The Rio Grande, in southern Albuquerque, New Mexico, is a Clean Water Act Section 303(d) Category 5 impaired reach for Escherichia coli (E. coli). The reach is 5 miles in length, extending from Tijeras Arroyo south to the Isleta Pueblo boundary. An evaluation of E. coli and microbial source tracking markers (human-, canine-, and waterfowl-specific sources) was conducted by the U.S. Geological Survey to determine the extent and source of fecal bacteria within the impaired reach of the Rio Grande, primarily during the dry season (November through June) in 2020 and 2021. Samples were collected in the river cross section at three locations within each site and collected during both the dry season and the wet season, thereby providing data over a range of flow conditions to better understand the extent and source of fecal bacteria. Because fecal microorganisms may readily attach to sediments, riverbed material samples were also collected. During the dry season, E. coli concentrations in water were primarily detected below the New Mexico Surface Water Quality Standard of 410 colony forming units per 100 milliliters and mostly human and canine sources were detected. However, approximately 40 percent of the water samples exceeded the Isleta Pueblo water quality standard of 88 colony forming units per 100 milliliters. E. coli concentrations in bed material were detected at low concentrations, and the bed material was a sandy substrate, with little fine-grained material, a suitable habitat that would allow for bacterial growth during the dry season. Significant spatial and temporal differences, where p-values were less than 0.05, were found in water-quality samples for E. coli (seasonal) and the human tracker concentrations (between sites and within a cross section of a site). Given the lack of correlation between discharge and E. coli concentration and the human marker being most prevalent in the study area, the sources of E. coli in the dry season are likely nonpoint sources. The results from this study will help decision makers determine the efficacy of their best management practices and guide new practices to improve water quality in the reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235019","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Travis, R.E., Wilkins, K.L., and Kephart, C.M., 2023, Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21: U.S. Geological Survey Scientific Investigations Report 2023–5019, 48 p., https://doi.org/10.3133/sir20235019.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-139896","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":414732,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.XML","size":"328 KB","linkFileType":{"id":8,"text":"xml"}},{"id":414632,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q2ECYV","text":"U.S. Geological Survey data release—Fecal bacteria and microbial source tracking marker data in the Rio Grande, Albuquerque, New Mexico 2017–2020"},{"id":414629,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5019/images"},{"id":414627,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5019/coverthb.jpg"},{"id":414626,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5019"},{"id":500713,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114616.htm","linkFileType":{"id":5,"text":"html"}},{"id":414733,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235019/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande, South Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 35.5\n ],\n [\n -107,\n 34.75\n ],\n [\n -106.25,\n 34.75\n ],\n [\n -106.25,\n 35.5\n ],\n [\n -107,\n 35.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd. NE
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Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.
This report reflects on the past, present, and potential future of community and citizen science (CCS) in the Elwha River watershed, with particular focus on the years before and after a major restoration event: the removal of two dams that had impacted the river system for a century. We ask: how does CCS feature in the Elwha story and how could it feature? We use the term CCS to reference the broad range of ways in which members of the public might participate in authentic science and monitoring processes, including students and both paid and unpaid interns: participants are individuals contributing to scientific projects without prior formal training in the topic.
Removal of the Elwha dams was a large-scale, complex project, and communities had an important role to play: the Lower Elwha Klallam Tribe (LEKT) and other local groups were a large part of the original drive to remove the dams. Some funding and policy requirements for monitoring are ending, but there is still much to learn from the changes happening in the Elwha, requiring ongoing research and monitoring. In 2022, the Elwha scientific community came together in a multifaceted effort called the “Elwha ScienceScape” to mark the ten-year anniversary of dam removal and to plan for future monitoring. One of ScienceScape’s priorities is expanding CCS efforts, and because the Elwha dam removal is a powerful international symbol of large-scale watershed restoration, ScienceScape is well-positioned to inform and emphasize the potential role of CCS in dam removal worldwide.
This report presents insights about Elwha CCS from an academic literature review and discussions with scientists and many others that have been working in the Elwha. We found that the history of CCS on the Elwha is important but understated, with few scientific papers acknowledging support by volunteers of various kinds. Recent and ongoing CCS projects on the Elwha tend to be focused on biological phenomena, and most are associated with educational opportunities (across many types of institutions) and paid internships. We also noted that most Elwha CCS projects required volunteers with particular pre-existing skill sets (e.g., botanical knowledge) or time to impart specialized training (e.g. boat use), leading many projects towards engagement with a smaller number of volunteers.
Partners working in the Elwha are considering a wide range of potential new CCS projects, and these ideas are in varying stages of development. Many new projects would broaden public involvement in terms of the opportunities available and increase the variety of focal topics for research and monitoring. This increased breadth is promising: there are indications that the local community’s interests also range widely, from fish recovery after dam removal to dam removal impacts on humans.
Elwha CCS projects have encountered some challenges and barriers, including the administrative burden of coordinating volunteers and managing liability concerns. But Elwha ScienceScape scientists are committed to the value that CCS brings both to the research itself as well as to those who participate in these projects. CCS can be a way to increase equity in science and engage people who would not otherwise participate in research, and in many cases the research simply wouldn’t be possible without their help. Support with project administration, volunteer management, and data management could help in expanding CCS efforts and broadening their inclusivity. More systematic tracking of CCS projects to assess how they contribute to research and to community and participant benefit could be helpful in establishing and maintaining a long-term CCS strategy in the Elwha.
","language":"English","publisher":"UC Davis Center for Community and Citizen Science","doi":"10.58076/C64W2B","usgsCitation":"Eitzel, M.V., Morley, S.A., Behymer, C., Meyer, R., Kagley, A., Ballard, H.L., Jadallah, C., Duda, J.J., Jennings, L., Miller, I.M., Stapleton, J., Shaffer, A., Miller, A., Shafroth, P., and Blackie, B., 2023, Community and citizen science on the Elwha River: Past, present, and future, 22 p., https://doi.org/10.58076/C64W2B.","productDescription":"22 p.","ipdsId":"IP-149069","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":414976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wshington","otherGeospatial":"Elwha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.62525330687644,\n 47.71956268467267\n ],\n [\n -123.42458708492356,\n 47.71956268467267\n ],\n [\n -123.42458708492356,\n 48.15187321706955\n ],\n [\n -123.62525330687644,\n 48.15187321706955\n ],\n [\n -123.62525330687644,\n 47.71956268467267\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eitzel, M. 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Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":868097,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shaffer, Anne","contributorId":168504,"corporation":false,"usgs":false,"family":"Shaffer","given":"Anne","email":"","affiliations":[],"preferred":false,"id":868098,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Miller, Allyce","contributorId":303794,"corporation":false,"usgs":false,"family":"Miller","given":"Allyce","email":"","affiliations":[{"id":65909,"text":"Lower Elwha Klallam Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":868099,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science 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Stickleback Culaea inconstans minimally alters the trophic ecology of four native fishes in Wyoming, USA","title":"Invasive Brook Stickleback Culaea inconstans minimally alters the trophic ecology of four native fishes in Wyoming, USA","docAbstract":"Invasive species introductions are a primary threat facing populations of native freshwater fishes. There are multiple mechanisms by which an invader can affect native species, with competition for food resources being one mechanism that can lead to declines in the distribution and abundance of native species. Invaders that are trophic generalists may cause shifts in the trophic ecology of native species and may be better suited for long-term persistence amid environmental stochasticity. Therefore, trophic studies can provide valuable information on the risk an invader poses to native species. Brook Stickleback Culaea inconstans is an invasive fish species in Wyoming whose effect on native fish assemblages is poorly understood. Our goal was to understand the potential for competitive interactions between Brook Stickleback and native fishes. We used stable isotopes of carbon (ẟ13C) and nitrogen (ẟ15N) to evaluate the feeding ecology of Brook Stickleback relative to four native fishes, and to explore whether native fish isotopic niches changed in sympatry with Brook Stickleback. We hypothesized that the isotopic niche of Brook Stickleback would be larger than that of native fishes, suggesting broader resource use. Additionally, we hypothesized that the isotopic niche of native fish populations sympatric with Brook Stickleback would contract. We did not find support for our hypotheses as the isotopic niche of Brook Stickleback was not substantially different from that of native fishes. Further, the isotopic niche of native fishes was not substantially affected by Brook Stickleback presence. As a result, we do not currently see evidence of Brook Stickleback altering the trophic ecology of native fish species. Our results provide insight to the effects of a small-bodied invasive fish species on native fishes in a previously unstudied region, and can help managers prioritize management actions to conserve native fishes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2023.e00275","usgsCitation":"Ruthvena, J.S., and Walters, A.W., 2023, Invasive Brook Stickleback Culaea inconstans minimally alters the trophic ecology of four native fishes in Wyoming, USA: Food Webs, v. 35, e00275, 9 p., https://doi.org/10.1016/j.fooweb.2023.e00275.","productDescription":"e00275, 9 p.","ipdsId":"IP-143172","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.09999209466312,\n 44.992832791258934\n ],\n [\n -111.09999209466312,\n 40.9969502976852\n ],\n [\n -104.06062494053327,\n 40.9969502976852\n ],\n [\n -104.06062494053327,\n 44.992832791258934\n ],\n [\n -111.09999209466312,\n 44.992832791258934\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ruthvena, Jacob S.","contributorId":338255,"corporation":false,"usgs":false,"family":"Ruthvena","given":"Jacob","email":"","middleInitial":"S.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903069,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243935,"text":"70243935 - 2023 - Further bacteriological analysis of annual Pheasantshell (Actinonaias pectorosa) mussel mortality events in the Clinch River (Virginia/Tennessee), USA, reveals a consistent association with Yokenella Regensburgei","interactions":[],"lastModifiedDate":"2023-05-25T15:09:35.233941","indexId":"70243935","displayToPublicDate":"2023-03-23T10:01:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Further bacteriological analysis of annual Pheasantshell (Actinonaias pectorosa) mussel mortality events in the Clinch River (Virginia/Tennessee), USA, reveals a consistent association with Yokenella Regensburgei","title":"Further bacteriological analysis of annual Pheasantshell (Actinonaias pectorosa) mussel mortality events in the Clinch River (Virginia/Tennessee), USA, reveals a consistent association with Yokenella Regensburgei","docAbstract":"Pheasantshell (Actinonaias pectorosa) mussels in the Clinch River (Tennessee/Virginia, USA) have declined dramatically in recent years. The bacterium Yokenella regensburgei was first isolated with high prevalence from Pheasantshells during the peak of a 2017 mortality event, but it was not identified after mortality subsided a few months later. Since 2017, Pheasantshell mortality in the Clinch River has occurred each autumn. We extended the investigation of culturable bacterial communities in the Clinch River during mussel mortality events in 2018, 2019, and 2020 and examined the spatial and temporal distribution of bacterial genera among Pheasantshells, as well as among other unionid mussels. We identified Y. regensburgei each year, almost exclusively during active mortality events. The significance of Y. regensburgei remains unclear, but the continued association of this bacterium with mussel mortality events warrants further study.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc-d-22-00001","usgsCitation":"Leis, E., Dziki, S., Richard, J., Agbalog, R., Waller, D.L., Putnam, J.G., Knowles, S., and Goldberg, T., 2023, Further bacteriological analysis of annual Pheasantshell (Actinonaias pectorosa) mussel mortality events in the Clinch River (Virginia/Tennessee), USA, reveals a consistent association with Yokenella Regensburgei: Freshwater Mollusk Biology and Conservation, v. 26, no. 1, p. 1-10, https://doi.org/10.31931/fmbc-d-22-00001.","productDescription":"10 p.","startPage":"1","endPage":"10","ipdsId":"IP-134398","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":444102,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc-d-22-00001","text":"Publisher Index Page"},{"id":435406,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SARYP3","text":"USGS data release","linkHelpText":"Bacteria identified in freshwater mussels in the Clinch River, VA, associated with mortality events from 2018 to 2020"},{"id":417438,"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 \"coordinates\": [\n [\n [\n -83.2506164916795,\n 37.10370776426737\n ],\n [\n -83.2506164916795,\n 36.46184748703568\n ],\n [\n -81.82888619270031,\n 36.46184748703568\n ],\n [\n -81.82888619270031,\n 37.10370776426737\n ],\n [\n -83.2506164916795,\n 37.10370776426737\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Leis, Eric","contributorId":179325,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","affiliations":[],"preferred":false,"id":873792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dziki, Sara","contributorId":305502,"corporation":false,"usgs":false,"family":"Dziki","given":"Sara","email":"","affiliations":[{"id":66232,"text":"La Crosse Fish Health Center – Midwest Fisheries Center, U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":873794,"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":873795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":873796,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":873797,"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":873798,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":873799,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249759,"text":"70249759 - 2023 - A plea for Red Wolf conservation throughout Its recent distribution","interactions":[],"lastModifiedDate":"2023-10-26T12:23:17.042485","indexId":"70249759","displayToPublicDate":"2023-03-23T07:22:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17080,"text":"Southesatern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"A plea for Red Wolf conservation throughout Its recent distribution","docAbstract":"Canis rufus (Red Wolf) is one of the most endangered mammals in North America. However, genes of the Red Wolf persist across much of the species' original range, carried predominantly within C. latrans (Coyote) populations. It is now known that such genes are distributed from extreme north-central Texas through most of eastern Texas to southern Louisiana. Publicizing of the most recent findings of Red Wolf genes in Coyotes of southern Louisiana emphasized that area for intensive conservation efforts. Such efforts could be applied throughout the entire known distribution of those rare genes, not just in the small area of southern Louisiana recently publicized. Because conservation efforts might be hindered by local conditions and circumstances, expanding geographic options for their application could make the difference in their success.
Selecting livestock genetics adapted to arid environments, such as Criollo cattle, is one of several strategies recommended for decreasing the vulnerability to climate change of ranching in the southwestern USA. Our objective was to determine whether desirable foraging traits of Criollo cattle previously documented in the Chihuahuan Desert, held true in two of the most climate-vulnerable ecosystems of the Southwest. We conducted a study at Rancho Corta Madera (RCM) in southern California and Dugout Ranch (DR) in southeast Utah. Twenty mature cows, 10 Raramuri Criollo and 10 Red or Black Angus, were monitored with GPS collars during multiple seasons between 2018 and 2021. Geolocation data were used to compute daily distance traveled (km*d−1), movement velocity (m*min−1), path sinuosity (SI), time spent grazing, resting, or traveling (h*d−1), and area of the pasture explored (ha*d−1) as well as to calculate selection of vegetation cover types (E, Ivlev's Electivity Index) by cows of each breed. The effects of breed, season, year, and pasture on each of these metrics were modeled with repeated measures analyses of variance. At both ranches, statistically detectable differences (P ≤ 0.05) between breeds were observed for most behavior metrics during the dormant season. Conversely, few breed differences were observed during the growing season. Criollo cattle exhibited greater relative preference for a number of shrub dominated vegetation types at both ranches, and similar relative selection of grassland dominated sites compared to Angus counterparts. At both ranches, Criollo cattle exhibited similar or less relative preference for riparian areas vs. Angus counterparts. Breed divergence vs. convergence of foraging behaviors during the dormant vs. growing seasons, previously observed in the Chihuahuan Desert, was documented at both sites. Positive system outcomes associated with foraging traits of Criollo cattle could be expected to occur more broadly across the Southwest.
Earthquake ground motion processing for next-generation attenuation (NGA) projects required human inspection to select high-pass corner frequencies (fcHP), which is time-intensive and subjective. With growth in the number of recordings per event and interest in enhancing repeatability, we sought to develop automated procedures for fcHP selection. These procedures consider signal-to-noise ratio (SNR) and non-physical features in the displacement time series that indicate high- and/or low-frequency noise effects. The procedures are implemented in a US Geological Survey software package (gmprocess). We extend previous procedures for SNR-based corner frequency selection to also check for low-frequency artifacts in the displacement record using a polynomial fit to improve fcHP selection. We evaluate the performance of the SNR and polynomial fit criteria using recordings from the 2020 M5.1 North Carolina and the 2013 M4.7 Southern Ontario earthquakes. Data processed with the SNR-only criteria can have low fcHP and displacement drift; the displacement check increases fcHP and reduces displacement drift.
Ecosystems provide essential services to people including food, water, climate regulation, and aesthetic experiences. Biodiversity can enhance and stabilize ecosystem function and the resulting services natural systems provide. Freshwater mollusks are a diverse group that provide a variety of ecosystem services through their feeding habits (e.g., filter feeding, grazing), top-down and bottom-up effects on food webs, provisioning of habitat, use as a food resource by people, and cultural importance. Research focused on quantifying the direct and indirect ways mollusks influence ecosystem services may help inform policy makers and the public about the value of mollusk communities to society. The Freshwater Mollusk Conservation Society highlighted the need to evaluate mollusk ecosystem services in their 2016 National Strategy for the Conservation of Native Freshwater Mollusks, and, while significant progress has been made, considerable work remains across the research, management, and outreach communities. We briefly review the global status of native freshwater mollusks, assess the current state of knowledge regarding their ecosystem services, and highlight recent advances and knowledge gaps to guide further research and conservation actions. Our intention is to provide ecologists, conservationists, economists, and social scientists with information to improve science-based consideration of the social, ecological, and economic value of mollusk communities to healthy aquatic systems.
The USGS Volcano Science Center has a long history of science and crisis communication about volcanoes and their eruptions. Centered mainly on websites, email notifications, traditional media, and in-person interaction in the past, our toolkit has expanded in the last decade to include social media channels. This medium has allowed us to communicate with both long-standing and new audiences in new ways. In the process, social media communication has further developed trust in USGS researchers. In particular, the nearly 4-month-long 2018 eruption of Kīlauea volcano in the State of Hawaii necessitated the rapid development of a communication strategy that more deeply incorporated web and social media (Facebook and Twitter) channels to share critical eruption information. This was the first major volcanic eruption response where the USGS used official social media accounts as a significant form of public communication and outreach. These timely and conversive interactions furthered engagement with residents and reinforced the USGS as an authoritative and approachable voice on the eruption with U.S. and international audiences. In many cases, USGS Volcanoes' social media channels were also sampled directly by media outlets looking to provide current information, particularly by local reporters and citizen journalists. This helped disseminate scientific information directly to those who needed it and removed pressure from observatory scientists to respond to media requests. In short, the conversational tone and engaged and inquisitive online audience allowed the USGS Volcanoes' social media channels to act as a virtual community meeting, which nurtured a nearly continuous educational environment for both directly affected and distant members of the public. We present the history and details of this strategy here in hopes that it will benefit volcano observatories and other official agencies and crisis communicators.
Invasive plant species alter community dynamics and ecosystem properties, potentially leading to regime shifts. Here, the invasion of a non-native tree species into a stand of native tree species is simulated using an agent-based model. The model describes an invasive tree with fast growth and high seed production that produces litter with a suppressive effect on native seedlings, based loosely on Melaleuca quinquenervia, invasive to southern Florida. The effect of a biocontrol agent, which reduces the invasive tree's growth and reproductive rates, is included to study how effective biocontrol is in facilitating the recovery of native trees. Even under biocontrol, the invader has some advantages over native tree species, such as the ability to tolerate higher stem densities than the invaded species and its litter's seedling suppression effect. We also include a standing dead component of both species, where light interception from dead canopy trees influences neighboring tree demographics. The model is applied to two questions. The first is how the mean seedling dispersal rate affects the spread of the invading species into a pure stand of natives, assuming the same mean dispersal distance for both species. For assumed litter seedling suppression that roughly balances the fitness levels of the two species, which species dominates depends on the mean dispersal distance. The invader dominates at both very high and very low mean seedling dispersal distances, while the native tree dominates for dispersal distances in the intermediate range. The second question is how standing dead trees affect either the rate of spread of the invader or the rate of recovery of the native species. The legacy of standing dead invasive trees may delay the recovery of native vegetation. The results here are novel and show that agent-based modeling is essential in illustrating how the fine-scale modeling of local interactions of trees leads to effects at the population level.
Well ER-5-3-2 is part of a well network designed to monitor long-term water levels and radionuclide concentrations downgradient from underground nuclear tests that occurred in Frenchman Flat, an area of the U.S. Department of Energy Nevada National Security Site in southern Nevada. Interpretation of monitoring records for well ER-5-3-2 was confounded by previously unexplained water-level fluctuations in the well hydrograph. This study integrated geologic, hydrologic, and water-chemistry data to evaluate potential stresses and hydrologic conditions that likely affected the well ER-5-3-2 hydrograph. Numerical groundwater models were applied to evaluate four model scenarios: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) equilibration to vertical heterogeneities between shallow (low transmissivity) and deep (higher transmissivity) carbonate zones, and (4) equilibration to lateral heterogeneities in carbonate rocks.
Meteoric recharge was not the cause of the 21-foot (ft) water-level rise in well ER-5-3-2 from 2001 to 2011 or the 4-ft decline from 2012 to 2016. Based on observed water-level fluctuations in nearby wells, the water-level rise and decline from recharge for these periods was less than 3 and 1 ft, respectively. The lateral-heterogeneity scenario is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was a natural water-level reequilibration following the pumping-induced depressurization of a large volume of high transmissivity and low-storage carbonate rock that is surrounded by low transmissivity and high-storage carbonate rock. The lateral-heterogeneity scenario was discounted because simulated water levels cannot match the well ER-5-3-2 hydrograph. Underground nuclear testing and temperature effects were discounted based on hydraulic connections and water-temperature data.
Wellbore-leakage scenarios are based on the assumption that the water-level rise was sustained from leakage rates required to cause a localized mounding in the carbonate system near well ER-5-3-2, where the carbonate transmissivity is 530 square feet per day. Even though simulated and measured water levels compare favorably for scenarios of wellbore leakage with and without recharge, large volumes (178–184 million gallons) of groundwater from volcanic rocks would be required to leak into the carbonate system, which is not supported by water-chemistry data.
An alternative conceptualization of wellbore leakage is based on the assumption that the 21-ft water-level rise from 2001 to 2011 was sustained by the hydraulic disconnection of well ER-5-3-2 from the carbonate system. The disconnection occurred several months after a constant-rate test in well ER-5-3-2 when carbonate rocks were hydraulically disconnected from the well by either (1) the shifting of sloughed fill in the open hole or (2) the encrusting of carbonate precipitate in the well screen. The hydraulic disconnection effectively sealed the well and caused a 21-ft water-level rise from wellbore leakage during 2001–11. In this case, total wellbore leakage from 2001 to 2011 was about 50 gallons. The 4-ft water-level decline from 2012 to 2016 was conceptualized to have occurred from the slow breaking of the seal and reconnection of the well to the carbonate system. This alternative conceptualization of wellbore leakage was consistent with water-chemistry analyses because the computed wellbore leakage (50 gallons) was small relative to purged volumes (30,000–40,000 gallons) for sampling, and the water chemistry would not be expected to change.
The shallow-deep carbonate scenario provided another explanation for the well ER-5-3-2 hydrograph. This scenario is based on the assumption that well-construction effects and vertical heterogeneity of the carbonate system explain the ER-5-3-2 water-level trend. Well-construction effects are attributed to a temporary clogging of the open interval below the well screen that was opened during pumping events, which affected the hydraulic connection of deep transmissive carbonate rocks to the wellbore. The 21-ft water-level rise from 2001 to 2011 was a natural equilibration to shallow, low-transmissivity carbonate rocks during a period when the lower open interval was clogged. The 4-ft decline from 2012 to 2016 represents equilibration between the shallow and deep intervals, because of a partial unclogging of the connection between the two intervals. The low water levels from 2016 to 2021 resulted from pumping for sampling and an unclogging of the open interval so that the low head in the deep carbonate dominated the water level. Despite potential well-construction effects, from either a wellbore leakage or shallow-deep carbonate scenario, samples collected from well ER-5-3-2 are representative of the carbonate system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225132","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., and Frus, R.J., 2023, Evaluation of potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey Scientific Investigations Report 2022–5132, 35 p., https://doi.org/10.3133/sir20225132.","productDescription":"Report: viii, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-139917","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":413963,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C0NG5","text":"MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada","description":"Jackson, T.R., and Frus, R.J., 2023, MODFLOW 6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, southern Nevada: U.S. Geological Survey data release, available at https://doi.org/10.5066/P95C0NG5."},{"id":500485,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114613.htm","linkFileType":{"id":5,"text":"html"}},{"id":413973,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225132/full"},{"id":413962,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5132/images"},{"id":413961,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.xml"},{"id":413960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5132/sir20225132.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":413959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5132/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Frenchman Flat","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.05668643871044,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 35.84481987187543\n ],\n [\n -115.27945901304658,\n 36.549912612507626\n ],\n [\n -116.05668643871044,\n 36.549912612507626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
A study in cooperation with the Papio-Missouri River Natural Resources District was completed in 2019 to determine the concentration of contaminants of emerging concern (CEC) in groundwater in the Papio-Missouri River Natural Resources District, eastern Nebraska. Each well was sampled twice (in June and October or November) in 2019, totaling 34 samples. Samples were analyzed for 132 CECs, which include pharmaceutical, steroid hormone, and other organic chemicals. Seven of the 132 CEC analytes were detected in samples collected during this study. The most commonly detected CEC in this study was the antibiotic sulfamethoxazole. Other CECs detected in this study were nicotine, methyl-1H-benzotiazole (industrial product), acetaminophen (analgesic), caffeine, and metformin (diabetes medicine). None of the detected CECs have health-based water-quality standards. The agricultural herbicide atrazine was also sampled for and was detected in 15 of 26 samples from 8 wells, but all samples were below the established water-quality standard.
Nitrate, dissolved oxygen, and iron sampling results for 2010–19 and 1992–2020 were also assessed to determine the extent and trend of anthropogenic contamination in the Papio-Missouri River Natural Resources District. Nitrate as nitrogen was detected at a concentration greater than 4 milligrams per liter in 92 samples (19 percent), and detections in 36 samples (7.6 percent) exceeded 10 milligrams per liter, which is the U.S. Environmental Protection Agency’s maximum contaminant level for drinking water and Nebraska’s Title 118 maximum contaminant level for groundwater. Time series analysis showed that nitrate concentrations are not increasing or decreasing in any of the aquifers except for in three specific well nests, which are in phase 2 management areas. Dissolved oxygen results indicate potential denitrification throughout the Elkhorn alluvial aquifer; iron concentrations indicate potential denitrification in parts of the Missouri River alluvial aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235018","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resources District","usgsCitation":"Hall, B.M., Kavan, C.L., Flynn, A.T., and Cherry, M.L., 2023, Selected anthropogenic contaminants in groundwater, Papio-Missouri River Natural Resources District, eastern Nebraska, 1992–2020: U.S. Geological Survey Scientific Investigations Report 2023–5018, 35 p., https://doi.org/10.3133/sir20235018.","productDescription":"Report: viii, 35 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-129446","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":500712,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114614.htm","linkFileType":{"id":5,"text":"html"}},{"id":414566,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235018/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":414475,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":414473,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5018/images"},{"id":414467,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5018/coverthb.jpg"},{"id":414472,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5018/sir20235018.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414471,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5018/sir20235018.pdf","text":"Report","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5018"}],"country":"United States","state":"Nebraska","otherGeospatial":"Papio-Missouri River Natural Resources District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.44002964238103,\n 42.54396877053898\n ],\n [\n -96.58279049799194,\n 42.600578625387556\n ],\n [\n -96.76947777071449,\n 42.600578625387556\n ],\n [\n -96.74751456215893,\n 42.44680377755785\n ],\n [\n -96.62671691510306,\n 42.05663484833863\n ],\n [\n -96.6047537065475,\n 41.68045966846773\n ],\n [\n -96.56082728943636,\n 41.38451863047598\n ],\n [\n -96.51690087232578,\n 41.16994185576513\n ],\n [\n -96.27530557821459,\n 41.18647280254271\n ],\n [\n -96.03371028410338,\n 41.22778191479978\n ],\n [\n -95.83604140710383,\n 41.26906494493187\n ],\n [\n -95.93487584560361,\n 41.63943772246449\n ],\n [\n -96.05567349265895,\n 41.885177311048636\n ],\n [\n -96.25334236965901,\n 42.317015660865195\n ],\n [\n -96.44002964238103,\n 42.54396877053898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil properties, groundwater flow, land use, and other features that describe the hydrogeology and geochemistry of the aquifer system. Four models were developed to predict the probability of manganese concentrations greater than a detection level of 10 micrograms per liter (μg/L) and greater than three threshold concentrations (50, 150, and 300 μg/L) relevant to drinking-water quality. One model was developed to predict the probability of 1,4-dioxane concentrations greater than a detection level of 0.07 μg/L. The 1,4-dioxane model was limited geographically to Suffolk County because of data availability. Predictions were made for two layers in the upper glacial aquifer and three layers in the Magothy aquifer, which are the upper two of the three major aquifers of the Long Island aquifer system.
The objective of the study described in this report was to demonstrate the application of the methods rather than to develop precise estimates of manganese or 1,4-dioxane concentrations at any given location. The predictive models developed in the study are considered preliminary in the sense that they are an initial effort at developing these kinds of models specifically for Long Island. The models could be improved by the inclusion of additional data, by the use of methods to improve the modeling of infrequent high concentrations of manganese and 1,4-dioxane (above threshold concentrations), and by including more explanatory variables that specifically describe conditions and contaminant sources on Long Island. Nonetheless, the distribution of model predictions and the influence of explanatory variables in the models were consistent with the expected relations between contaminant concentrations and groundwater-flow-system characteristics and the distribution of manmade sources.
Mapped predictions indicated that manganese detections were more probable in the upper glacial aquifer and along the southern shore of Long Island, consistent with the distribution of anoxic conditions in groundwater in the Long Island aquifer system. Manganese was infrequently predicted at concentrations greater than thresholds of concern for drinking-water quality in any of the aquifer layers. Detections of 1,4-dioxane were predicted in the western, more highly developed parts of Suffolk County, in the upper glacial aquifer and the top and middle layers of the Magothy aquifer, and in northwestern Suffolk County in the bottom layer of the Magothy aquifer. Although preliminary in nature and based on limited data, these mapped predictions can be used to generally identify areas where manganese and 1,4-dioxane may be present at concentrations of concern to prioritize areas for future monitoring and to guide future modeling and mapping efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225120","programNote":"National Water Quality Program","usgsCitation":"DeSimone, L.A., 2023, Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5120, 34 p., https://doi.org/10.3133/sir20225120.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133571","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":414438,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5120/images/"},{"id":414437,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.XML"},{"id":414436,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225120/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5120"},{"id":500463,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114612.htm","linkFileType":{"id":5,"text":"html"}},{"id":414439,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AT9YG","text":"USGS data release","linkHelpText":"Data and model archive for preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York"},{"id":414434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5120/coverthb.jpg"},{"id":414435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5120/sir20225120.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5120"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.05146015978082,\n 40.628474760922984\n ],\n [\n -73.96502494019428,\n 40.542103435896706\n ],\n [\n -73.54649650851006,\n 40.545560430280744\n ],\n [\n -73.20985407432973,\n 40.61811609149555\n ],\n [\n -72.74128419972719,\n 40.738867255336714\n ],\n [\n -72.19082832762075,\n 40.90411303840304\n ],\n [\n -71.79504600635465,\n 41.08266452105815\n ],\n [\n -72.259066658874,\n 41.20257103045407\n ],\n [\n -72.71853808930965,\n 41.00374947032347\n ],\n [\n -73.1643618534946,\n 41.010615404965876\n ],\n [\n -73.52375039809249,\n 40.948796241204036\n ],\n [\n -73.76485916851937,\n 40.873160815851264\n ],\n [\n -73.87858972060721,\n 40.79055078444986\n ],\n [\n -74.01961560519632,\n 40.72163048139012\n ],\n [\n -74.05146015978082,\n 40.68714353955147\n ],\n [\n -74.05146015978082,\n 40.628474760922984\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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10 Bearfoot Road
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No abstract available.
","largerWorkTitle":"Recent advancement in geoinformatics and data science","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2558(001)","usgsCitation":"Ma, X., Mookerjee, M., Hsu, L., and Hills, D., 2023, Foreword, chap. of Recent advancement in geoinformatics and data science, v. 558, https://doi.org/10.1130/2022.2558(001).","productDescription":"1 p.","startPage":"v","ipdsId":"IP-142265","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":418592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"558","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ma, Xiaogang","contributorId":315381,"corporation":false,"usgs":false,"family":"Ma","given":"Xiaogang","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":876409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mookerjee, Matty","contributorId":315382,"corporation":false,"usgs":false,"family":"Mookerjee","given":"Matty","email":"","affiliations":[{"id":36475,"text":"Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":876410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":876411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hills, Denise","contributorId":315383,"corporation":false,"usgs":false,"family":"Hills","given":"Denise","email":"","affiliations":[{"id":13327,"text":"Geological Survey of Alabama","active":true,"usgs":false}],"preferred":false,"id":876412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247698,"text":"70247698 - 2023 - Metabarcoding analysis of meiobenthic biodiversity along the Gulf of Mexico continental shelf","interactions":[],"lastModifiedDate":"2023-08-11T14:27:27.877857","indexId":"70247698","displayToPublicDate":"2023-03-22T09:23:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Metabarcoding analysis of meiobenthic biodiversity along the Gulf of Mexico continental shelf","docAbstract":"This study explores how diverse the meiobenthic (meiofauna and other benthic micro-eukaryotes) community is throughout the United States Gulf of Mexico (GOM) continental shelf. In late 2010 and 2011, 51 sediment samples were collected along GOM from Texas through Florida at a range of depths (40m–496m). An additional six deep-sea slope sediment cores were collected in December 2010 near the Deepwater Horizon platform (1370–1385m and 1865m). Metabarcoding of the 18S hypervariable V9 region was conducted to assess biodiversity. Within continental shelf samples, there was greater meiobenthic diversity off the Eastern GOM coast in comparison to both Central and Western GOM coast locations. The Eastern GOM coast has known sediment differences from Western GOM sites. These sediment differences along with influences from the Gulf of Mexico Loop Current may account for observed variations in GOM meiobenthic diversity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2023.108303","usgsCitation":"Brannock, P.M., Demopoulos, A., Landers, S.C., Waits, D.S., and Halanych, K.M., 2023, Metabarcoding analysis of meiobenthic biodiversity along the Gulf of Mexico continental shelf: Estuarine, Coastal and Shelf Science, v. 285, 108303, 11 p., https://doi.org/10.1016/j.ecss.2023.108303.","productDescription":"108303, 11 p.","ipdsId":"IP-143979","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444121,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2023.108303","text":"Publisher Index Page"},{"id":419746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.0454668578711,\n 25.2112866504243\n ],\n [\n -82.26870034867704,\n 26.46838949145966\n ],\n [\n -82.72708466360095,\n 27.33933552895833\n ],\n [\n -82.88356327975112,\n 27.923703811696868\n ],\n [\n -82.69546708608799,\n 28.85741771478554\n ],\n [\n -83.05448982390429,\n 29.044828371309947\n ],\n [\n -84.00249439203462,\n 29.935333298895316\n ],\n [\n -85.17649729113158,\n 29.542297138113014\n ],\n [\n -86.37997870471644,\n 30.195765985638346\n ],\n [\n -87.96931156473273,\n 30.203483534805827\n ],\n [\n -89.11097137994817,\n 29.882704328046657\n ],\n [\n -89.74582087072409,\n 29.106656041968364\n ],\n [\n -90.30786307986214,\n 28.943752350879407\n ],\n [\n -92.53157289617916,\n 29.458633979299037\n ],\n [\n -93.8003865513774,\n 29.500604600212952\n ],\n [\n -94.75696670446546,\n 29.108237482144702\n ],\n [\n -96.00740561464876,\n 28.332182802828413\n ],\n [\n -96.97702364006607,\n 27.67159636325421\n ],\n [\n -97.18084541150141,\n 26.927544006927846\n ],\n [\n -97.05621148925196,\n 26.087197230578298\n ],\n [\n -95.67021883090977,\n 26.02157337070716\n ],\n [\n -94.6724873820428,\n 27.196943925915562\n ],\n [\n -89.67087171666184,\n 27.50767402365959\n ],\n [\n -87.5707278267364,\n 28.476759671528825\n ],\n [\n -85.60214939415401,\n 26.79513667934289\n ],\n [\n -84.15280818816129,\n 24.02363974069364\n ],\n [\n -81.0454668578711,\n 25.2112866504243\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brannock, Pamela M.","contributorId":328398,"corporation":false,"usgs":false,"family":"Brannock","given":"Pamela","email":"","middleInitial":"M.","affiliations":[{"id":24752,"text":"Rollins College","active":true,"usgs":false}],"preferred":false,"id":880076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":221145,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":880077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landers, Stephen C.","contributorId":328399,"corporation":false,"usgs":false,"family":"Landers","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":78357,"text":"Troy University","active":true,"usgs":false}],"preferred":false,"id":880078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waits, Damien S.","contributorId":328400,"corporation":false,"usgs":false,"family":"Waits","given":"Damien","email":"","middleInitial":"S.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":880079,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halanych, Kenneth M.","contributorId":328401,"corporation":false,"usgs":false,"family":"Halanych","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[{"id":65271,"text":"University of North Carolina at Wilmington","active":true,"usgs":false}],"preferred":false,"id":880080,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242003,"text":"70242003 - 2023 - Sex-biased infections scale to population impacts for an emerging wildlife disease","interactions":[],"lastModifiedDate":"2023-04-04T12:24:59.519962","indexId":"70242003","displayToPublicDate":"2023-03-22T07:21:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sex-biased infections scale to population impacts for an emerging wildlife disease","docAbstract":"Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.
Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.117668","usgsCitation":"Robertson, E.P., Walsh, D.P., Martin, J., Work, T.M., Kellogg, C.A., Evans, J.S., Hawthorn, A.C., Aeby, G., Paul, V.J., Walker, B., Kiryu, Y., Woodley, C., Meyer, J.L., Rosales, S.M., Studivan, M.S., Moore, J., Brandt, M.E., and Bruckner, A., 2023, Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases: Journal of Environmental Management, v. 337, 117668, 9 p., https://doi.org/10.1016/j.jenvman.2023.117668.","productDescription":"117668, 9 p.","ipdsId":"IP-146770","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research 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rate of biodiversity loss of terrestrial and marine habitats. As status quo actions within the conservation community are not reversing the downward trajectory for freshwater biodiversity, we propose four actions to shift the narrative such that freshwater biodiversity is no longer invisible and overlooked, but rather explicitly recognized, valued, and protected: (1) Reshape our relationship with freshwater habitats and biodiversity, (2) Appreciate indigenous knowledge systems relating to freshwater habitats, (3) Connect science more directly to action, and (4) Elevate freshwater habitats as a unique “domain” that requires explicit recognition in conservation planning (RACE). We highlight roles that both freshwater scientists and the wider conservation community can play in implementing the four actions such that the “RACE” can be won.
By 2050, mean temperature in the state of Maine, located in the Northeastern USA, is expected to increase nearly 1°C, which could directly affect native coldwater salmonid behaviour and increase competition with warmwater smallmouth bass. We conducted a microcosm experiment to examine the feeding and agonistic behaviour of endangered juvenile Atlantic Salmon (Salmo salar) at two temperatures (18 and 21°C) in the presence and absence of non-native Smallmouth Bass (Micropterus dolomieu). By visually reviewing footage of fish competition in our tanks, we quantified feeding and agonistic interactions. We predicted salmon would exhibit lower feeding activity than bass at 21°C and antagonistic interactions between the two species would increase with warming. We found salmon feeding activity was reduced by smallmouth bass presence and this effect was stronger at 21°C. We also found smallmouth bass aggression was strongest at 21°C when salmon were present. Lastly, feeding activity and aggression in both species changed with food availability. These findings illustrate the potential for invasive warmwater species to outcompete native salmonids for resources, especially under the warmer conditions predicted by climate change scenarios.
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