss from factors including hurricanes, land use change, and high subsidence rates contributing to high relative sea-level rise. The Coastal Protection and Restoration Authority (CPRA) was established after major hurricanes in 2005 to coordinate coastal restoration in Louisiana and develop the Louisiana Coastal Master Plan. The LA Coastal Master Plan uses numerical modeling of multiple scenarios to select a suite of restoration projects based on maximum land area created and flood reduction (as proxies for ecosystem value). Using potential value to aquatic, terrestrial, and social resources, our work compared habitat value of shallow open water areas to emergent wetland. While potential resource benefits varied by emergent wetland salinity type and emergent wetland versus water, they were similar, suggesting that restoration planning based primarily on wetland land area may not achieve the maximum possible ecosystem benefits. After nearly 20 years of integrated restoration planning in coastal Louisiana, a reassessment of restoration planning decision drivers may be beneficial to ensure maximum benefits from coastal restoration. As a result of the Deepwater Horizon oil spill, settlement funds will be a major support to coastal restoration in Louisiana for many years. Assessing potential habitat value to multiple natural and social resources in Louisiana has potential to maximize synergy with large northern Gulf of Mexico restoration programs.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13564","usgsCitation":"Carruthers, T., Kiskaddon, E.P., Baustian, M., Darnell, K.M., Moss, L.C., Perry, C.L., and Stagg, C., 2021, Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?: Restoration Ecology, v. 30, e13564, 8 p., https://doi.org/10.1111/rec.13564.","productDescription":"e13564, 8 p.","ipdsId":"IP-074120","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13564","text":"Publisher Index Page"},{"id":395227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.922119140625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 28.94086176940557\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":832364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiskaddon, Erin P.","contributorId":272886,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"Erin","email":"","middleInitial":"P.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa M.","contributorId":189569,"corporation":false,"usgs":false,"family":"Baustian","given":"Melissa M.","affiliations":[],"preferred":false,"id":832366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":832367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Leland C.","contributorId":272644,"corporation":false,"usgs":false,"family":"Moss","given":"Leland","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832368,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Carey L.","contributorId":189570,"corporation":false,"usgs":false,"family":"Perry","given":"Carey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":832369,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":832370,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229723,"text":"70229723 - 2021 - Fishing gear performance nearshore is substantiated by spatial analyses","interactions":[],"lastModifiedDate":"2022-03-16T15:31:45.435187","indexId":"70229723","displayToPublicDate":"2021-09-18T10:17:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Fishing gear performance nearshore is substantiated by spatial analyses","docAbstract":"We estimated whether the fish assemblages nearshore represented by electrofishing and gillnetting indexed location of reservoirs in a river basin. We expected that location in the basin would reflect a multiplicity of factors that determine fish habitat and fish assemblage composition, and therefore also anticipated a correlation between fish species composition and spatial variables if the gear type reflected legitimate differences in fish assemblages. We collected 1.6 million fish of 129 species in 22 reservoirs of the Tennessee River basin, USA. Standardized electrofishing represented different aspects of the fish assemblages than standardized gillnetting. Nevertheless, the assemblages documented by each gear type were correlated with the spatial location of the reservoirs in the river basin. Thus, even as these gear types reflected different aspects of existing fish assemblages, they each tracked spatial differences, suggesting that they reflected standing fish assemblages. Our study supports the use of standardized boat electrofishing and gillnetting as proper means for monitoring fish assemblages at large spatial scales. Our results further suggest that a well-designed and standardized sampling protocol can in fact provide an informative bird’s eye view of fish assemblages at regional, national, or continental scales suitable for informing conservation programs.
","language":"English","publisher":"Springer","doi":"10.1007/s11160-021-09683-7","usgsCitation":"Miranda, L.E., Faucheux, N.M., and Lakin, K.M., 2021, Fishing gear performance nearshore is substantiated by spatial analyses: Reviews in Fish Biology and Fisheries, v. 31, p. 977-987, https://doi.org/10.1007/s11160-021-09683-7.","productDescription":"11 p.","startPage":"977","endPage":"987","ipdsId":"IP-130241","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee, Virginia","otherGeospatial":"Tennessee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.43994140625,\n 37.28279464911045\n ],\n [\n -88.74755859375,\n 34.97600151317588\n ],\n [\n -88.17626953125,\n 33.94335994657882\n ],\n [\n -85.45166015624999,\n 34.07086232376631\n ],\n [\n -82.880859375,\n 34.45221847282654\n ],\n [\n -83.0126953125,\n 35.04798673426734\n ],\n [\n -82.37548828125,\n 35.191766965947394\n ],\n [\n -81.93603515625,\n 35.191766965947394\n ],\n [\n -81.62841796875,\n 37.21283151445594\n ],\n [\n -82.02392578125,\n 37.49229399862877\n ],\n [\n -82.37548828125,\n 37.71859032558816\n ],\n [\n -84.9462890625,\n 36.914764288955936\n ],\n [\n -85.49560546875,\n 35.60371874069731\n ],\n [\n -87.62695312499999,\n 37.28279464911045\n ],\n [\n -88.43994140625,\n 37.28279464911045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2021-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faucheux, Nicky M.","contributorId":271194,"corporation":false,"usgs":false,"family":"Faucheux","given":"Nicky","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":838099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lakin, Kurt M.","contributorId":288574,"corporation":false,"usgs":false,"family":"Lakin","given":"Kurt","email":"","middleInitial":"M.","affiliations":[{"id":61800,"text":"Tennessee Valley Authority, Chattanooga","active":true,"usgs":false}],"preferred":false,"id":838100,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225531,"text":"70225531 - 2021 - Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","interactions":[],"lastModifiedDate":"2021-12-10T17:01:34.289166","indexId":"70225531","displayToPublicDate":"2021-09-18T08:12:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","docAbstract":"Groundwater total dissolved solids (TDS) distribution was mapped with a three-dimensional (3D) model, and it was found that TDS variability is largely controlled by stratigraphy and geologic structure. General TDS patterns in the San Joaquin Valley of California (USA) are attributed to predominantly connate water composition and large-scale recharge from the adjacent Sierra Nevada. However, in smaller areas, stratigraphy and faulting play an important role in controlling TDS. Here, the relationship of stratigraphy and structure to TDS concentration was examined at Poso Creek Oil Field, Kern County, California. The TDS model was constructed using produced water TDS samples and borehole geophysics. The model was used to predict TDS concentration at discrete locations in 3D space and used a Gaussian process to interpolate TDS over a volume. In the overlying aquifer, TDS is typically <1,000 mg/L and increases with depth to ~1,200–3,500 mg/L in the hydrocarbon zone below the Macoma claystone—a regionally extensive, fine-grained unit—and reaches ~7,000 mg/L in isolated places. The Macoma claystone creates a vertical TDS gradient in the west where it is thickest, but control decreases to the east where it pinches out and allows freshwater recharge. Previously mapped normal faults were found to exhibit inconsistent control on TDS. In one case, high-density faulting appears to prevent recharge from flushing higher-TDS connate water. Elsewhere, the high-throw segments of a normal fault exhibit variable behavior, in places blocking lower-TDS recharge and in other cases allowing flushing. Importantly, faults apparently have differential control on oil and groundwater.
Changes in winter conditions, such as decreased ice coverage and duration, have been observed in the Laurentian Great Lakes for more than 20 years. Such changes have been hypothesized to be linked to low Coregonus spp. survival to age-1 as most cisco (Coregonus artedi) populations are autumn spawners whose embryos incubate under ice throughout the winter. The quantity of light during winter is regulated by ice coverage, and light affects embryo survival and development in some teleosts. We experimentally evaluated how cisco embryos from lakes Superior and Ontario respond to three light treatments that represented day-light intensity under 0–10, 40–60, and 90–100% ice coverage. Embryonic response measures included two developmental factors (embryo survival and incubation period) and two morphological traits (length-at-hatch and yolk-sac volume). Embryo survival was highest at the medium light treatment and decreased at high and low treatments for both populations, suggesting cisco may be adapted to withstand some light exposure from inter-annual variability in ice coverage. Light intensity had no overall effect on length of incubation. Increasing light intensity decreased length-at-hatch in Lake Superior but had no effect in Lake Ontario. Yolk-sac volume was positively correlated with increasing light in Lake Superior and negatively correlated in Lake Ontario. Contrasting responses in embryo development between lakes suggests differences in populations’ response to light is flexible. Our results provide a step towards better understanding the high variability observed in coregonine recruitment and may help predict what the future of this species may look like under current climate trends.
In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km2 with most locations having abundances between 20,000 to 50,000 particles/km2. The average abundance in Lake Superior was ~30,000 particles/km2 which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.
Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.
The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.
This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.
Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.
The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.
Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.
Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.
The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.
More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.
The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Since 2011, seafloor temperatures, pressures, and seismic ground motions have been measured by the seafloor cabled Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) on the Nankai margin. These measurements, high-resolution bathymetry, and abundant contextual information make the DONET region seem ideally suited to provide constraints on seismic shaking-triggered sediment slope failures and gravity flows, particularly since numerous published studies have linked paleo- to modern earthquakes to failures and flows within the DONET. The occurrences of the local 2016 M6.0 Mie-ken and regional M7.0 Kumamoto earthquakes within and at regional distances, respectively, from the DONET data set provided an opportunity to explore this potential. We used DONET seismic recordings of the posited triggering shaking and to search for submarine slide signals and continuous temperature and pressure data to detect pulses of warm and densified water indicative of passing flows. We developed and applied a variety of analytical methods to eliminate signals generated by water column processes, while leaving slope failures and sediment gravity flow anomalies as residuals. Our explorations yielded no evidence that earthquake shaking initiated either phenomenon, which we suggest reflects the finicky nature both of the detection of and the physical processes that contribute to slope failures and flows (i.e., both require satisfying precise suites of conditions). Nonetheless, this negative result, our analyses, and the estimates of physical properties we derived for them, provide useful lessons and inputs for future studies.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB022588","usgsCitation":"Gomberg, J.S., Ariyoshi, K., Hautala, S., and Johnson, H., 2021, The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows: Journal of Geophysical Research, v. 126, e2021JB022588, 26 p., https://doi.org/10.1029/2021JB022588.","productDescription":"e2021JB022588, 26 p.","ipdsId":"IP-123242","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 135.61973524947484,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 34.29410264461973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":924643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, H.P.","contributorId":349727,"corporation":false,"usgs":false,"family":"Johnson","given":"H.P.","affiliations":[],"preferred":false,"id":924644,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224538,"text":"70224538 - 2021 - Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","interactions":[],"lastModifiedDate":"2022-01-24T16:16:48.709305","indexId":"70224538","displayToPublicDate":"2021-09-17T10:11:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5020,"text":"Microorganisms","active":true,"publicationSubtype":{"id":10}},"title":"Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","docAbstract":"Freshwater mussels (Unionida) are suffering mass mortality events worldwide, but the causes remain enigmatic. Here, we describe an analysis of bacterial loads, community structure, and inferred metabolic pathways in the hemolymph of pheasantshells (Actinonaias pectorosa) from the Clinch River, USA, during a multi-year mass mortality event. Bacterial loads were approximately 2 logs higher in moribund mussels (cases) than in apparently healthy mussels (controls). Bacterial communities also differed between cases and controls, with fewer sequence variants (SVs) and higher relative abundances of the proteobacteria Yokenella regensburgei and Aeromonas salmonicida in cases than in controls. Inferred bacterial metabolic pathways demonstrated a predominance of degradation, utilization, and assimilation pathways in cases and a predominance of biosynthesis pathways in controls. Only two SVs correlated with Clinch densovirus 1, a virus previously shown to be strongly associated with mortality in this system: Deinococcota and Actinobacteriota, which were associated with densovirus-positive and densovirus-negative mussels, respectively. Overall, our results suggest that bacterial invasion and shifts in the bacterial microbiome during unionid mass mortality events may result from primary insults such as viral infection or environmental stressors. If so, bacterial communities in mussel hemolymph may be sensitive, if generalized, indicators of declining mussel health.
","language":"English","publisher":"MDPI","doi":"10.3390/microorganisms9091976","usgsCitation":"Richard, J., Campbell, L., Leis, E., Agbalog, R., Dunn, C.D., Waller, D.L., Knowles, S., Putnam, J.G., and Goldberg, T., 2021, Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve: Microorganisms, v. 9, no. 9, 1976, 15 p., https://doi.org/10.3390/microorganisms9091976.","productDescription":"1976, 15 p.","ipdsId":"IP-131640","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":450779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/microorganisms9091976","text":"Publisher Index Page"},{"id":389816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee, Virginia","otherGeospatial":"Clinch River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.2489013671875,\n 36.416862115300304\n ],\n [\n -81.485595703125,\n 37.413800350662896\n ],\n [\n -82.177734375,\n 37.71859032558816\n ],\n [\n -84.44091796875,\n 36.43012234551576\n ],\n [\n -83.2489013671875,\n 36.416862115300304\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richard, Jordan","contributorId":211789,"corporation":false,"usgs":false,"family":"Richard","given":"Jordan","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":823973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Lewis J. 0000-0002-7852-2250","orcid":"https://orcid.org/0000-0002-7852-2250","contributorId":244773,"corporation":false,"usgs":false,"family":"Campbell","given":"Lewis J.","affiliations":[],"preferred":false,"id":823974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Eric","contributorId":179325,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","affiliations":[],"preferred":false,"id":823975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agbalog, Rose","contributorId":239870,"corporation":false,"usgs":false,"family":"Agbalog","given":"Rose","affiliations":[{"id":48017,"text":"USFWS-Virginia Field Office","active":true,"usgs":false}],"preferred":false,"id":823976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Christopher D.","contributorId":225521,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":41155,"text":"Department of Pathobiological Sciences, University of Wisconsin-Madison,","active":true,"usgs":false}],"preferred":false,"id":823977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":823979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Putnam, Joel G. 0000-0002-5464-4587 jgputnam@usgs.gov","orcid":"https://orcid.org/0000-0002-5464-4587","contributorId":5783,"corporation":false,"usgs":true,"family":"Putnam","given":"Joel","email":"jgputnam@usgs.gov","middleInitial":"G.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823980,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goldberg, Tony","contributorId":211788,"corporation":false,"usgs":false,"family":"Goldberg","given":"Tony","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":823981,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255182,"text":"70255182 - 2021 - Mechanistic invasive species management models and their application in conservation","interactions":[],"lastModifiedDate":"2024-06-13T13:39:08.914783","indexId":"70255182","displayToPublicDate":"2021-09-17T08:36:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Mechanistic invasive species management models and their application in conservation","docAbstract":"Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.533","usgsCitation":"Thompson, B.K., Olden, J., and Converse, S.J., 2021, Mechanistic invasive species management models and their application in conservation: Conservation Science and Practice, v. 3, no. 11, e533, 18 p., https://doi.org/10.1111/csp2.533.","productDescription":"e533, 18 p.","ipdsId":"IP-126294","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.533","text":"Publisher Index Page"},{"id":430128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":338912,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle","email":"","middleInitial":"K.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olden, Julian D.","contributorId":338914,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903683,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224582,"text":"70224582 - 2021 - Evaluation of SWIR crop residue bands for the Landsat Next mission","interactions":[],"lastModifiedDate":"2021-09-29T13:25:56.428904","indexId":"70224582","displayToPublicDate":"2021-09-17T08:22:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of SWIR crop residue bands for the Landsat Next mission","docAbstract":"Natural and cultural resource managers are increasingly working with the scientific community to create information on how best to adapt to the current and projected impacts of climate change. Engaging with these managers is a strategy that researchers can use to ensure that scientific outputs and findings are actionable (or useful and usable). In this article, the authors adapt Davidson’s wheel of participation to characterize and describe common stakeholder engagement strategies across the spectrum of Inform, Consult, Participate, and Empower. This adapted framework provides researchers with a standardized vocabulary for describing their engagement approach, guidance on how to select an approach, methods for implementing engagement, and potential barriers to overcome. While there is often no one “best” approach to engaging with stakeholders, researchers can use the objectives of their project and the decision context in which their stakeholders operate to guide their selection. Researchers can also revisit this framework over time as their project objectives shift and their stakeholder relationships evolve.
Global mean sea level (GMSL) has been rising since the last century, posing a serious challenge for the coastal areas. A variety of regression models have been utilized for determining GMSL rise over the past one hundred years, resulting in a large spread of sea level rise rates and multidecadal variations. In this study, we develop a new nonparametric noise model that is data-dependent and considers overfitting due to regression. The noise model is used to determine whether one regression model has significantly better skill than others over the period 1900–2010. The choices of background noise and GMSL reconstruction influence whether two sea level models can be statistically distinguished. With our new nonparametric noise spectra, the differences of model skills in explaining sea level variance are significant only in 34% of model comparisons. However, stepwise trends with three inflection points are significantly more skillful than the linear, quadratic, or exponential trend for most GMSL reconstructions, suggesting the importance of multidecadal variability of sea level rise in the twentieth century. Nevertheless, stepwise trend models cannot be distinguished from models with a long-term harmonic oscillation, indicating that the shape of multidecadal variability is not conclusive. The multidecadal variability is also significant in the steric and barystatic sea level contributions and is related to both natural and anthropogenic forcings. GMSL predictions based on regression fits in the twentieth century underestimate the sea level rise rate over the period 2011–2020 because the sea level acceleration in the recent decade (2011–2020) is not well represented.
Forest structure and topography can influence the ecohydrologic function and resiliency to drought and changing climate. It is, therefore, important to understand how forest restoration treatments alter snowpack distribution and design the treatments accordingly. We use a combination of aerial lidar, multi-temporal terrestrial mobile lidar, and UAV photogrammetry to estimate rapidly changing snow depth and cover in northern Arizona, USA. We then examine the impact of forest structure and topography on snow depth and snow cover persistence to inform forest restoration treatments. Our results show that mobile lidar data can be used to estimate snow depth with standard errors of 8 cm when differenced with snow-off airborne lidar data. UAV-based Structure-from-Motion data can be used to estimate snow cover persistence with 92–97% overall accuracies in forested ecosystems. Random forest models indicate spatially varying importance of forest structural and topographic variables in predicting snow depth and cover persistence, when summarized at different spatial scales (from 5 m to 250 m) and with variable directional location offsets. Forest snow depth was best explained (R2 ≈ 0.46) by canopy height metrics at summary scales of >75 m, while canopy cover was most important at summary scales of <40 m (R2 ≈ 0.3). Snow cover persistence was best explained at very local scales by canopy cover (R2 ≈ 0.38) and less so at larger scales (>75 m) by topographic and forest patch characteristics (R2 ≈ 0.34). Our results demonstrate that 3-dimensional datasets are critical in rapidly characterizing changing snowpack to better understand the impacts of forest structure and topography to inform forest restoration treatment designs. The relationships observed in our study can inform currently ongoing regional-scale forest restoration in the southwest to improve forest health and resiliency.
Assessing the long-term efficacy of control methods is a critical component of invasive species management. For example, if traits related to control have significant heritability or are influenced by maternal effects, control methods may lose efficacy over time. The potential for these effects can be evaluated via parent/offspring survival analysis, which concomitantly recasts adaptive management as an evolutionary force for invasive species. However, difficulties can arise when the life history of an invasive is cryptic, precluding direct observations of familial relationships. Genomic pedigree reconstruction can facilitate such analyses by assigning offspring to parents in invasive species for which mating and reproduction are difficult to study. Here, we use genomic pedigree reconstruction to quantify parental longevity and probability of offspring survival for brown treesnakes (Boiga irregularis) on Guam in a landscape treated with toxic baits simulating application via an aerial delivery system (ADS). To do so, we used 398 single nucleotide polymorphisms (SNPs) to update an existing multi-generational genomic pedigree for a geographically-closed population of brown treesnakes. This facilitated assignment of parents to juveniles born during three consecutive years of toxic bait application under a simulated aerial treatment program (N = 72). We found that the offspring of dams that persisted until the end of the study displayed greater survival probability (cox proportional hazard model, P < 0.001), yet there was no such effect for sires. This sex-specific relationship between parental longevity and offspring survival indicates that heritability of traits contributing to resistance to ADS is unlikely, but it supports a role of maternal effects that could undermine ADS. Our study identifies potential risks associated with control efforts and also highlights the utility of parent-offspring survival analyses informed by genomic pedigree reconstruction as a tool for adaptive management.