{"pageNumber":"66","pageRowStart":"1625","pageSize":"25","recordCount":46619,"records":[{"id":70261622,"text":"70261622 - 2024 - Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface","interactions":[],"lastModifiedDate":"2024-12-17T15:16:51.839947","indexId":"70261622","displayToPublicDate":"2024-06-20T09:10:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface","docAbstract":"<p><span>The wild to domestic bird interface is an important nexus for emergence and transmission of highly pathogenic avian influenza (HPAI) viruses. Although the recent incursion of HPAI H5N1 Clade 2.3.4.4b into North America calls for emergency response and planning given the unprecedented scale, readily available data-driven models are lacking. Here, we provide high resolution spatial and temporal transmission risk models for the contiguous United States. Considering virus host ecology, we included weekly species-level wild waterfowl (Anatidae) abundance and endemic low pathogenic avian influenza virus prevalence metrics in combination with number of poultry farms per commodity type and relative biosecurity risks at two spatial scales: 3&nbsp;km and county-level. Spillover risk varied across the annual cycle of waterfowl migration and some locations exhibited persistent risk throughout the year given higher poultry production. Validation using wild bird introduction events identified by phylogenetic analysis from 2022 to 2023 HPAI poultry outbreaks indicate strong model performance. The modular nature of our approach lends itself to building upon updated datasets under evolving conditions, testing hypothetical scenarios, or customizing results with proprietary data. This research demonstrates an adaptive approach for developing models to inform preparedness and response as novel outbreaks occur, viruses evolve, and additional data become available.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-64912-w","usgsCitation":"Prosser, D., Kent, C.M., Sullivan, J.D., Patyk, K.A., McCool, M., Torchetti, M.K., Lantz, K., and Mullinax, J.M., 2024, Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface: Scientific Reports, v. 14, 14199, 13 p., https://doi.org/10.1038/s41598-024-64912-w.","productDescription":"14199, 13 p.","ipdsId":"IP-160406","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-64912-w","text":"Publisher Index 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]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-06-20","publicationStatus":"PW","contributors":{"authors":[{"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":921226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":921227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":921228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patyk, Kelly A.","contributorId":139696,"corporation":false,"usgs":false,"family":"Patyk","given":"Kelly","email":"","middleInitial":"A.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":921229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCool, Mary-Jane","contributorId":347273,"corporation":false,"usgs":false,"family":"McCool","given":"Mary-Jane","email":"","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":921230,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torchetti, Mia K.","contributorId":252830,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"K.","affiliations":[{"id":50437,"text":"US Department of Agriculture – Veterinary Services, Ames, Iowa, USA","active":true,"usgs":false}],"preferred":false,"id":921231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lantz, Kristina","contributorId":317920,"corporation":false,"usgs":false,"family":"Lantz","given":"Kristina","email":"","affiliations":[{"id":69192,"text":"National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, USDA","active":true,"usgs":false}],"preferred":false,"id":921232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":921233,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70257521,"text":"70257521 - 2024 - Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model","interactions":[],"lastModifiedDate":"2024-09-06T15:17:44.893598","indexId":"70257521","displayToPublicDate":"2024-06-20T08:11:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model","docAbstract":"<p><span>Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability &gt;0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ecy.4362","usgsCitation":"Custer, C.A., North, J.S., Schliep, E., Verhoeven, M.R., Hansen, G.J., and Wagner, T., 2024, Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model: Ecology, v. 105, no. 8, e4362, 16 p., https://doi.org/10.1002/ecy.4362.","productDescription":"e4362, 16 p.","ipdsId":"IP-159528","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4362","text":"Publisher Index Page"},{"id":433556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University","active":true,"usgs":false}],"preferred":false,"id":910614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verhoeven, Michael R.","contributorId":343087,"corporation":false,"usgs":false,"family":"Verhoeven","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":910615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Gretchen J.A.","contributorId":343090,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen","email":"","middleInitial":"J.A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":910616,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X 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,{"id":70259615,"text":"70259615 - 2024 - Indications of preferential groundwater seepage feeding northern peatland pools","interactions":[],"lastModifiedDate":"2024-10-17T12:07:58.976979","indexId":"70259615","displayToPublicDate":"2024-06-20T07:05:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Indications of preferential groundwater seepage feeding northern peatland pools","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab010\" class=\"abstract author\"><div id=\"as010\"><div id=\"sp0010\" class=\"u-margin-s-bottom\">Groundwater seepage from underlying permeable glacial sedimentary structures, such as eskers, has been hypothesized to directly feed pools in northern peat bogs. These hypotheses directly contradict classical peat bog models for ombrogenous systems, wherein meteoric water is the sole water input to these systems. Variations in the underlying mineral sediment in contact with the peat imply that unrecognized hydrogeologic connectivity may exist with pools in northern peat bogs, particularly where high permeability materials are in contact with the peat. Seepage dynamics originating from these structural variations were investigated using a suite of thermal and hydrogeophysical methods deployed around pools in a peat bog of northeastern Maine, USA. Thermal characterization methods mapped anomalies that were confirmed as matrix seepage or preferential flow pathways (PFPs). Geochemical methods were employed at identified thermal anomalies to confirm upwelling of solute-rich groundwater. Conduits around pools were associated with surficial terminations of suspected peat pipes, based on the inference of pathways extending down into the peat, that focus flow through PFPs in the peat matrix. Discharge also occurred through the peat matrix adjacent to suspected pipe structures and matrix seepage rates were quantified using analysis of diurnal temperature signals recorded at multiple depths. Seepage rates, with a maximum of nearly 0.4&nbsp;m/d, were measured at localized points around pools. Periods of synchronized temperatures paired with highly muted diurnal temperature signals, recorded in diurnal temperature with depth data, were interpreted qualitatively as activation of strong upward discharge rates through suspected peat pipes. These time periods correlated strongly with local precipitation events around the peatland. Ground-penetrating radar surveys revealed discontinuities in the low permeability glacio-marine clay at the mineral sediment-peat interface, interpreted to be regional glacial esker deposits, which were located beneath and around pools. Heat tracing, specific conductance contrasts, seepage rates, and trace metal concentrations all imply groundwater seepage originating from underlying permeable glacial esker deposits and directly sourcing pools. Preferential groundwater inputs into northern peat bogs may play a key role in developing and maintaining pool systems, with enhanced solute transport impacting peatland ecology, water resources, and carbon cycling.</div></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131479","usgsCitation":"Moore, H., Comas, X., Briggs, M., Reeve, A., and Slater, L., 2024, Indications of preferential groundwater seepage feeding northern peatland pools: Journal of Hydrology, v. 638, 131479, 16 p., https://doi.org/10.1016/j.jhydrol.2024.131479.","productDescription":"131479, 16 p.","ipdsId":"IP-162568","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466993,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.131479","text":"Publisher Index Page"},{"id":462938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","county":"Washington County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -67.76495873773497,\n              45.39282615624336\n            ],\n            [\n              -67.76495873773497,\n              45.153153649758934\n            ],\n            [\n              -67.37203263181632,\n              45.153153649758934\n            ],\n            [\n              -67.37203263181632,\n              45.39282615624336\n            ],\n            [\n              -67.76495873773497,\n              45.39282615624336\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"638","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Henry","contributorId":302186,"corporation":false,"usgs":false,"family":"Moore","given":"Henry","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":915966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Comas, Xavier","contributorId":201325,"corporation":false,"usgs":false,"family":"Comas","given":"Xavier","email":"","affiliations":[],"preferred":false,"id":915967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":915968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeve, Andrew S.","contributorId":343135,"corporation":false,"usgs":false,"family":"Reeve","given":"Andrew S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":915969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slater, Lee","contributorId":55707,"corporation":false,"usgs":false,"family":"Slater","given":"Lee","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":915970,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70255569,"text":"70255569 - 2024 - Evaluation of extinction risk for stream fishes within an urban riverscape using population viability analysis","interactions":[],"lastModifiedDate":"2024-06-24T15:05:48.717034","indexId":"70255569","displayToPublicDate":"2024-06-19T09:46:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of extinction risk for stream fishes within an urban riverscape using population viability analysis","docAbstract":"<p><span>1. The Santa Ana River in the Los Angeles region of California demonstrates common habitat degradation symptoms that are characteristic of the urban stream syndrome. These impacts have altered the Santa Ana River community structure, with few species as impacted as the native Santa Ana sucker (sucker;&nbsp;</span><i>Pantosteus santaanae</i><span>). 2. Consequently, a recovery plan developed for sucker identified the need for a population viability analysis (PVA) to assess sucker extirpation risk. However, PVAs can be data-intensive and are subject to several sources of bias when standardized protocols are absent. 3. More than 20&nbsp;years of sucker and arroyo chub (chub;&nbsp;</span><i>Gila orcuttii</i><span>) surveys using different methods were compiled to build an integrated hierarchical multi-population PVA to estimate trends in abundance and extirpation probability of these native fishes from the Santa Ana River. 4. PVA modelling indicated similar patterns in sucker and chub abundance along the Santa Ana River, with the highest abundance of both species in the upper regions of the river during the early 2000s and downstream in recent years (2018–2022). Extirpation risk was estimated to be greatest near wastewater treatment facilities, where native fish abundance estimates have been zero since 2018. Extirpation risk was lower downstream of the wastewater treatment facilities for both species, although extinction risk was higher for sucker than chub throughout the river. 5. As the model evolves and more data are collected, the PVA could be used to assess the effects of various management actions, such as non-native predator removals and native fish re-introductions, on sucker and chub persistence.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/aqc.4164","usgsCitation":"Huntsman, B., Palenscar, K., Russell, K., Mills, B., Jones, C., Ota, W., Anderson, K.E., Dyer, H., Abadi, F., and Wulff, M.L., 2024, Evaluation of extinction risk for stream fishes within an urban riverscape using population viability analysis: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 34, no. 6, e4164, 15 p., https://doi.org/10.1002/aqc.4164.","productDescription":"e4164, 15 p.","ipdsId":"IP-155060","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":490042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aqc.4164","text":"Publisher Index Page"},{"id":430448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Ana River drainage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.91739602273532,\n              33.59806544941986\n            ],\n            [\n              -117.07072671553968,\n              33.928615009582344\n            ],\n            [\n              -116.86491284498888,\n              34.11992851642641\n            ],\n            [\n              -117.15177435882552,\n              34.394478755569835\n            ],\n            [\n              -117.39266048613797,\n              34.37688489281085\n            ],\n            [\n              -117.75593270208347,\n              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E.","contributorId":265545,"corporation":false,"usgs":false,"family":"Anderson","given":"Kurt","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":904783,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dyer, Heather","contributorId":297134,"corporation":false,"usgs":false,"family":"Dyer","given":"Heather","email":"","affiliations":[{"id":64298,"text":"San Bernardino Valley Municipal Water District","active":true,"usgs":false}],"preferred":false,"id":904784,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Abadi, Fitsum","contributorId":244779,"corporation":false,"usgs":false,"family":"Abadi","given":"Fitsum","affiliations":[{"id":48968,"text":"New Mexico State University, Department of Fish, Wildlife and Conservation Ecology","active":true,"usgs":false}],"preferred":false,"id":904785,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wulff, Marissa L. 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,{"id":70256577,"text":"70256577 - 2024 - Fish size structure analysis via ordination: A visualization aid","interactions":[],"lastModifiedDate":"2024-08-26T15:05:37.260447","indexId":"70256577","displayToPublicDate":"2024-06-18T11:54:36","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Fish size structure analysis via ordination: A visualization aid","docAbstract":"<h3 id=\"nafm10998-sec-1000-title\" class=\"article-section__sub-title section1\">Objective</h3><p>Visual aids like length-frequency histograms are widely used to examine fish population status and trends; however, comparing multiple histograms simultaneously becomes cumbersome and inefficient. Complicating matters further, overlaying covariates on histograms to highlight connections with length frequencies can be challenging. An alternative, and the subject of this Perspective, is to display length distributions as an ordination using similarity indexes; in many cases, this allows for improved visual organization and representation of relationships with covariates.</p><h3 id=\"nafm10998-sec-2000-title\" class=\"article-section__sub-title section1\">Methods</h3><p>I review the application of ordination methods for analysis of size structures using alternative visualizations that may facilitate the identification of connections that are concealed when analyzing a series of histograms. After a brief introduction to similarity indexes, types of ordinations, and sample sizes, I examine four case studies to illustrate size structure analysis via similarity indices: (1) unconstrained ordination to identify “bass-crowded” populations in a set of 34 small fishing lakes, (2) unconstrained ordination to evaluate the effect of three consecutive length limits on a Largemouth Bass<span>&nbsp;</span><i>Micropterus nigricans</i><span>&nbsp;</span>population over a span of 28 years, (3) constrained ordination to assess the relationships between fish community size structure and in-lake and off-lake environmental descriptors in 30 oxbow lakes, and (4) constrained ordination to identify what aspects of Largemouth Bass size structure were related to six types of reservoir habitats.</p><h3 id=\"nafm10998-sec-3000-title\" class=\"article-section__sub-title section1\">Result</h3><p>Size structure analysis via similarity indexes enabled the exploration of extensive length-frequency data. It is important to acknowledge that ordinations serve solely as a visual aid for assessing size structure—no statistical testing is involved.</p><h3 id=\"nafm10998-sec-4000-title\" class=\"article-section__sub-title section1\">Conclusion</h3><p>Ordination techniques and software are advancing at a quick pace, holding great promise for the future of size structure analysis via similarity indices.</p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10998","usgsCitation":"Miranda, L.E., 2024, Fish size structure analysis via ordination: A visualization aid: North American Journal of Fisheries Management, v. 44, no. 4, p. 763-775, https://doi.org/10.1002/nafm.10998.","productDescription":"13 p.","startPage":"763","endPage":"775","ipdsId":"IP-158640","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499918,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10998","text":"Publisher Index Page"},{"id":432603,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-06-18","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":908104,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70255333,"text":"dr1196 - 2024 - Distribution, abundance, and habitat characteristics of Coastal Cactus Wrens (Campylorhynchus brunneicapillus) in San Diego County, California—2023 Data Summary","interactions":[],"lastModifiedDate":"2024-06-18T21:01:38.647217","indexId":"dr1196","displayToPublicDate":"2024-06-18T09:43:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1196","displayTitle":"Distribution, Abundance, and Habitat Characteristics of Coastal Cactus Wrens (<i>Campylorhynchus brunneicapillus</i>) in San Diego County, California—2023 Data Summary","title":"Distribution, abundance, and habitat characteristics of Coastal Cactus Wrens (Campylorhynchus brunneicapillus) in San Diego County, California—2023 Data Summary","docAbstract":"<h1>Executive Summary</h1><p>We surveyed for coastal Cactus Wren (<i>Campylorhynchus brunneicapillus</i>) in 507 established plots in San Diego County in 2023, encompassing 4 genetic clusters (Otay, Lake Jennings, Sweetwater/Encanto, and San Pasqual). Two surveys were completed at each plot between March 1 and July 31. Cactus Wrens were detected in 181 plots (36 percent of plots). Cactus Wrens were detected in 26 percent of plots that have been consistently surveyed since 2020, indicating lower plot occupancy than in 2022 (31 percent), 2021 (34 percent), and 2020 (35 percent). There were 158 Cactus Wren territories detected across all survey plots in 2023. In plots that have been consistently surveyed since 2020, we documented 85 territories, which is a decrease from 94 territories in 2022, 113 territories in 2021, and 109 territories in 2020. The number of territories declined from 2022 to 2023 in the Lake Jennings, Sweetwater/Encanto, and San Pasqual genetic clusters but remained virtually the same in the Otay genetic cluster. At least 80 percent of Cactus Wren territories were occupied by pairs, and 125 fledglings were observed in 2023.</p><p>We observed 14 banded Cactus Wrens in 2023, 9 of which we could identify individually by color band combination. Adults of known age ranged from 4 to 7 years old. All individually identifiable adult Cactus Wrens occupied the same territory in 2023 that they occupied in 2022, and we detected no movement of banded Cactus Wrens between genetic clusters.</p><p>Vegetation at Cactus Wren survey plots was dominated by coastal sage scrub shrubs, such as California sagebrush (<i>Artemisia californica</i>), California buckwheat (<i>Eriogonum fasciculatum</i>), lemonade berry (<i>Rhus integrifolia</i>), jojoba (<i>Simmondsia chinensis</i>), and San Diego viguiera (<i>Bahiopsis laciniata</i>). No definitive signs of fungal pathogens were observed on cactus within and around survey plots. Blue elderberry (<i>Sambucus mexicana</i>) was detected at 41 percent of plots, and Cactus Wrens occupied proportionally more plots with elderberry than plots without elderberry. Very little dead or unhealthy cactus was observed within all survey plots, and Cactus Wren occupancy did not differ between plots with high or low amounts of dead or unhealthy cactus. Almost 90 percent of plots had more than 5 percent of cactus crowded or overtopped by vines and shrubs, and Cactus Wren occupancy did not differ between plots with high or low amounts of cactus crowded or overtopped by vines and shrubs. Non-native annual cover was more prevalent in survey plots in 2023 than in 2022. Cactus Wrens did not select or avoid plots with more non-native cover.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1196","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., and Kus, B.E., 2024, Distribution, abundance, and habitat characteristics of Coastal Cactus Wrens (<i>Campylorhynchus brunneicapillus</i>) in San Diego County, California—2023 data summary: U.S. Geological Survey Data Report 1196, 14 p., https://doi.org/10.3133/dr1196.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-159898","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":430332,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"USGS Data Release","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Deigo County (ver. 4.0, February 2024): U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5.","linkHelpText":"Surveys and monitoring of Coastal Cactus Wren in southern San Deigo County (ver. 4.0, February 2024)"},{"id":430327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1196/covrthb.jpg"},{"id":430328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1196/dr1196.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430329,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1196/dr1196.xml"},{"id":430330,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1196/images"},{"id":430331,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1196/full"}],"country":"United States","state":"California","county":"San Diego 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href=\"https://www.usgs.gov/centers/werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/werc\">Western Ecological Research Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Executive Summary</li><li>Introduction</li><li>Study Area and Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-06-18","noUsgsAuthors":false,"publicationDate":"2024-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynn, Suellen 0000-0003-1543-0209 suellen_lynn@usgs.gov","orcid":"https://orcid.org/0000-0003-1543-0209","contributorId":3843,"corporation":false,"usgs":true,"family":"Lynn","given":"Suellen","email":"suellen_lynn@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":904308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":904309,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70256575,"text":"70256575 - 2024 - Fish assemblage and functional trait responses to small-dam removal","interactions":[],"lastModifiedDate":"2025-08-29T16:56:57.301943","indexId":"70256575","displayToPublicDate":"2024-06-18T06:11:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Fish assemblage and functional trait responses to small-dam removal","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><ol class=\"\"><li>Dams are one of the greatest threats to freshwater biodiversity and efforts to remove dams to restore riverine systems are increasing. However, dam-removal studies have primarily focused on taxonomic responses to large dam removals with little work on the functional trait responses of fish to small-dam removals; such a focus limits the application of results in regions with different taxonomic compositions.</li><li>We explored taxonomic and functional trait responses of fish assemblages to two small-dam removals over 10 years using a Multiple Before After Control Impact design at a dammed and an undammed river. Eight life-history traits were used to calculate functional diversity (RaoQ) and determine the position of each fish species within a multivariate life-history space relative to three life-history strategy endpoints: opportunistic, periodic, and equilibrium. The distance of each species relative to these endpoints was used to calculate community weighted means (CWM), allowing us to examine the shift in life-history strategy of a given assemblage after dam removal. Based on life-history theory, we predicted a decrease in the CWM of equilibrium strategists and an increase in the CWM opportunistic strategists after dam removal.</li><li>For the dammed river, we observed changes in assemblage structure at both the control and impact sites driven primarily by shifts from a lentic to lotic assemblage, with the most apparent change occurring in the formerly impounded sites. These changes tended to occur within 1 year, suggesting rapid colonization by lotic species after habitat change. By contrast, no change in assemblage structure was found for the undammed river, suggesting that dam removal was the primary driver of the shifts in assemblage structure observed in the dammed river. We found no change in the CWM of periodic strategists or RaoQ of all life-history traits at any site after dam removal.</li><li>Based on CWM, life-history strategies shifted in response to dam removal at impounded sites where equilibrium strategists decreased and opportunistic strategists tended to increase after the impounded sites changed from a stable lentic environment to an unstable lotic environment, supporting predictions made by life-history theory.</li><li>Our results suggest that small-dam removal may provide ecological benefits by restoring fish assemblages to a more natural riverine state and reversing the negative effects of dam construction on the ecosystem. We demonstrate that combining both trait-based and taxonomic approaches can improve our ecological understanding of the impacts of dam removal on fish assemblages and provide relevant data for local management.</li></ol></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/fwb.14288","usgsCitation":"Bower, L.M., Marion, C.A., Scott, M.K., Kubach, K., and Gelder, A., 2024, Fish assemblage and functional trait responses to small-dam removal: Freshwater Biology, v. 69, no. 8, p. 1043-1056, https://doi.org/10.1111/fwb.14288.","productDescription":"14 p.","startPage":"1043","endPage":"1056","ipdsId":"IP-158010","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487526,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fwb.14288","text":"Publisher Index Page"},{"id":433044,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.02923236280236,\n              35.003395735325356\n            ],\n            [\n              -83.02923236280236,\n              34.38010788611466\n            ],\n            [\n              -82.17229876905215,\n              34.38010788611466\n            ],\n            [\n              -82.17229876905215,\n              35.003395735325356\n            ],\n            [\n              -83.02923236280236,\n              35.003395735325356\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"69","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bower, Luke Max 0000-0002-0739-858X","orcid":"https://orcid.org/0000-0002-0739-858X","contributorId":341034,"corporation":false,"usgs":true,"family":"Bower","given":"Luke","email":"","middleInitial":"Max","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marion, C. A.","contributorId":341211,"corporation":false,"usgs":false,"family":"Marion","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scott, M. K.","contributorId":272276,"corporation":false,"usgs":false,"family":"Scott","given":"M.","email":"","middleInitial":"K.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kubach, Kevin","contributorId":341212,"corporation":false,"usgs":false,"family":"Kubach","given":"Kevin","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gelder, Andrew","contributorId":360772,"corporation":false,"usgs":false,"family":"Gelder","given":"Andrew","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908092,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70260653,"text":"70260653 - 2024 - Climate driven trends in historical extreme low streamflows on four continents","interactions":[],"lastModifiedDate":"2024-11-06T16:09:59.479296","indexId":"70260653","displayToPublicDate":"2024-06-17T10:07:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Climate driven trends in historical extreme low streamflows on four continents","docAbstract":"<p><span>Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low-flow events (5- to 100-year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate-driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low-flow regimes using air temperature and monthly low-flow frequency. Trends in the annual occurrence rate of extreme low-flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (</span><i>p</i><span>&nbsp;&lt;&nbsp;0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold-season low-flow regime in North America were found to be significantly decreasing for low return-period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low-flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold- and warm-season low-flow regimes; there were also some non-significant trends. The difference in the results between the shorter (40-year) and longer (70-year) records and between low-flow regimes highlights the complexities of low-flow response to changing climatic conditions.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR034326","usgsCitation":"Hodgkins, G.A., Renard, B., Whitfield, P.H., Laaha, G., Stahl, K., Hannaford, J., Burn, D.H., Westra, S., Fleig, A.K., Lopes, W.T., Murphy, C., Mediero, L., and Hanel, M., 2024, Climate driven trends in historical extreme low streamflows on four continents: Water Resources Research, v. 60, no. 6, e2022WR034326, 25 p., https://doi.org/10.1029/2022WR034326.","productDescription":"e2022WR034326, 25 p.","ipdsId":"IP-147345","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":466994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr034326","text":"Publisher Index Page"},{"id":463765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":918098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitfield, Paul H.","contributorId":198041,"corporation":false,"usgs":false,"family":"Whitfield","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918099,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laaha, Gregor","contributorId":335609,"corporation":false,"usgs":false,"family":"Laaha","given":"Gregor","email":"","affiliations":[{"id":80445,"text":"University of Natural Resources and Life Sciences, Austria","active":true,"usgs":false}],"preferred":false,"id":918100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stahl, Kerstin","contributorId":198044,"corporation":false,"usgs":false,"family":"Stahl","given":"Kerstin","email":"","affiliations":[],"preferred":false,"id":918101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hannaford, Jamie","contributorId":198043,"corporation":false,"usgs":false,"family":"Hannaford","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":918102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burn, Donald H.","contributorId":198042,"corporation":false,"usgs":false,"family":"Burn","given":"Donald","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918103,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Westra, Seth","contributorId":335610,"corporation":false,"usgs":false,"family":"Westra","given":"Seth","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":918104,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fleig, Anne K.","contributorId":198045,"corporation":false,"usgs":false,"family":"Fleig","given":"Anne","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":918105,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lopes, Walsczon Terllizzie Araujo","contributorId":335611,"corporation":false,"usgs":false,"family":"Lopes","given":"Walsczon","email":"","middleInitial":"Terllizzie Araujo","affiliations":[{"id":80446,"text":"National Water and Sanitation Agency, Brazil","active":true,"usgs":false}],"preferred":false,"id":918106,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Murphy, Conor","contributorId":198049,"corporation":false,"usgs":false,"family":"Murphy","given":"Conor","email":"","affiliations":[],"preferred":false,"id":918108,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mediero, Luis","contributorId":198047,"corporation":false,"usgs":false,"family":"Mediero","given":"Luis","email":"","affiliations":[],"preferred":false,"id":918109,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hanel, Martin","contributorId":346109,"corporation":false,"usgs":false,"family":"Hanel","given":"Martin","email":"","affiliations":[],"preferred":false,"id":918115,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70256062,"text":"70256062 - 2024 - Solute export patterns across the contiguous USA","interactions":[],"lastModifiedDate":"2024-07-18T14:43:05.641206","indexId":"70256062","displayToPublicDate":"2024-06-17T09:38:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Solute export patterns across the contiguous USA","docAbstract":"<p><span>Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large-scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS-Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long-term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15197","usgsCitation":"Kincaid, D.W., Underwood, K.L., Hamshaw, S.D., Li, L., Seybold, E.C., Stewart, B., Rizzo, D.M., Ul Haq, I., and Perdrial, J.N., 2024, Solute export patterns across the contiguous USA: Hydrological Processes, v. 38, no. 6, e15197, 17 p., https://doi.org/10.1002/hyp.15197.","productDescription":"e15197, 17 p.","ipdsId":"IP-155135","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15197","text":"Publisher Index Page"},{"id":431219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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    28.11\n              ],\n              [\n                -100.45584,\n                28.69612\n              ],\n              [\n                -100.9576,\n                29.38071\n              ],\n              [\n                -101.6624,\n                29.7793\n              ],\n              [\n                -102.48,\n                29.76\n              ],\n              [\n                -103.11,\n                28.97\n              ],\n              [\n                -103.94,\n                29.27\n              ],\n              [\n                -104.45697,\n                29.57196\n              ],\n              [\n                -104.70575,\n                30.12173\n              ],\n              [\n                -105.03737,\n                30.64402\n              ],\n              [\n                -105.63159,\n                31.08383\n              ],\n              [\n                -106.1429,\n                31.39995\n              ],\n              [\n                -106.50759,\n                31.75452\n              ],\n              [\n                -108.24,\n                31.75485\n              ],\n              [\n                -108.24194,\n                31.34222\n              ],\n              [\n                -109.035,\n                31.34194\n              ],\n              [\n                -111.02361,\n                31.33472\n              ],\n              [\n                -113.30498,\n                32.03914\n              ],\n              [\n                -114.815,\n                32.52528\n              ],\n              [\n                -114.72139,\n                32.72083\n              ],\n              [\n                -115.99135,\n                32.61239\n              ],\n              [\n                -117.12776,\n                32.53534\n              ],\n              [\n                -117.29594,\n                33.04622\n              ],\n              [\n                -117.944,\n                33.62124\n              ],\n              [\n                -118.4106,\n                33.74091\n              ],\n              [\n                -118.51989,\n                34.02778\n              ],\n              [\n                -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                35.15686\n              ],\n              [\n                -121.71457,\n                36.16153\n              ],\n              [\n                -122.54747,\n                37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"38","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kincaid, Dustin W. 0000-0003-1640-685X","orcid":"https://orcid.org/0000-0003-1640-685X","contributorId":340199,"corporation":false,"usgs":false,"family":"Kincaid","given":"Dustin","email":"","middleInitial":"W.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Kristen L. 0000-0003-3008-3057","orcid":"https://orcid.org/0000-0003-3008-3057","contributorId":340200,"corporation":false,"usgs":false,"family":"Underwood","given":"Kristen","email":"","middleInitial":"L.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":906558,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, L.","contributorId":152225,"corporation":false,"usgs":false,"family":"Li","given":"L.","affiliations":[],"preferred":false,"id":906559,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seybold, Erin C. 0000-0002-0365-2333","orcid":"https://orcid.org/0000-0002-0365-2333","contributorId":340201,"corporation":false,"usgs":false,"family":"Seybold","given":"Erin","email":"","middleInitial":"C.","affiliations":[{"id":35641,"text":"Kansas Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906560,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, Bryn 0000-0002-3199-0129","orcid":"https://orcid.org/0000-0002-3199-0129","contributorId":340202,"corporation":false,"usgs":false,"family":"Stewart","given":"Bryn","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":906561,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rizzo, Donna M.","contributorId":171679,"corporation":false,"usgs":false,"family":"Rizzo","given":"Donna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":906562,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ul Haq, Ijaz","contributorId":340203,"corporation":false,"usgs":false,"family":"Ul Haq","given":"Ijaz","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906563,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Perdrial, Julia N.","contributorId":177340,"corporation":false,"usgs":false,"family":"Perdrial","given":"Julia","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":906564,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064B","publicationYear":"2024","noYear":false,"chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-17T22:21:15.873668","indexId":"sir20235064B","displayToPublicDate":"2024-06-17T07:11:12","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","docAbstract":"<p>This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; 30 years 1991–2020) for trends, change points, and other statistical properties indicative of changing conditions. Median peak streamflows generally exhibit upward trends across the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 23-percent increase during the 30-year trend period to a 41-percent increase during the 100-year trend period. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. More than two-thirds of streamgages at the 100- and 75-year trend periods exhibit a trend and change point in median peak streamflow in the same direction. Temporally, clusters of change points are observed in the late 1960s through early 1980s for the 100- and 75-year trend periods and around 2006 for the 50- and 30-year trend periods. Trends in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, increased about the same amount as the 50-percent quantile peak streamflows, except at the 100-year trend period, where the 50-percent quantile peak flow increased more for almost all streamgages. The frequency of high flows has also increased in Illinois, with increases in peaks-over-threshold observed across much of the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods.</p><p>Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with clusters of likely upward trends observed in northern and southern Illinois at the 50- and 30-year trend periods. As expected in response to increasing temperature, the modeled proportion of precipitation falling as snow has largely decreased in the study basins across the State, and modeled potential evapotranspiration has increased. Upward trends in modeled annual runoff, which in this report incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation.</p><p>The widespread upward trends in the magnitude of median peak streamflows and the frequency with which high flows occur across the State at the 100- and 75-year trend periods and in northern and southern Illinois at the 50- and 30-year trend periods appear to be driven largely by increases in precipitation based on spatial patterns of these changes and statistical relations between streamflow and climate metrics. Other effects not considered in this report, like urbanization, may be important drivers for certain streamgages in the State.</p><p>The prevalence of nonstationarity in peak streamflow in Illinois has important implications for peak-flow frequency analysis. Average annual precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064B","collaboration":"Prepared in cooperation with the Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Marti, M.K., and Over, T.M., 2024, Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020, chap. B <i>of</i> Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 58 p., https://doi.org/10.3133/sir20235064B.","productDescription":"Report: viii, 58 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146370","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":430160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1921–2020"},{"id":430161,"rank":6,"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":430162,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064B/full"},{"id":430156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/b/coverthb.jpg"},{"id":430157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/b/sir20235064b.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5064–B"},{"id":430158,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/b/sir20235064b.XML"},{"id":430159,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/b/images/"}],"country":"United States","state":"Illinois","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -87.33349056030417,\n              42.37444620609594\n            ],\n            [\n              -88.56395931030433,\n              42.37444620609594\n            ],\n            [\n              -88.56395931030433,\n              41.26921156456524\n            ],\n            [\n              -87.33349056030417,\n              41.26921156456524\n            ],\n            [\n              -87.33349056030417,\n              42.37444620609594\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water\" data-mce-href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>405 North Goodwin<br>Urbana, IL 61801</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Description of Study Area</li><li>Brief History of U.S. Geological Survey Peak-Flow Data Collection in Illinois</li><li>History of Statistical Analysis of Peak Streamflows</li><li>Review of Research Relating to Climatic Variability and Change</li><li>Data</li><li>Methods</li><li>Results</li><li>Discussion and Implications for Peak-Flow Frequency Analysis</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2024-06-17","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904063,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70256001,"text":"70256001 - 2024 - Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","interactions":[],"lastModifiedDate":"2024-07-12T11:58:11.727858","indexId":"70256001","displayToPublicDate":"2024-06-16T06:56:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","docAbstract":"<div id=\"143454193\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>We examine how the choice of ground‐motion‐to‐intensity conversion equations (GMICEs) in earthquake early warning (EEW) systems affects resulting alert regions. We find that existing GMICEs can underestimate observed shaking at short rupture distances or overestimate the extent of low‐intensity shaking. Updated GMICEs that remove these biases would improve the accuracy of alert regions for the ShakeAlert EEW system for the West Coast of the United States. ShakeAlert uses ground‐motion prediction equations (GMPEs), which calculate spatial distributions of peak ground acceleration (PGA) and peak ground velocity (PGV) from earthquake source estimates, combined with GMICEs to translate GMPE output into modified Mercalli intensity (MMI). We find significant epistemic uncertainty in alert distances; near‐source MMI estimates from different GMICEs can differ by over 1 MMI unit, and MMI extents used for public EEW alerts can differ by hundreds of kilometers for larger magnitude earthquakes (<strong>M</strong><span>&nbsp;</span>∼6.5+). We use a catalog of “Did You Feel It?” shaking reports to evaluate how well GMICEs predict observed shaking. Our preferred GMICE is the one that computes MMI using PGV for high intensities and transitions to using PGA for nondamaging intensities. These results motivate updating GMICE relationships more generally, including in ShakeMap applications.</p></div>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320240004","usgsCitation":"Saunders, J., Baltay Sundstrom, A.S., Minson, S.E., and Bose, M., 2024, Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions: The Seismic Record, v. 4, no. 2, p. 121-130, https://doi.org/10.1785/0320240004.","productDescription":"10 p.","startPage":"121","endPage":"130","ipdsId":"IP-163153","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":439394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320240004","text":"Publisher Index Page"},{"id":431000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunders, Jessie","contributorId":293575,"corporation":false,"usgs":false,"family":"Saunders","given":"Jessie","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":906318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":906319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bose, Maren","contributorId":222639,"corporation":false,"usgs":false,"family":"Bose","given":"Maren","email":"","affiliations":[{"id":40575,"text":"Swiss Seismological Service, Swiss Federal Institute of Technology Zürich (ETH Zürich), Zürich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":906321,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70255588,"text":"70255588 - 2024 - Conservation genetics of the endangered California Freshwater Shrimp (Syncaris pacifica): Watershed and stream networks define gene pool boundaries","interactions":[],"lastModifiedDate":"2024-09-23T16:09:53.213353","indexId":"70255588","displayToPublicDate":"2024-06-16T06:46:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Conservation genetics of the endangered California Freshwater Shrimp (Syncaris pacifica): Watershed and stream networks define gene pool boundaries","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Understanding genetic structure and diversity among remnant populations of rare species can inform conservation and recovery actions. We used a population genetic framework to spatially delineate gene pools and estimate gene flow and effective population sizes for the endangered California Freshwater Shrimp<span>&nbsp;</span><i>Syncaris pacifica</i>. Tissues of 101 individuals were collected from 11 sites in 5 watersheds, using non-lethal tissue sampling. Single Nucleotide Polymorphism markers were developed<span>&nbsp;</span><i>de novo</i><span>&nbsp;</span>using ddRAD-seq methods, resulting in 433 unlinked loci scored with high confidence and low missing data. We found evidence for strong genetic structure across the species range. Two hierarchical levels of significant differentiation were observed: (i) five clusters (regional gene pools,<span>&nbsp;</span><i>F</i><sub><i>ST</i></sub><span>&nbsp;</span>= 0.38–0.75) isolated by low gene flow were associated with watershed limits and (ii) modest local structure among tributaries within a watershed that are not connected through direct downstream flow (local gene pools,<span>&nbsp;</span><i>F</i><sub><i>ST</i></sub><span>&nbsp;</span>= 0.06–0.10). Sampling sites connected with direct upstream-to-downstream water flow were not differentiated. Our analyses suggest that regional watersheds are isolated from one another, with very limited (possibly no) gene flow over recent generations. This isolation is paired with small effective population sizes across regional gene pools (N<sub>e</sub><span>&nbsp;</span>= 62.4–147.1). Genetic diversity was variable across sites and watersheds (<i>H</i><sub><i>e</i></sub><span>&nbsp;</span>= 0.09–0.22). Those with the highest diversity may have been refugia and are now potential sources of genetic diversity for other populations. These findings highlight which portions of the species range may be most vulnerable to future habitat fragmentation and provide management consideration for maintaining local effective population sizes and genetic connectivity.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01621-x","usgsCitation":"Ada, A.M., Vandergast, A.G., Fisher, R., Fong, D., and Bohonak, A.J., 2024, Conservation genetics of the endangered California Freshwater Shrimp (Syncaris pacifica): Watershed and stream networks define gene pool boundaries: Conservation Genetics, v. 25, p. 1021-1033, https://doi.org/10.1007/s10592-024-01621-x.","productDescription":"13 p.","startPage":"1021","endPage":"1033","ipdsId":"IP-164477","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":430496,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01621-x","text":"Publisher Index Page"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -123.47340931765449,\n              38.73701805195546\n            ],\n            [\n              -123.47340931765449,\n              37.712620718207944\n            ],\n            [\n              -121.8975980490833,\n              37.712620718207944\n            ],\n            [\n              -121.8975980490833,\n              38.73701805195546\n            ],\n            [\n              -123.47340931765449,\n              38.73701805195546\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ada, Abdul M.","contributorId":339671,"corporation":false,"usgs":false,"family":"Ada","given":"Abdul","email":"","middleInitial":"M.","affiliations":[{"id":81380,"text":"San Diego State University; Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":904843,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":904844,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":904845,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fong, Darren 0000-0003-4863-470X","orcid":"https://orcid.org/0000-0003-4863-470X","contributorId":295967,"corporation":false,"usgs":false,"family":"Fong","given":"Darren","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":904846,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohonak, Andrew J.","contributorId":195156,"corporation":false,"usgs":false,"family":"Bohonak","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":904847,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70255690,"text":"70255690 - 2024 - Diel temperature signals track seasonal shifts in localized groundwater contributions to headwater streamflow generation at network scale","interactions":[],"lastModifiedDate":"2024-07-15T16:09:29.053962","indexId":"70255690","displayToPublicDate":"2024-06-16T06:02:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Diel temperature signals track seasonal shifts in localized groundwater contributions to headwater streamflow generation at network scale","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab010\" class=\"abstract author\"><div id=\"as010\"><p id=\"sp0010\">Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p &lt; 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p &lt; 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131528","usgsCitation":"Rey, D., Hare, D.K., Fair, J.H., and Briggs, M., 2024, Diel temperature signals track seasonal shifts in localized groundwater contributions to headwater streamflow generation at network scale: Journal of Hydrology, v. 639, 131528, 15 p., https://doi.org/10.1016/j.jhydrol.2024.131528.","productDescription":"131528, 15 p.","ipdsId":"IP-164580","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439396,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.131528","text":"Publisher Index Page"},{"id":430651,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Virginia","otherGeospatial":"Neversink River watershed, Shenandoah River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -74.7,\n              42\n            ],\n            [\n              -74.7,\n              41.8\n            ],\n            [\n              -74.3,\n              41.8\n            ],\n            [\n              -74.3,\n              42\n            ],\n            [\n              -74.7,\n              42\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -77.52831390102027,\n              39.27792441330183\n            ],\n            [\n              -77.83871193609114,\n              39.39785464899202\n            ],\n            [\n              -78.73093275753851,\n              38.3847187263394\n            ],\n            [\n              -79.42867821492842,\n              37.74364742887474\n            ],\n            [\n              -79.63357390597133,\n              37.49366930002196\n            ],\n            [\n              -79.43969040174417,\n              37.38807130712959\n            ],\n            [\n              -78.71956708540327,\n              38.0365993314291\n            ],\n            [\n              -77.52831390102027,\n              39.27792441330183\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"639","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":905287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hare, Danielle K. 0000-0001-7474-6727","orcid":"https://orcid.org/0000-0001-7474-6727","contributorId":304446,"corporation":false,"usgs":false,"family":"Hare","given":"Danielle","email":"","middleInitial":"K.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":905288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905289,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":905290,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70256781,"text":"70256781 - 2024 - Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions","interactions":[],"lastModifiedDate":"2024-08-26T14:53:28.721234","indexId":"70256781","displayToPublicDate":"2024-06-15T09:33:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions","docAbstract":"<p><span>Preventing the spread of aquatic invasive species is an important management action. Identifying the characteristics of lakes that are susceptible to invasion creates an opportunity for management groups to prioritize limited resources for high-risk areas. In this study, we leveraged big data from a popular fishing app and other publicly available sources of environmental and human-use exposure measurements to develop machine learning models to predict aquatic invasive species presence in 30,375 lakes in the upper Mississippi river basin of the United States. Our results predicted that an additional 665, 771, 544, 703, and 638 lakes in the basin are invaded or at high risk of invasion by Eurasian watermilfoil, curly-leaf pondweed, rusty crayfish, Chinese mystery snail, and dreissenid mussels, respectively. Lake invasions were predicted by a combination of environmental, human-use exposure, and community dynamics variables. Features that made a lake more attractive to recreationists were consistently important across our models including the presence of a boat ramp, larger lake size, and surrounding natural landscape. The importance of co-occurring invasive species in some models could reflect several scenarios including invasional meltdown, facilitation among species, similar pathways for introduction, or similar response to the environment. Our models predicted a higher proportion of invasions in less popular lakes compared to known invasions. The finding underscores the potential importance of less popular lakes in the invasion process and suggests that the detection of invasions may be lower in these lakes. These results serve as a valuable tool for data-driven management decisions and can provide actionable insights for effective aquatic invasive species management.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03367-6","usgsCitation":"Weir, J.L., Daniel, W., Hyder, K., Skov, C., and Venturelli, P.A., 2024, Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions: Biological Invasions, v. 26, p. 3163-3178, https://doi.org/10.1007/s10530-024-03367-6.","productDescription":"16 p.","startPage":"3163","endPage":"3178","ipdsId":"IP-162115","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":432144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weir, Jessica L.","contributorId":330438,"corporation":false,"usgs":false,"family":"Weir","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219312,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyder, Kieran","contributorId":291284,"corporation":false,"usgs":false,"family":"Hyder","given":"Kieran","email":"","affiliations":[{"id":62658,"text":"The Centre for Environment, Fisheries and Aquaculture Science (Cefas) and Collaborative Centre for Sustainable Use of the Seas (CCSUS), School of Environmental Sciences, University of East Anglia, Norwich Research Park","active":true,"usgs":false}],"preferred":false,"id":908935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skov, Christian","contributorId":268055,"corporation":false,"usgs":false,"family":"Skov","given":"Christian","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":908936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Venturelli, Paul A.","contributorId":171477,"corporation":false,"usgs":false,"family":"Venturelli","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70256119,"text":"70256119 - 2024 - Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","interactions":[],"lastModifiedDate":"2024-07-23T20:23:11.322287","indexId":"70256119","displayToPublicDate":"2024-06-15T09:11:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples.  The variability in egg ages in virtual samples compare well to field samples.  The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 kilometers upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102376","usgsCitation":"Soong, D., Jackson, P.R., Kocovsky, P.M., Morrison, L., Garcia, T., Santacruz, S., Chen, C., Zhu, Z., and Embke, H.S., 2024, Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model: Journal of Great Lakes Research, v. 50, no. 4, 102376, 14 p., https://doi.org/10.1016/j.jglr.2024.102376.","productDescription":"102376, 14 p.","ipdsId":"IP-157787","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439399,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102376","text":"Publisher Index Page"},{"id":431354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83,\n              41.5\n            ],\n            [\n              -83.25,\n              41.5\n            ],\n            [\n              -83.25,\n              41.25\n            ],\n            [\n              -83,\n              41.25\n            ],\n            [\n              -83,\n              41.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":906762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Lori","contributorId":340259,"corporation":false,"usgs":false,"family":"Morrison","given":"Lori","email":"","affiliations":[{"id":81526,"text":"Alaska Water Resources","active":true,"usgs":false}],"preferred":false,"id":906763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Tatiana","contributorId":340260,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":81527,"text":"AquaIntel Inc.","active":true,"usgs":false}],"preferred":false,"id":906764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santacruz, Santiago","contributorId":340261,"corporation":false,"usgs":false,"family":"Santacruz","given":"Santiago","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":906765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Cindy","contributorId":340262,"corporation":false,"usgs":false,"family":"Chen","given":"Cindy","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":906766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhenduo","contributorId":340263,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","affiliations":[{"id":81528,"text":"Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":906767,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":906768,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70260439,"text":"70260439 - 2024 - Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","interactions":[],"lastModifiedDate":"2024-11-01T13:35:38.261218","indexId":"70260439","displayToPublicDate":"2024-06-15T08:26:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","docAbstract":"<p><span>According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope,&nbsp;</span><i>m</i><span>*. We further identify site-specific&nbsp;</span><i>m</i><span>* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70254922,"text":"sir20245027 - 2024 - Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","interactions":[],"lastModifiedDate":"2026-02-03T18:16:49.214434","indexId":"sir20245027","displayToPublicDate":"2024-06-14T15:39:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5027","displayTitle":"Accuracy Assessment of Three-Dimensional Point Cloud Data Collected With a Scanning Total Station on Shinnecock Nation Tribal Lands in Suffolk County, New York","title":"Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","docAbstract":"<p>A combined point cloud of about 85.6 million points was collected during 27 scans of a section of the western shoreline along the Shinnecock Peninsula of Suffolk County, New York, to document baseline geospatial conditions during July and October 2022 using a scanning total station. The three-dimensional accuracy of the combined point cloud is assessed to identify potential systematic error sources associated with the surveying equipment and the novel methodology used to collect and field-register (data are oriented and aligned in real time) point cloud data. The accuracy of the combined point cloud was assessed in terms of relative and absolute reference frames. Relative accuracy provides a measure of error within the local coordinate system and is determined by combining the uncertainty associated with the position of the scan station (the point being occupied by the scanning total station during the scan), the uncertainty associated with the position of the network control points, and the uncertainty associated with the laser of the scanning total station. Assessment of the absolute accuracy includes these three potential error sources combined with the uncertainty associated with the geodetic coordinates to which the local control network is referenced. The combined overall relative horizontal and vertical accuracy of the point cloud is 0.0156 and 0.0241 meter, respectively, at the 95 percent confidence level. The combined overall absolute horizontal and vertical accuracy of the point cloud is 0.0598 and 0.0733 meter, respectively, at the 95 percent confidence level.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245027","collaboration":"Prepared in cooperation with the Shinnecock Nation and the Federal Emergency Management Agency","usgsCitation":"Noll, M.L., Capurso, W.D., and Chu, A., 2024, Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York: U.S. Geological Survey Scientific Investigations Report 2024–5027, 23 p., https://doi.org/10.3133/sir20245027.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153251","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":429787,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5027 XML"},{"id":429784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5027/coverthb.jpg"},{"id":429785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.pdf","text":"Report","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5027 PDF"},{"id":429786,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245027/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5027 HTML"},{"id":429788,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/"},{"id":429789,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OG0AAO","text":"USGS data release","linkHelpText":"Three-dimensional point cloud data collected with a scanning total station on the western shoreline of the Shinnecock Nation Tribal lands, Suffolk County, New York, 2022"},{"id":429861,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig02.png","text":"Figure 2","size":"3.20 MB","linkHelpText":"- Map showing the study area where three-dimensional point cloud data were collected with a scanning total station along the western shoreline of the Shinnecock Peninsula in Suffolk County, New York, for a point cloud accuracy assessment"},{"id":429862,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig03.png","text":"Figure 3","size":"3.25 MB","linkHelpText":"- Map showing estimated position of the shoreline after sea-level rise of about 0.46 meter (m) within the study area on the Shinnecock Nation Tribal lands in Suffolk County, New York, using a conservative model projection for 2050"},{"id":429863,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig04.png","text":"Figure 4","size":"1.76 MB","linkHelpText":"- Graphical representation of the point cloud of <em>A</em>, the study area in plan view, <em>B</em>, the coastal spit in plan view, and <em>C</em>, the dune adjacent to the Tribal cemetery on the Shinnecock Nation Tribal lands in Suffolk County, New York, in section view in July 2022"},{"id":499455,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117076.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Suffolk County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -72.67660903513142,\n              41.00419828031659\n            ],\n            [\n              -72.67660903513142,\n              40.789306473711775\n            ],\n            [\n              -72.23164172630058,\n              40.789306473711775\n            ],\n            [\n              -72.23164172630058,\n              41.00419828031659\n            ],\n            [\n              -72.67660903513142,\n              41.00419828031659\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/ny-water\" data-mce-href=\"https://www.usgs.gov/centers/ny-water\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods of Investigation</li><li>Accuracy Assessment</li><li>Discussion of Error</li><li>Conclussion</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-06-14","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Noll, Michael L. 0000-0003-2050-3134 mnoll@usgs.gov","orcid":"https://orcid.org/0000-0003-2050-3134","contributorId":4652,"corporation":false,"usgs":true,"family":"Noll","given":"Michael","email":"mnoll@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capurso, William D. 0000-0003-1182-2846","orcid":"https://orcid.org/0000-0003-1182-2846","contributorId":218672,"corporation":false,"usgs":true,"family":"Capurso","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chu, Anthony 0000-0001-8623-2862 achu@usgs.gov","orcid":"https://orcid.org/0000-0001-8623-2862","contributorId":2517,"corporation":false,"usgs":true,"family":"Chu","given":"Anthony","email":"achu@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70255309,"text":"ofr20241019 - 2024 - Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2019–2021","interactions":[],"lastModifiedDate":"2026-01-29T19:35:27.171984","indexId":"ofr20241019","displayToPublicDate":"2024-06-14T14:48:59","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1019","displayTitle":"Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2019–2021","title":"Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2019–2021","docAbstract":"<p>The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year, depending on location.</p><p>The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents the results of data collected as part of the monitoring program in the Black Mesa area from calendar years 2020–2021 and, additionally, uses streamflow statistics from November and December 2019. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.</p><p>In calendar year 2020, total groundwater withdrawals were estimated to be 2,680 acre-feet (acre-ft), and, in 2021, total withdrawals were estimated to be 2,570 acre-ft. Total withdrawals during 2021 were about 65 percent less than total withdrawals in 2005 because the Peabody Western Coal Company discontinued its use of water to transport coal in a coal slurry pipeline after 2005 and ceased mining operations in 2019.</p><p>Owing to Navajo Nation and Hopi Reservation access restrictions during the Coronavirus pandemic, water levels were not collected from municipal wells in 2020 or 2021. Water levels measured in 2021 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 7 of 13 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 13 wells ranged from +8.4 feet (ft) to −42.4 ft, and the median change was −0.4 ft. Water levels also showed decline in 11 of 12 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −25.9 ft, with changes across all 12 wells ranging from +17.3 ft to −133.7 ft.</p><p>Spring flow was measured at four springs between 2020 and 2021. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p&lt;0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso, Arizona. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s, and discharge at Pasture Canyon Spring has fluctuated for the period of record.</p><p>Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976–2021), Dinnebito Wash near Sand Springs 09401110 (1993–2020), Polacca Wash near Second Mesa 09400568 (1994–2020), and Pasture Canyon Springs 09401265 (2004–2021). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was observed at Moenkopi Wash and Dinnebito Wash.</p><p>In 2020 and 2021, water samples were collected from a total of four springs in the Black Mesa area and analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids and sulfate at Pasture Canyon Spring have not varied significantly (p&gt;0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. However, concentrations of chloride from Pasture Canyon Spring show a diminishing trend. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids at Burro Spring have varied for the period of record, but there is no statistical trend in the data. However, concentrations of chloride and sulfate from Burro Spring show a trend towards lower concentrations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241019","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2024, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2019–2021: U.S. Geological Survey Open-File Report 2024–1019, 47 p., https://doi.org/10.3133/ofr20241019.","productDescription":"vii, 48 p.","onlineOnly":"Y","ipdsId":"IP-148316","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":430241,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1019/images"},{"id":430240,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.xml"},{"id":430239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1019/covrthb.jpg"},{"id":430242,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241019/full"},{"id":499245,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117071.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.50769351138865,\n              36.993810314532595\n            ],\n            [\n              -111.50769351138865,\n              35.29946810356502\n            ],\n            [\n              -109.33240054263857,\n              35.29946810356502\n            ],\n            [\n              -109.33240054263857,\n              36.993810314532595\n            ],\n            [\n              -111.50769351138865,\n              36.993810314532595\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/arizona-water-science-center/connect\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/arizona-water-science-center/connect\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/az-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/az-water\">Arizona Water Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Description of Study Area</li><li>Hydrologic Data</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2024-06-14","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Mason, Jon P. 0000-0003-0576-5494","orcid":"https://orcid.org/0000-0003-0576-5494","contributorId":215822,"corporation":false,"usgs":true,"family":"Mason","given":"Jon P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904230,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70254603,"text":"fs20243004 - 2024 - The U.S. Geological Survey Ohio Water Microbiology Laboratory","interactions":[],"lastModifiedDate":"2024-06-14T10:48:52.206281","indexId":"fs20243004","displayToPublicDate":"2024-06-14T06:10:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3004","displayTitle":"The U.S. Geological Survey Ohio Water Microbiology Laboratory","title":"The U.S. Geological Survey Ohio Water Microbiology Laboratory","docAbstract":"<p>The U.S. Geological Survey Ohio Water Microbiology Laboratory is a part of the Ohio-Kentucky-Indiana Water Science Center. The mission of the laboratory is to provide microbiological data of public health significance from surface waters, groundwaters, and sediments for a variety of study objectives. The laboratory conducts internal projects, works with external cooperators, and assists U.S. Geological Survey offices and National programs. The laboratory offers guidance, study design, and data interpretation expertise to collaborators, all following rigorous quality control and quality assurance procedures.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243004","usgsCitation":"Lanier, B.M., Brady, A.M.G., Cicale, J.R., Kephart, C.M., Lynch, L.D., Schroeder, M.W., and Stelzer, E.A., 2024, The U.S. Geological Survey Ohio Water Microbiology Laboratory: U.S. Geological Survey Fact Sheet 2024–3004, 4 p., https://doi.org/10.3133/fs20243004.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158056","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":429504,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3004/images/"},{"id":429503,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3004 XML"},{"id":429502,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243004/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3004 HTML"},{"id":429501,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3004 PDF"},{"id":429500,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3004/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a>,<br><a href=\"https://www.usgs.gov/labs/ohio-water-microbiology-laboratory\" data-mce-href=\"https://www.usgs.gov/labs/ohio-water-microbiology-laboratory\">Ohio Water Microbiology Laboratory</a><br>U.S. Geological Survey<br>6460 Busch Blvd, Suite 100<br>Columbus, OH 43229</p><p>Laboratory email: <a href=\"mailto:gs-w-ohclb_owml@usgs.gov\" data-mce-href=\"mailto:gs-w-ohclb_owml@usgs.gov\">gs-w-ohclb_owml@usgs.gov</a></p>","tableOfContents":"<ul><li>Get To Know the Ohio Water Microbiology Laboratory</li><li>Laboratory Capabilities</li><li>Indicators of Fecal Contamination</li><li>Microbial Source Tracking</li><li>Environmental Deoxyribonucleic Acid (eDNA) Detection</li><li>Cyanobacterial Harmful Algal Blooms</li><li>Taste and Odor Issues in Water</li><li>Predictive Modeling</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-06-14","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Lanier, Braden M. 0000-0003-3356-8850","orcid":"https://orcid.org/0000-0003-3356-8850","contributorId":337140,"corporation":false,"usgs":true,"family":"Lanier","given":"Braden","email":"","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M.G. 0000-0002-7414-0992 amgbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-7414-0992","contributorId":2544,"corporation":false,"usgs":true,"family":"Brady","given":"Amie","email":"amgbrady@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cicale, Jessica R. 0000-0002-0008-4051","orcid":"https://orcid.org/0000-0002-0008-4051","contributorId":208157,"corporation":false,"usgs":true,"family":"Cicale","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902064,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Lauren D. 0000-0003-0209-1797","orcid":"https://orcid.org/0000-0003-0209-1797","contributorId":337141,"corporation":false,"usgs":true,"family":"Lynch","given":"Lauren","email":"","middleInitial":"D.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schroeder, Maxim W. 0000-0002-8625-4540","orcid":"https://orcid.org/0000-0002-8625-4540","contributorId":337142,"corporation":false,"usgs":true,"family":"Schroeder","given":"Maxim","email":"","middleInitial":"W.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902067,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902068,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70255030,"text":"ofr20241024 - 2024 - Neotropical migratory bird monitoring study at Marine Corps Base Camp Pendleton, California—2021 annual data summary","interactions":[],"lastModifiedDate":"2024-06-13T13:30:39.085831","indexId":"ofr20241024","displayToPublicDate":"2024-06-12T11:20:33","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1024","displayTitle":"Neotropical Migratory Bird Monitoring Study at Marine Corps Base Camp Pendleton, California—2021 Annual Data Summary","title":"Neotropical migratory bird monitoring study at Marine Corps Base Camp Pendleton, California—2021 annual data summary","docAbstract":"<h1>Executive Summary</h1><p>Two Monitoring Avian Productivity and Survivorship (MAPS) stations were operated at Marine Corps Base Camp Pendleton (MCBCP), California, in 2021: one at De Luz Creek and one at the Santa Margarita River. The stations were established to provide data on Neotropical migratory birds at MCBCP to support the dual missions of environmental stewardship and military readiness.</p><p>A total of 1,227 individual birds were captured in 2021 between the two stations: 395 at De Luz and 832 at Santa Margarita (both 15 banding days). Of these 1,227 individuals captured, 955 were newly banded (273 at De Luz and 682 at Santa Margarita), 150 were recaptures banded before 2021 (28 at De Luz and 122 at Santa Margarita, excluding recaptures released before reading band number [1 at De Luz and 3 at Santa Margarita]), and 118 were unbanded (93 at De Luz and 25 at Santa Margarita). Return rate in 2021 was much lower than the annual mean at De Luz (1995–2019) and similar to the annual mean at the Santa Margarita station (1998–2020). The sex ratio of known-sex adult birds was skewed toward males at both stations in 2021.</p><p>Species richness was similar at De Luz from 2019 to 2021, increased at Santa Margarita from 2020 to 2021 and was above annual means at both sites (1995–2019 and 1998–2020, respectively). The most abundant species at De Luz were Wrentit (<i>Chamaea fasciata</i>) and Allen’s Hummingbird (<i>Selasphorus sasin</i>). Song Sparrow (<i>Melospiza melodia</i>) and Common Yellowthroat (<i>Geothlypis trichas</i>) were most abundant at Santa Margarita.</p><p>Since 2002, we have examined the population trends of 12 species at De Luz and 13 species at Santa Margarita for which numbers of known-age individuals were adequate for statistical analysis. We estimated population size and calculated indices of productivity and survival for a subset of these species with sufficient captures and recaptures for valid parameter estimation—four at De Luz and six at Santa Margarita. We determined that in 2021, abundance of 42 percent (5 of 12) of focal species at De Luz and 38 percent (5 of 13) of focal species at Santa Margarita was below the annual mean abundance. Of the focal species below mean abundance, 40 percent (2 of 5) at De Luz and 60 percent (3 of 5) at Santa Margarita were migrant populations. Of the focal species, 25 (3 of 12) percent at De Luz and 31 percent (4 of 13) at Santa Margarita had declining population trends during the span of station operation. With few exceptions, these declines appeared to be associated with conditions on the breeding grounds.</p><p>Annual productivity (calculated as the ratio of juveniles to adults among individual captures) was zero for all focal species at De Luz in 2021. At Santa Margarita, productivity increased from year 2020 to 2021 for Common Yellowthroat, Song Sparrow, and Yellow Warbler (<i>Setophaga petechia</i>) and declined from year 2020 to 2021 for Least Bell’s Vireo (<i>Vireo bellii pusillus</i>), but productivity was above the 1998–2020 mean for all four species, whereas productivity was maintained for Orange-crowned Warbler (<i>Leiothlypis celata</i>) and Yellow-breasted Chat (<i>Icteria virens</i>). Winter precipitation affected productivity of Black-headed Grosbeak (<i>Pheucticus melanocephalus</i>), Common Yellowthroat, and Song Sparrow at De Luz and affected productivity of Common Yellowthroat, Orange-crowned Warbler, Song Sparrow, Yellow-breasted Chat, and Yellow Warbler at Santa Margarita.</p><p>We calculated the mean annual adult survival for 1998–2020 at Santa Margarita, excluding years when the station was not operated. Survival could not be calculated for De Luz in 2021 because the station was not operated in 2020. Model-averaged annual adult survival ranged from 42 to 66 percent for residents and from 30 to 66 percent for migrants at Santa Margarita. Survival of Common Yellowthroat, Song Sparrow, and possibly Yellow Warbler was found to be affected by winter precipitation. Sex was a significant predictor of survival for Common Yellowthroat, Least Bell’s Vireo, Orange-crowned Warbler, and Yellow-breasted Chat at Santa Margarita, where females were found to have lower survival than males.</p><p>At Santa Margarita, multiple regression analyses examining adult survival and productivity as predictors of future population size indicated that resident Song Sparrow and migrant Yellow Warbler populations were affected by population size from the previous year, migrant Yellow-breasted Chat populations were affected by productivity from the previous year, and migrant Orange-Crowned Warbler populations were affected by survival from the previous year. Updated previous-year population size predictions could not be calculated for De Luz because the station was not operated in 2020.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241024","collaboration":"Prepared in cooperation with Assistant Chief of Staff, Environmental Security, U.S. Marine Corps Base Camp Pendleton","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Mendia, S.M., and Kus, B.E., 2024, Neotropical migratory bird monitoring study at Marine Corps Base Camp Pendleton, California—2021 annual data summary: U.S. Geological Survey Open-File Report 2024–1024, 69 p., https://doi.org/10.3133/ofr20241024.","productDescription":"viii, 69 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-155196","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":429918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1024/covrthb.jpg"},{"id":429919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429920,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.xml"},{"id":429921,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1024/images"},{"id":429922,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241024/full"}],"country":"United States","state":"California","otherGeospatial":"Marine Corps Base Camp Pendleton","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.38,\n              33.275\n            ],\n            [\n              -117.38,\n              33.26\n            ],\n            [\n              -117.35,\n              33.26\n            ],\n            [\n              -117.35,\n              33.275\n            ],\n            [\n              -117.38,\n              33.275\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.33,\n              33.38\n            ],\n            [\n              -117.33,\n              33.37\n            ],\n            [\n              -117.32,\n              33.37\n            ],\n            [\n              -117.32,\n              33.38\n            ],\n            [\n              -117.33,\n              33.38\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/werc\">Western Ecological Research Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgements</li><li>Executive Summary</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1. Alpha Codes, Common Names, and Scientific Names for Species Captured at De Luz Creek and Santa Margarita River, Camp Pendleton, California</li><li>Appendix 2. Tables for De Luz Creek, Camp Pendleton, California</li><li>Appendix 3. Tables for Santa Margarita River, Camp Pendleton, California</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-06-12","noUsgsAuthors":false,"publicationDate":"2024-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mendia, Shannon M. 0000-0003-4520-7024 smendia@usgs.gov","orcid":"https://orcid.org/0000-0003-4520-7024","contributorId":223097,"corporation":false,"usgs":true,"family":"Mendia","given":"Shannon","email":"smendia@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":903172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":903173,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70256219,"text":"70256219 - 2024 - An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape","interactions":[],"lastModifiedDate":"2024-07-29T13:58:25.97437","indexId":"70256219","displayToPublicDate":"2024-06-12T08:41:02","publicationYear":"2024","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":"An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape","docAbstract":"<p><span>Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs16122122","usgsCitation":"Petrakis, R., Norman, L., Villarreal, M.L., Senay, G.B., Friedrichs, M., Cassassuce, F., Gomis, F., and Nagler, P.L., 2024, An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape: Remote Sensing, v. 16, no. 12, 2122, 35 p.; Data Release, https://doi.org/10.3390/rs16122122.","productDescription":"2122, 35 p.; Data Release","ipdsId":"IP-160120","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":439410,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16122122","text":"Publisher Index Page"},{"id":434943,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBXG2R","text":"USGS data release","linkHelpText":"Monthly Ensemble Mean Evapotranspiration (EMET) Product for the Los Planes basin in Baja California Sur, Mexico from January 2006 through December 2021: U.S. Geological Survey Data Release"},{"id":431560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California Sur","otherGeospatial":"Los Planes Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -110.15,\n              24.185882621902465\n            ],\n            [\n              -110.15,\n              23.666\n            ],\n            [\n              -109.796162654228,\n              23.666\n            ],\n            [\n              -109.796162654228,\n           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Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":907135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":907136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":907137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":907138,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cassassuce, Florance","contributorId":337023,"corporation":false,"usgs":false,"family":"Cassassuce","given":"Florance","email":"","affiliations":[{"id":80952,"text":"Rancho Ancon","active":true,"usgs":false}],"preferred":false,"id":907139,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomis, Florent","contributorId":337024,"corporation":false,"usgs":false,"family":"Gomis","given":"Florent","email":"","affiliations":[{"id":80952,"text":"Rancho Ancon","active":true,"usgs":false}],"preferred":false,"id":907140,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":907141,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70255312,"text":"70255312 - 2024 - Source, migration pathways, and atmospheric release of geologic methane associated with the complex permafrost regimes of the outer Mackenzie River Delta, Arctic, Canada","interactions":[],"lastModifiedDate":"2024-06-17T12:07:59.713806","indexId":"70255312","displayToPublicDate":"2024-06-12T07:06:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Source, migration pathways, and atmospheric release of geologic methane associated with the complex permafrost regimes of the outer Mackenzie River Delta, Arctic, Canada","docAbstract":"<div class=\"article-section__content en main\"><p>Sources and fluxes of methane to the atmosphere from permafrost are significant but poorly constrained in global climate models. We present data collected from the variable permafrost setting of the outer Mackenzie River Delta, including observations of aquatic methane seepage, core determinations of in situ methane occurrence and seep gas isotope geochemistry. The sources and locations of in situ geologic methane occurrence and aquatic and atmospheric gas release appear to be controlled by the regional geology and permafrost conditions. Where permafrost is &gt;250&nbsp;m thick, thermogenic gas deposits at depth are isolated by laterally continuous, low permeability ice-bearing sediments with few through-going thawed taliks. Thus, the observed in situ methane and aquatic gas seepage appears to be dominated by microbial methane. In contrast, where permafrost is &lt;80&nbsp;m thick, taliks are more likely to be through-going, providing permeable conduits from depth and migration pathways for both thermogenic and biogenic gas. Continuous annual fluid sampling of two lakes and a river channel documents aquatic methane flux from microbial sources, more deeply buried thermogenic sources, and mixtures of both. Using estimates of in situ methane concentration from deep core samples and observations of in situ free gas occurrences, we conclude that the reservoir of in situ geologic methane within ice bonded permafrost is substantial and that this methane is presently migrating with ongoing atmospheric release. It is our assessment that the permafrost setting, and processes described are sensitive to future climate change as the permafrost warms.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JF007515","usgsCitation":"Dallimore, S., Lapham, L., Cote, M., Bowen, R., MacLeod, R., Marcek, H., Wheat, C.G., and Collett, T., 2024, Source, migration pathways, and atmospheric release of geologic methane associated with the complex permafrost regimes of the outer Mackenzie River Delta, Arctic, Canada: Journal of Geophysical Research, v. 129, no. 6, e2023JF007515, 19 p., https://doi.org/10.1029/2023JF007515.","productDescription":"e2023JF007515, 19 p.","ipdsId":"IP-158764","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":439411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jf007515","text":"Publisher Index Page"},{"id":430265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Mackenzie River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -138.53790459926796,\n              69.49821513283834\n            ],\n            [\n              -138.53790459926796,\n              66.68151109301039\n            ],\n            [\n              -130.9353655367677,\n              66.68151109301039\n            ],\n            [\n              -130.9353655367677,\n              69.49821513283834\n            ],\n            [\n              -138.53790459926796,\n              69.49821513283834\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Dallimore, Scott","contributorId":208634,"corporation":false,"usgs":false,"family":"Dallimore","given":"Scott","email":"","affiliations":[{"id":37855,"text":"Geological Survey of Canada Pacific Vancouver, Vancouver, British Columbia, CANADA","active":true,"usgs":false}],"preferred":false,"id":904247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lapham, Laura","contributorId":296666,"corporation":false,"usgs":false,"family":"Lapham","given":"Laura","affiliations":[{"id":64119,"text":"Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, MD","active":true,"usgs":false}],"preferred":false,"id":904248,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cote, Michelle","contributorId":339433,"corporation":false,"usgs":false,"family":"Cote","given":"Michelle","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":904249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowen, Robert","contributorId":339434,"corporation":false,"usgs":false,"family":"Bowen","given":"Robert","email":"","affiliations":[{"id":81299,"text":"Diversified Scientific Solutions","active":true,"usgs":false}],"preferred":false,"id":904250,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacLeod, Roger","contributorId":339435,"corporation":false,"usgs":false,"family":"MacLeod","given":"Roger","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":904251,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marcek, Hadley","contributorId":339436,"corporation":false,"usgs":false,"family":"Marcek","given":"Hadley","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":904252,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wheat, C. Geoffrey","contributorId":298540,"corporation":false,"usgs":false,"family":"Wheat","given":"C.","email":"","middleInitial":"Geoffrey","affiliations":[{"id":36971,"text":"University of Alaska","active":true,"usgs":false}],"preferred":false,"id":904253,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":904254,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70255332,"text":"70255332 - 2024 - Chlorophyll a in lakes and streams of the United States (2005–2022)","interactions":[],"lastModifiedDate":"2024-06-17T12:02:05.380493","indexId":"70255332","displayToPublicDate":"2024-06-12T06:59:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17083,"text":"Nature Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Chlorophyll a in lakes and streams of the United States (2005–2022)","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>The concentration of chlorophyll<span>&nbsp;</span><i>a</i><span>&nbsp;</span>in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41597-024-03453-3","usgsCitation":"Spaulding, S., Platt, L., Murphy, J.C., Covert, S.A., and Harvey, J., 2024, Chlorophyll a in lakes and streams of the United States (2005–2022): Nature Scientific Data, v. 11, 611, 12 p., https://doi.org/10.1038/s41597-024-03453-3.","productDescription":"611, 12 p.","ipdsId":"IP-159954","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":439412,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-024-03453-3","text":"Publisher Index Page"},{"id":430263,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -129.38257820277917,\n              51.66913995584653\n            ],\n            [\n              -129.38257820277917,\n              24.282007345034003\n            ],\n            [\n              -65.04664070277936,\n              24.282007345034003\n            ],\n            [\n              -65.04664070277936,\n              51.66913995584653\n            ],\n            [\n              -129.38257820277917,\n              51.66913995584653\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2024-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Platt, Lindsay R.C.","contributorId":339455,"corporation":false,"usgs":false,"family":"Platt","given":"Lindsay R.C.","affiliations":[{"id":81301,"text":"Consortium of Universities for Advancement of Hydrologic Science, Inc. (CUAHSI)","active":true,"usgs":false}],"preferred":false,"id":904302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":4281,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Covert, S. Alex 0000-0001-5981-1826","orcid":"https://orcid.org/0000-0001-5981-1826","contributorId":207179,"corporation":false,"usgs":true,"family":"Covert","given":"S.","email":"","middleInitial":"Alex","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904305,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70255028,"text":"sim3517 - 2024 - Colored shaded relief bathymetry and acoustic backscatter of Ozette Lake, Washington","interactions":[],"lastModifiedDate":"2026-01-29T21:47:59.578406","indexId":"sim3517","displayToPublicDate":"2024-06-11T14:24:55","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3517","displayTitle":"Colored Shaded Relief Bathymetry and Acoustic Backscatter of Ozette Lake, Washington","title":"Colored shaded relief bathymetry and acoustic backscatter of Ozette Lake, Washington","docAbstract":"<p>Offshore of the Pacific Northwest of the United States is the Cascadia Subduction Zone, a 1,000-kilometer-long tectonic boundary defined by a large fault, called a megathrust, that extends from the Mendocino Junction off northern California to the Nootka Fracture Zone off Vancouver Island, Canada (U.S. Geological Survey, 2023). The Juan de Fuca and Gorda oceanic plates to the west of this boundary subduct under the North America continental plate to the east. Several other smaller faults that cut through the North America plate crust also affect the region. Although their effects upon Ozette Lake are uncertain, geological evidence for past earthquakes, such as underwater landslides, may be found in Pacific Northwest lakes.</p><p>Underwater landslides caused by past earthquakes should be well preserved in these relatively undisturbed lake environments. The floor of Ozette Lake, Washington, located along the Pacific coast of the United States, west of the Puget Sound region and about 140 kilometers east of the megathrust was mapped by the U.S. Geological Survey in July of 2019 to search for evidence of past earthquakes. Mapping was completed using a SWATHplus-M 234-kHz interferometric side scan sonar system pole-mounted on the U.S. Geological Survey research vessel San Lorenzo. The system collected full-coverage bathymetric and acoustic backscatter data that were processed to 2-meter spatial resolution (Dartnell and others, 2024). This two-map series displays the results of this mapping. A <a data-mce-href=\"../sim/3517/sim3517_sheet1.pdf\" href=\"../sim/3517/sim3517_sheet1.pdf\" target=\"_blank\" rel=\"noopener\">colored shaded relief bathymetry map (sheet 1)</a> and an <a data-mce-href=\"../sim/3517/sim3517_sheet2.pdf\" href=\"../sim/3517/sim3517_sheet2.pdf\" target=\"_blank\" rel=\"noopener\">acoustic backscatter map (sheet 2)</a> show the lake floor morphology and backscatter intensities, respectively, that can be analyzed for evidence of past earthquakes.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3517","usgsCitation":"Dartnell, P., Brothers, D., Ritchie, A.C., Sherrod, B., Currie, J.E., Dal Ferro, P., and Powers, D.C., 2024, Colored shaded relief bathymetry and acoustic backscatter of Ozette Lake, Washington: U.S. Geological Survey Scientific Investigations Map 3517, 2 sheets, scale 1:18,000, https://doi.org/10.3133/sim3517.","productDescription":"2 Sheets: 27.81 × 37.40 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-155366","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":429905,"rank":1,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3517/sim3517_sheet1.pdf","text":"Sheet 1","size":"45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Colored shaded relief bathymetry map"},{"id":429906,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3517/sim3517_sheet2.pdf","text":"Sheet 2","size":"50 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Acoustic backscatter map"},{"id":429912,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3517/covrthb.jpg"},{"id":499286,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117068.htm","linkFileType":{"id":5,"text":"html"}},{"id":429913,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U91FSB","text":"USGS Data Release","description":"Dartnell, P., Brothers, D.S., Ritchie, A.C.,Sherrod, B., Currie, J.E., Dal Ferro, P., Powers, D.C., 2024, Bathymetry and acoustic-backscatter data for Ozette Lake, Washington collected during USGS field activity 2019-622-FA: U.S. Geological Survey data release, https://doi.org/10.5066/P9U91FSB.","linkHelpText":"Bathymetry and acoustic-backscatter data for Ozette Lake, Washington collected during USGS field activity 2019-622-FA"}],"country":"United States","state":"Washington","otherGeospatial":"Ozette Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -124.7303438621736,\n              48.18265809125998\n            ],\n            [\n              -124.7303438621736,\n              48.025264988649184\n            ],\n            [\n              -124.54883551443689,\n              48.025264988649184\n            ],\n            [\n              -124.54883551443689,\n              48.18265809125998\n            ],\n            [\n              -124.7303438621736,\n              48.18265809125998\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/pcmsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/pcmsc\">Pacific Coastal and Marine Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>2885 Mission St.<br>Santa Cruz, CA 95060</p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2024-06-11","noUsgsAuthors":false,"publicationDate":"2024-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dartnell, Peter 0000-0002-9554-729X","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":208208,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":210199,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ritchie, Andrew C. 0000-0001-5826-9983","orcid":"https://orcid.org/0000-0001-5826-9983","contributorId":333630,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":903164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Currie, Jackson E. 0000-0001-9182-4408","orcid":"https://orcid.org/0000-0001-9182-4408","contributorId":337004,"corporation":false,"usgs":true,"family":"Currie","given":"Jackson","email":"","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dal Ferro, Peter 0000-0002-3448-0204 pdalferro@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-0204","contributorId":240034,"corporation":false,"usgs":true,"family":"Dal Ferro","given":"Peter","email":"pdalferro@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powers, Daniel C. 0000-0002-3215-2151","orcid":"https://orcid.org/0000-0002-3215-2151","contributorId":240040,"corporation":false,"usgs":true,"family":"Powers","given":"Daniel","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":903167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70254789,"text":"ofr20241031 - 2024 - Environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2021","interactions":[],"lastModifiedDate":"2026-01-29T19:44:39.237909","indexId":"ofr20241031","displayToPublicDate":"2024-06-11T13:55:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1031","displayTitle":"Environmental Monitoring of Groundwater, Surface Water, and Soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2021","title":"Environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2021","docAbstract":"<p>Letterkenny Army Depot in Chambersburg, Pennsylvania, built an Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility in 2016 to centralize rocket motor destruction and contain all waste during the destruction process. The U.S. Geological Survey has collected environmental samples from groundwater, surface water, and soils at ARMD since 2016.</p><p>During 2021, samples were collected from four groundwater wells in September, one surface-water site in October, and five soil sites in November near the facility. Samples were analyzed for nutrients, trace metals, major ions, total volatile organic compounds, and perchlorate. Perchlorate was not detected in any 2021 samples.</p><p>Groundwater results showed no constituents exceeded any U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL). Dissolved arsenic (As) was detected in one well above the reporting detection level (RDL) of 3 micrograms per liter (μg/L) at 5.4 μg/L but below its MCL of 10 μg/L. Dissolved iron (Fe) was the only inorganic constituent measured above an EPA secondary maximum contaminant level (SMCL). All groundwater samples collected in 2021 exceeded the Fe SMCL of 300 μg/L, with concentrations ranging from 390 μg/L to 3,500 μg/L.</p><p>Surface-water data collected during 2021 showed no measured constituents in the surface-water sample that exceeded any EPA MCL or SMCL.</p><p>Soil samples collected from 2016 through 2021 showed all concentrations of As exceeded the EPA soil screening levels of 3 milligrams per kilogram (mg/kg) but did not exceed the Pennsylvania medium-specific concentrations for As of 61 mg/kg. Arsenic concentrations in 2021 ranged from 9.1 mg/kg to 12.9 mg/kg.</p><p>The 2021 results for the ARMD Facility indicate no increases in concentrations of reported compounds compared to data from 2016 to 2020. The contained burn treatment facility for demilitarization of rocket motors during 2021 appears to have operated without elevating concentrations of target compounds compared to previous years.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241031","collaboration":"Prepared in Cooperation with the Letterkenny Army Depot","usgsCitation":"Galeone, D.G., and Donmoyer, S.J., 2024, Environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2021: U.S. Geological Survey Open-File Report 2024–1031, 31 p., https://doi.org/10.3133/ofr20241031","productDescription":"Report: vii, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148346","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":499252,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117074.htm","linkFileType":{"id":5,"text":"html"}},{"id":429681,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1031/ofr20241031.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1031 XML"},{"id":429679,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YIATZ","text":"USGS data release","linkHelpText":"Groundwater, surface water, and soil data collected near and at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania"},{"id":429680,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1031/images/"},{"id":429677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1031/ofr20241031.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1031 PDF"},{"id":429678,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241031/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1031 HTML"},{"id":429676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1031/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Letterkenny Army Depot","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -77.7937803364734,\n              40.07953712912567\n            ],\n            [\n              -77.7937803364734,\n              39.95565132046923\n            ],\n            [\n              -77.61334530644311,\n              39.95565132046923\n            ],\n            [\n              -77.61334530644311,\n              40.07953712912567\n            ],\n            [\n              -77.7937803364734,\n              40.07953712912567\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_pa@usgs.gov\" data-mce-href=\"mailto:dc_pa@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/pennsylvania-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/pennsylvania-water-science-center\">Pennsylvania Water Science Center</a><br>U.S. Geological Survey<br>215 Limekiln Road<br>New Cumberland, PA 17070</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Design and Methods</li><li>Quality Assurance and Quality Control</li><li>2021 Characterization Data</li><li>Study Limitations</li><li>Summary and Conclusions</li><li>Acknowledgements</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2024-06-11","noUsgsAuthors":false,"publicationDate":"2024-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Galeone, Daniel G. 0000-0002-8007-9278","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":207250,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donmoyer, Shaun J. 0000-0002-9600-8570","orcid":"https://orcid.org/0000-0002-9600-8570","contributorId":331207,"corporation":false,"usgs":true,"family":"Donmoyer","given":"Shaun","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902564,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
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