No abstract available.
","language":"English","publisher":"Arizona Geological Survey","usgsCitation":"Thompson, L.A., Haxel, G.B., Peterson, D.W., May, D.J., Tosdal, R., Miller, R.J., Gray, F., LeVeque, R., and Umhoefer, P.J., 2024, Bedrock geologic map of Organ Pipe Cactus National Monument and vicinity, southwest Arizona: Arizona Geological Survey Contributed Report CR-24-B, Report: 28 p.; 1 Plate: 78.00 x 53.73 inches.","productDescription":"Report: 28 p.; 1 Plate: 78.00 x 53.73 inches","ipdsId":"IP-167019","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":464915,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://repository.arizona.edu/handle/10150/674763"},{"id":464917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Organ Pipe Cactus National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.60466467320839,\n 31.818050523947505\n ],\n [\n -112.5712178771873,\n 31.858642501376792\n ],\n [\n -112.68111449268478,\n 32.21508023390702\n ],\n [\n -113.08725415865368,\n 32.22316494823238\n ],\n [\n -113.09681038608835,\n 31.972204964089357\n ],\n [\n -112.60466467320839,\n 31.818050523947505\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Lisa A.","contributorId":347006,"corporation":false,"usgs":false,"family":"Thompson","given":"Lisa","email":"","middleInitial":"A.","affiliations":[{"id":34160,"text":"Arizona Geological Survey","active":true,"usgs":false}],"preferred":false,"id":920527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haxel, Gordon B. 0000-0002-6722-7803 gbhaxel@usgs.gov","orcid":"https://orcid.org/0000-0002-6722-7803","contributorId":261783,"corporation":false,"usgs":true,"family":"Haxel","given":"Gordon","email":"gbhaxel@usgs.gov","middleInitial":"B.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":920528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Donald W.","contributorId":347007,"corporation":false,"usgs":false,"family":"Peterson","given":"Donald","email":"","middleInitial":"W.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":920529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"May, Daniel J.","contributorId":347008,"corporation":false,"usgs":false,"family":"May","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920530,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tosdal, Richard M.","contributorId":347010,"corporation":false,"usgs":false,"family":"Tosdal","given":"Richard M.","affiliations":[],"preferred":false,"id":920531,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Robert J.","contributorId":176277,"corporation":false,"usgs":false,"family":"Miller","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gray, Floyd","contributorId":347011,"corporation":false,"usgs":false,"family":"Gray","given":"Floyd","affiliations":[],"preferred":false,"id":920533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeVeque, Richard A.","contributorId":347012,"corporation":false,"usgs":false,"family":"LeVeque","given":"Richard A.","affiliations":[],"preferred":false,"id":920534,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Umhoefer, Paul J.","contributorId":200335,"corporation":false,"usgs":false,"family":"Umhoefer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920535,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257839,"text":"70257839 - 2024 - The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","interactions":[],"lastModifiedDate":"2024-08-28T13:17:55.218007","indexId":"70257839","displayToPublicDate":"2024-08-27T08:12:09","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","docAbstract":"A changing climate and increasing human population necessitate understanding global freshwater availability. To enable assessment of lake water variability from local-to-global and monthly-to-decadal scales, we extended the Global Lake area, Climate, and Population (GLCP) dataset, which contains monthly lake surface area for 1.42 million lakes with paired basin-level climate and population data from 1995 through 2020. In comparison to the previous version of the GLCP, the extended version is monthly and includes information on lake ice cover as well as basin-level snow area, humidity, longwave and shortwave radiation, and cloud cover. The extended GLCP emphasizes FAIR data principles by expanding its scripting repository and maintaining unique HydroLAKES identifiers, which enables the GLCP to be joined with other HydroLAKES-derived products. Compared to the original version, the extended GLCP contains a richer suite of variables that enable disparate analyses of lake water trends at broad spatial and temporal scales.
","language":"English","publisher":"EarthArxiv","doi":"10.31223/X57X31","usgsCitation":"Meyer, M.F., Virdis, S.G., Yang, X., Brousil, M., McClure, R.P., Sharma, S., Woolway, R.I., Cramer, A.N., Ren, J., Katz, S.L., Hampton, S.E., and Shi, H., 2024, The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables: EarthArXiv, https://doi.org/10.31223/X57X31.","productDescription":"35 p.","ipdsId":"IP-167090","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439189,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://dx.doi.org/10.31223/x57x31","text":"External Repository"},{"id":433245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meyer, Michael Frederick 0000-0002-8034-9434 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8034-9434","contributorId":304191,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"Frederick","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":911761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Virdis, Salvatore G.P. 0000-0003-3927-9494","orcid":"https://orcid.org/0000-0003-3927-9494","contributorId":334733,"corporation":false,"usgs":false,"family":"Virdis","given":"Salvatore","email":"","middleInitial":"G.P.","affiliations":[{"id":80222,"text":"Asian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":911762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Xiao 0000-0002-0046-832X","orcid":"https://orcid.org/0000-0002-0046-832X","contributorId":268230,"corporation":false,"usgs":false,"family":"Yang","given":"Xiao","email":"","affiliations":[{"id":55603,"text":"University of North Carolina Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":911763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brousil, Mattew R. 0000-0001-8229-9445","orcid":"https://orcid.org/0000-0001-8229-9445","contributorId":334731,"corporation":false,"usgs":false,"family":"Brousil","given":"Mattew R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":911764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClure, Ryan P. 0000-0001-6370-3852","orcid":"https://orcid.org/0000-0001-6370-3852","contributorId":268224,"corporation":false,"usgs":false,"family":"McClure","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":911765,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sharma, Sapna","contributorId":150332,"corporation":false,"usgs":false,"family":"Sharma","given":"Sapna","email":"","affiliations":[{"id":16184,"text":"York University","active":true,"usgs":false}],"preferred":false,"id":911766,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woolway, R. Iestyn 0000-0003-0498-7968","orcid":"https://orcid.org/0000-0003-0498-7968","contributorId":297333,"corporation":false,"usgs":false,"family":"Woolway","given":"R.","email":"","middleInitial":"Iestyn","affiliations":[{"id":64373,"text":"European Space Agency Climate Office","active":true,"usgs":false}],"preferred":false,"id":911767,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cramer, Alli N. 0000-0002-0356-5782","orcid":"https://orcid.org/0000-0002-0356-5782","contributorId":268216,"corporation":false,"usgs":false,"family":"Cramer","given":"Alli","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":911768,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ren, Jianning 0000-0002-5849-2189","orcid":"https://orcid.org/0000-0002-5849-2189","contributorId":304196,"corporation":false,"usgs":false,"family":"Ren","given":"Jianning","email":"","affiliations":[{"id":16704,"text":"University of Nevada - Reno","active":true,"usgs":false}],"preferred":false,"id":911769,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Katz, Stephen L.","contributorId":245617,"corporation":false,"usgs":false,"family":"Katz","given":"Stephen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":911770,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hampton, Stephanie E.","contributorId":178718,"corporation":false,"usgs":false,"family":"Hampton","given":"Stephanie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":911771,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shi, Haoran 0000-0001-9543-3324","orcid":"https://orcid.org/0000-0001-9543-3324","contributorId":343708,"corporation":false,"usgs":false,"family":"Shi","given":"Haoran","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":911772,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70257818,"text":"70257818 - 2024 - Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","interactions":[],"lastModifiedDate":"2024-09-11T16:26:52.60017","indexId":"70257818","displayToPublicDate":"2024-08-27T07:07:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","docAbstract":"Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real‐time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P‐ and S‐wave arrival‐time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey‐published 1D P‐wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.
Seattle Public Utilities (SPU) has a history of conducting traditional fish surveys in urban streams of Seattle, Washington. Limited staff resources have reduced SPU's capacity to monitor fish, and environmental DNA (eDNA) was recognized as an alternative survey method that could potentially improve the efficiency and capacity of SPU-sponsored fish surveys. We performed spatiotemporal surveys of eDNA to assess occupancy and distribution of Chinook Salmon (Oncorhynchus tshawytscha), Coho Salmon (O. kisutch), and Coastal Cutthroat Trout (O. clarkii clarkii) in Thornton Creek, Seattle, between October 2018 and December 2020. Peak Chinook and Coho eDNA detections occurred in October and October–November, respectively, coinciding with expected adult return time. Chinook and Coho eDNA was detected in May at the time when juveniles outmigrate through the Lake Washington basin. Coastal Cutthroat Trout eDNA was widespread and detected at high rates across seasons, reflecting their ubiquitous distribution. Results from multiscale occupancy modeling suggested that distance upstream affected site-level occupancy probabilities for adult Chinook, but not Coho. Model results also suggested that the probability of Coho and Chinook eDNA occurring in water samples was affected by survey year. Finally, model results suggested that the probability of detecting Chinook eDNA in PCR technical replicates was affected by survey year and collection day but detection of Coho eDNA was only affected by collection day. This study indicates eDNA surveys are effective for assessing distribution and occupancy of salmonids in Seattle's urban streams. Integrating eDNA surveys into urban stream monitoring programs can help alleviate the burden of limited assets facing many resource managers.
Fire facilitates erosion through changes in vegetation and soil, with major postfire erosion commonly occurring even with moderate rainfall. As climate warms, the western United States (U.S.) is experiencing an intensifying fire regime and increasing frequency of extreme rain. We evaluated whether these hydroclimatic changes are evident in patterns of postfire erosion by modeling hillslope erosion following all wildfires larger than 100 km2 in California from 1984 to 2021. Our results show that annual statewide postfire hillslope erosion has increased significantly over time. To supplement the hillslope erosion modeling, we compiled modeled and measured postfire debris-flow volumes. We find that, in northern California, more than 50% of fires triggering the top 20 values of sediment mass and sediment yield occurred in the most recent decade (between 2011 and 2021). In southern California, the postfire sediment budget was dominated by debris flows, which showed no temporal trend. Our analysis reveals that 57% of postfire sediment erosion statewide occurred upstream of reservoirs, indicating potential impacts to reservoir storage capacity and thus increased risk to water-resource security with ongoing climate change.
Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4961","usgsCitation":"Brusa, J., Farr, M., Evenson, J., Silverman, E., Murphie, B., Cyra, T., Tschaekofske, H., Spragens, K., and Converse, S.J., 2024, Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data: Ecosphere, v. 15, no. 8, e4961, 15 p., https://doi.org/10.1002/ecs2.4961.","productDescription":"e4961, 15 p.","ipdsId":"IP-157545","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4961","text":"Publisher Index Page"},{"id":482967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.31135582023572,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 49.0056879632715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Brusa, Jamie L.","contributorId":351922,"corporation":false,"usgs":false,"family":"Brusa","given":"Jamie L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farr, Matthew T.","contributorId":351923,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew T.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evenson, Joseph","contributorId":351924,"corporation":false,"usgs":false,"family":"Evenson","given":"Joseph","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silverman, Emily","contributorId":351925,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphie, Bryan","contributorId":351927,"corporation":false,"usgs":false,"family":"Murphie","given":"Bryan","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cyra, Thomas A.","contributorId":351929,"corporation":false,"usgs":false,"family":"Cyra","given":"Thomas A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929756,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tschaekofske, Heather","contributorId":351931,"corporation":false,"usgs":false,"family":"Tschaekofske","given":"Heather","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spragens, Kyle A.","contributorId":351933,"corporation":false,"usgs":false,"family":"Spragens","given":"Kyle A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929758,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929759,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257684,"text":"fs20243028 - 2024 - Structured science syntheses to inform decision making on Federal public lands","interactions":[],"lastModifiedDate":"2024-08-27T14:26:52.31055","indexId":"fs20243028","displayToPublicDate":"2024-08-26T15:00:00","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":"2024-3028","displayTitle":"Structured Science Syntheses to Inform Decision Making on Federal Public Lands","title":"Structured science syntheses to inform decision making on Federal public lands","docAbstract":"The U.S. Geological Survey, Bureau of Land Management, and U.S. Fish and Wildlife Service partnered to develop a new type of science product: the structured science synthesis. Structured science syntheses are peer-reviewed reports that synthesize science information about a priority resource management issue on public lands. Structured science syntheses are developed explicitly to facilitate the application of science to decision making. Key characteristics of structured science syntheses include that they are coproduced with resource managers, developed using clear, repeatable methods and designed for ease of use. The syntheses include different types of science information needed for analyses completed under the National Environmental Policy Act.
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U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The Coastal California Gnatcatcher (Polioptila californica californica: “gnatcatcher”) is a resident species restricted to coastal sage scrub habitat in southern California. Listed as federally threatened, the gnatcatcher is subject to multiple threats, including habitat loss, fragmentation, and degradation, particularly in association with the increasing frequency of large wildfires. The California Gnatcatcher is a focal species under several habitat conservation plans and is monitored to determine population trends and evaluate the success of the plans in protecting the species.
Historically, gnatcatcher monitoring has been limited in geographic scope and has used differing methodologies, limiting the extent to which findings can be generalized across larger spatial scales and multiple populations. In 2015, we completed the first of an intended series of surveys following a standardized protocol designed to address two broad objectives. First, we sought to determine gnatcatcher occupancy at the regional scale, including habitat from throughout the species’ range in southern California, as well as in two subregions: Orange County and San Diego County, to address specific management objectives within those jurisdictions. In addition, we collected vegetation data to better understand gnatcatcher habitat associations that affect occupancy. In a parallel objective, we evaluated the effect of fire on gnatcatchers and their habitat by comparing occupancy and vegetation characteristics across sites varying in the length of time since the last fire. Data collected in 2020 allowed us to expand the study to include analyses of colonization (sites unoccupied in one year and occupied the next) and extinction (sites occupied in one year but not the next).
In 2020, we surveyed 327 regional points and 180 subregional points each in Orange and San Diego Counties. In addition, we surveyed 95–106 points within 4 postfire categories based on the year of the last fire at each point: before or during 2002 (“unburned”), 2003–06, 2007–10, and 2011–14. We surveyed for gnatcatchers during three area searches centered on each point at 2-week intervals commencing in mid-March. Vegetation data were collected during May–June using a modified point-intercept method along fixed transects.
Shrub and tree cover at our plots was dominated by California sagebrush (Artemisia californica), California buckwheat (Eriogonum fasciculatum), laurel sumac (Malosma laurina), sage (including Salvia mellifera and S. leucophylla), and sunflowers (including Encelia californica, E. farinosa, and Bahiopsis laciniata); however, most of the vegetation at plots consisted of non-native grass and herbaceous plants, indicating a high level of disturbance associated with fire. We documented vegetation differences at the subregional scale indicative of differences in fire history: in Orange County, overall shrub/tree cover was higher and herbaceous cover lower than in San Diego, where three large fires had burned within 17 years of this study. Both woody and herbaceous cover increased between 2016 and 2020 at the regional and two subregional scales, likely a response to above-average precipitation during 2 years preceding the 2020 surveys. Herbaceous vegetation also increased at postfire points; however, woody vegetation cover changed little between 2016 and 2020.
We modeled the effects of vegetation and physical (elevation, distance to Pacific coast, slope) covariates on gnatcatcher occupancy, colonization, and extinction probabilities in the regional, subregional, and postfire datasets. Cover of California sagebrush was the strongest predictor of gnatcatcher occupancy and appeared in the top models for every dataset. California buckwheat was another strong positive predictor of gnatcatcher occupancy in all datasets. Cover of sunflowers was a positive predictor of occupancy in the Orange County subregion, and both sunflowers and sage were positive predictors of occupancy at postfire points. In contrast, laurel sumac was negatively related to gnatcatcher occupancy in postfire habitats, with occupancy unlikely when sumac exceeded 50 percent cover. Herbaceous vegetation, including invasive grass, negatively affected gnatcatcher occupancy regionwide.
Covariates that were strong positive predictors of occupancy were also positive predictors of colonization and (or) negative predictors of extinction, and vice versa. Outside of the positive effects of California sagebrush and California buckwheat, and the negative effect of total herbaceous cover, we identified few covariates influencing colonization. In contrast, we identified many more predictors of extinction, including cover of laurel sumac and grass, which increased extinction risk, and cover of California sagebrush, sunflowers, and bare ground, along with time since last fire, which reduced extinction risk.
We used our modelled estimates of colonization and extinction probabilities to derive occupancy in 2020 based on starting occupancy in 2016. Gnatcatcher occupancy increased in 2020 at regional and subregional scales and in unburned habitat, growing by 19–35 percent since 2016. Among burned sites, occupancy tripled from 2016 to 2020 at points burned during 2011–14 but was unchanged at the 2007–10 postfire points and declined by 28 percent at points burned in 2003–06. The slow recovery of the gnatcatcher population in this latter category, which makes up 16 percent of the suitable habitat in San Diego County, is a matter of conservation concern warranting further attention.
Collectively, our rangewide results reveal a widespread and long-term effect of wildfire on California Gnatcatcher habitat, particularly in San Diego County. These data provide a baseline from which future monitoring can be used to evaluate changes in habitat condition over time and to improve our understanding of the factors and processes influencing gnatcatcher occupancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241015","collaboration":"Prepared in cooperation with the San Diego Association of Governments, Natural Communities Coalition, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kus, B.E., Houston, A., and Preston, K.L., 2024, Occupancy dynamics of the Coastal California Gnatcatcher in southern California: U.S. Geological Survey Open-File Report 2024–1015, 34 p., https://doi.org/10.3133/ofr20241015.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-156536","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":433034,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241015/full"},{"id":433032,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.xml"},{"id":433031,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":433029,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC30JX","text":"USGS Data Release","description":"Kus, B.E., and Houston, A., 2021, Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/F7PC30JX.","linkHelpText":"Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023)"},{"id":433033,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1015/images"},{"id":433030,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1015/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.95012019018054,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 35.34010394860475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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Geologic cross section N–N′ is the sixth in a series of geologic cross sections constructed by the U.S. Geological Survey to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section N–N′ provides a regional view of the structural and stratigraphic framework of the Appalachian basin in the Valley and Ridge province in western and central Alabama; it spans approximately 69 miles (mi) (111 kilometers [km]).
This geologic cross section is a companion to geologic cross sections E–E′, D–D′, C–C′, I–I′, and A–A′ that are located approximately 350 to 550 mi (563 to 885 km) to the northeast. Cross section N–N' complements earlier geologic cross sections through the Alabama part of the Appalachian basin. Although some of the other cross sections show more structural and stratigraphic detail, they are of more limited extent geographically and stratigraphically.
Cross section N–N′ contains information that is useful for evaluating energy resources in the Appalachian basin. Although the Appalachian basin petroleum systems are not shown on the cross section, many of their key elements (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Other aspects of petroleum systems (such as the timing of petroleum generation and petroleum migration pathways) may be evaluated by burial history, thermal history, and fluid flow models based on what is shown on the cross section. In addition, cross section N–N′ may be used as a reconnaissance tool to identify plausible geologic structures and strata for the subsurface storage of liquid waste or for the sequestration of carbon dioxide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3524","usgsCitation":"Trippi, M.H., Coleman, J.L., and Ryder, R.T., 2024, Cross section N–N' through the Valley and Ridge province of the southern Appalachian basin, from Greene County, west-central Alabama, to Bibb County, central Alabama (ver. 1.1, November 2024): U.S. Geological Survey Scientific Investigations Map 3524, 1 sheet, 51-p. pamphlet, https://doi.org/10.3133/sim3524.","productDescription":"Report: viii, 51 p.; 1 Sheet: 71.83 x 37.31 inches","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-154937","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463685,"rank":12,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3524/versionHist.txt","size":"1.69 KB","linkFileType":{"id":2,"text":"txt"}},{"id":433016,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2985","text":"Scientific Investigations Map 2985","linkHelpText":"- Geologic Cross Section E-E' through the Appalachian Basin from the Findlay Arch, Wood County, Ohio, to the Valley and Ridge Province, Pendleton County, West Virginia"},{"id":433020,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3425","text":"Scientific Investigations Map 3425","linkHelpText":"- Geologic Cross Section A–A′ Through the Appalachian Basin from the Southern Margin of the Ontario Lowlands Province, Genesee County, Western New York, to the Valley and Ridge Province, Lycoming County, North-Central Pennsylvania"},{"id":432956,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3524/images/"},{"id":432955,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3524/sim3524.XML","linkFileType":{"id":8,"text":"xml"}},{"id":432957,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3524/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":497892,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117262.htm","linkFileType":{"id":5,"text":"html"}},{"id":432953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_pamphlet.pdf","text":"Pamphlet","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3524/coverthb.jpg"},{"id":433017,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3067","text":"Scientific Investigations Map 3067","linkHelpText":"- Geologic Cross Section D-D' Through the Appalachian Basin from the Findlay Arch, Sandusky County, Ohio, to the Valley and Ridge Province, Hardy County, West Virginia"},{"id":433018,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3172","text":"Scientific Investigations Map 3172","linkHelpText":"- Geologic cross section C-C' through the Appalachian basin from Erie County, north-central Ohio, to the Valley and Ridge province, Bedford County, south-central Pennsylvania"},{"id":433019,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3343","text":"Scientific Investigations Map 3343","linkHelpText":"- Geologic Cross Section I–I′ Through the Appalachian Basin from the Eastern Margin of the Illinois Basin, Jefferson County, Kentucky, to the Valley and Ridge Province, Scott County, Virginia"},{"id":432954,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_map.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.63427073659228,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 32.37457003921131\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 26, 2024; Version 1.1: November 5, 2024","contact":"Geology, Energy & Minerals Science Center
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This report presents a plan supported by the National Earthquake Hazards Reduction Program (NEHRP) to coordinate domestic and international post-earthquake investigations (herein called “the Plan”). Post-earthquake scientific and engineering investigations are undertaken to capture critical information to understand the causes and impacts of the event, lessons from which can substantially improve the Nation’s resilience after future earthquakes. NEHRP is the Federal Government’s coordinated nationwide program to reduce risks to life and property from earthquakes. The Plan describes the activation and coordination of the four designated NEHRP Agencies in the Federal Government: Federal Emergency Management Agency, National Institute of Standards and Technology, U.S. National Science Foundation, and U.S. Geological Survey (USGS). The Plan also describes coordination between NEHRP Agencies and other organizations that may participate in pre-event and post-earthquake investigations, including non-NEHRP Federal agencies; State, regional, local, Tribal, and territorial agencies; domestic nongovernmental organizations; academic institutions and affiliated organizations; private companies; foreign governmental agencies and nongovernmental organizations; and international organizations. The Plan delineates the coordination of NEHRP post-earthquake scientific and engineering investigations to document the direct, indirect, and cascading physical and societal impacts from fault rupture and ground shaking hazards and from secondary hazards such as landslides, liquefaction, and tsunamis. In addition, the Plan identifies pre-event activities necessary to ensure that post-earthquake investigations are executed effectively. The USGS is the lead NEHRP Agency for activating and coordinating NEHRP post-earthquake investigations and for implementing this Plan. The USGS also leads coordination of the NEHRP Agencies in completing the pre-event activities identified in the Plan. The Plan has new information and supersedes USGS Circular 1242, “The Plan to Coordinate NEHRP Post-Earthquake Investigations,” which was published in 2003. This second-generation Plan was developed with the assistance of the Applied Technology Council (ATC) of Redwood City, California, under USGS contract 140G0121P0309, ATC-155. A 17-member Project Review Panel provided guidance on plan development, and input was solicited from subject matter experts representing key stakeholder groups and from participants at a public workshop.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1542","isbn":"978-1-4113-4584-3","collaboration":"Prepared in collaboration with the Federal Emergency Management Agency, National Institute of Standards and Technology, and U.S. National Science Foundation","usgsCitation":"Poland, C., Bray, J.D., Johnson, L., Nikolaou, S., Rathje, E., and Sherrod, B., 2024, Plan to coordinate post-earthquake investigations supported by the National Earthquake Hazards Reduction Program (NEHRP): U.S. Geological Survey Circular 1542, 36 p., https://doi.org/10.3133/cir1542. 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U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192
The Kansas River is an essential water resource that provides drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical water-treatment strategies to remove contaminants before distribution. Water District No. 1 of Johnson County, Kansas (WaterOne), is the largest water supplier in the State and uses the Kansas and Missouri Rivers as water-supply sources to provide drinking water to the Kansas City metropolitan area. WaterOne has been using ozone disinfection as a primary water-treatment strategy since the summer of 2020. Water suppliers that rely on ozone disinfection have become increasingly concerned with the presence of elevated dissolved bromide (the negatively charged form of bromine; hereafter referred to as “bromide”) concentrations in their water-supply source. Ozone disinfection of source water containing elevated concentrations of bromide can lead to the formation of bromate, a regulated disinfection byproduct and probable carcinogen. Real-time computations of bromide concentrations upstream from the WaterOne source-water intake in the Kansas River can be used to assist WaterOne with proactive adjustment of water-treatment strategies. These computations can also be used to advance understanding of hydrologic processes affecting ozone disinfection and formation of bromate.
This report documents the development of the surrogate-regression model that computes bromide concentrations in real time at De Soto, Kansas, and characterizes daily and monthly bromide concentrations at this location during the study period. The U.S. Geological Survey (USGS), in cooperation with WaterOne, collected specific conductance and discrete bromide sample data at the USGS streamgage Kansas River at De Soto, Kans. (06892350; hereafter referred to as “De Soto”), during January 2021 through October 2023 to develop a surrogate-regression model using ordinary least-squares regression that computes bromide concentrations at De Soto, which is about 15 miles upstream from the WaterOne source-water intake in the Kansas River. Specific conductance explained about 85 percent of the variance in bromide concentrations at De Soto during the study period. The surrogate-regression model documented in this report estimated that bromide concentrations at De Soto were likely to exceed the WaterOne water-treatment level of concern (150 micrograms per liter [μg/L]) when specific conductance was greater than or equal to about 930 microsiemens per centimeter at 25 degrees Celsius. Surrogate-regression model computations of bromide concentrations documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov/).
Bromide concentrations in discrete samples ranged from 31.9 to 251 μg/L and exceeded the water-treatment level of concern in about 34 percent of the 41 samples collected at De Soto during January 2021 through October 2023. Computed daily bromide concentrations ranged from 38.2 to 277 μg/L and exceeded the water-treatment level of concern about 46 percent of the time during January 2021 through October 2023. Generally, an inverse relation was observed between bromide and streamflow during the study period. Higher bromide concentrations were observed during September through February, and lower bromide concentrations were observed during March through August. Seasonal median bromide concentrations were significantly different in all pairwise seasonal combinations, except for summer versus spring. Computed median bromide concentrations were highest during winter, followed by fall, then spring and summer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245078","collaboration":"Prepared in cooperation with the Water District No. 1 of Johnson County, Kansas","usgsCitation":"Williams, T.J., and Totzke, G.S., 2024, Computation of bromide concentrations at the Kansas River at De Soto, Kansas, January 2021 through October 2023: U.S. Geological Survey Scientific Investigations Report 2024–5078, 18 p., https://doi.org/10.3133/sir20245078.","productDescription":"Report: vii, 18 p.; Appendix; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-166673","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":433130,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245078/full"},{"id":433128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5078/downloads/","text":"Appendix 1","linkHelpText":"—Model Archival Summary for Bromide Concentration at U.S. Geological Survey Streamgage 06892350, Kansas River at De Soto, Kansas, during January 2021 through October 2023"},{"id":499481,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117306.htm","linkFileType":{"id":5,"text":"html"}},{"id":433124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5078/coverthb.jpg"},{"id":433127,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5078/images/"},{"id":433126,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.XML"},{"id":433125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.pdf","text":"Report","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5078"},{"id":433129,"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"}],"country":"United States","state":"Kansas","city":"De Soto","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.89522863083639,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 39.3821856985449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Climate change poses a significant threat to Arctic ecosystems. Evaluation of genetic diversity within and differentiation among populations is needed to effectively conserve Arctic species and ensure genetic variation is appropriately managed.This research examined the population genetic structure in Larus hyperboreus (Glaucous Gull), a circumpolar Arctic species that is declining in parts of its range. Population genetic information is needed to help delineate management units, including information on genetic differences among related species, among subspecies (currently distinguished by subtle morphometric and plumage differences), and among colonies in North America. We conducted double-digest restriction site-associated DNA sequencing for 62 L. hyperboreus, 18 L. argentatus smithsonianus (American Herring Gull), 6 L. a. argentatus (European Herring Gull), and 15 L. glaucescens (Glaucous-winged Gull) sampled across the Canadian and European Arctic. Interspecific analyses using 2,145 loci were unable to distinguish between all members of this species complex. Despite the geographic distance among sampling locations, molecular assignments and principal coordinates analyses based on 621 loci uncovered only weak population genetic differentiation among sampled European and Canadian colonies of L. hyperboreus. As L. hyperboreus occupying the eastern Canadian Arctic appears to be acting as a single panmictic population, conservation plans that protect Arctic habitat may help slow or reverse population declines. Proactive conservation strategies will benefit both L. hyperboreus and associated coastal Arctic ecosystems.
","language":"English","publisher":"BioOne","doi":"10.1093/ornithapp/duae037","usgsCitation":"Linklater, E., Sonsthagen, S.A., Robertson, G., Colston-Nepali, L., Vigfusdottir, F., and Friesen, V., 2024, 26 August 2024 Reduced representation sequencing reveals weak genetic differentiation between Canadian and European Larus hyperboreus (Glaucous Gull): Ornithological Applications, v. 126, no. 4, p. 1-11, https://doi.org/10.1093/ornithapp/duae037.","productDescription":"12 p.","startPage":"1","endPage":"11","ipdsId":"IP-154834","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae037","text":"Publisher Index Page"},{"id":482264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Iceland, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -170.12197544996508,\n 62.606661683706506\n ],\n [\n -174.0470710526139,\n 50.852424322031254\n ],\n [\n -117.65103250372262,\n 49.497113822314844\n ],\n [\n -49.87838442995181,\n 44.44688847413198\n ],\n [\n -5.424596138025919,\n 65.78041162742718\n ],\n [\n -129.8047925349852,\n 79.78944673754077\n ],\n [\n -170.12197544996508,\n 62.606661683706506\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Linklater, Emma Lachance","contributorId":351063,"corporation":false,"usgs":false,"family":"Linklater","given":"Emma Lachance","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":927825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Gregory J.","contributorId":351064,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory J.","affiliations":[{"id":83910,"text":"Wildlife Research Division, Environment and Climate Change Canada (ECCC)","active":true,"usgs":false}],"preferred":false,"id":927826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colston-Nepali, Lila","contributorId":351065,"corporation":false,"usgs":false,"family":"Colston-Nepali","given":"Lila","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vigfusdottir, Freydís","contributorId":351066,"corporation":false,"usgs":false,"family":"Vigfusdottir","given":"Freydís","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":927828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friesen, Vicki L.","contributorId":351067,"corporation":false,"usgs":false,"family":"Friesen","given":"Vicki L.","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927829,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257809,"text":"70257809 - 2024 - Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data","interactions":[],"lastModifiedDate":"2024-08-28T12:06:30.07639","indexId":"70257809","displayToPublicDate":"2024-08-26T07:01:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data","docAbstract":"Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and translational slides derived solely from high-resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non-landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high-resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age-roughness curves that are calibrated with high-resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age-roughness-derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high-resolution elevation data compared with age-dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.
Ozette Lake is an ~100-m-deep coastal lake located along the outer coast of the Olympic Peninsula (Washington, USA); it is situated above the locked portion of the northern Cascadia megathrust but also relatively isolated from active crustal faults and intraslab earthquakes. Here we present a suite of geophysical and geological evidence for earthquake-triggered mass transport deposits (MTDs) and related turbidite deposition in Ozette Lake since ca. 14 ka. Comprehensive high-resolution bathymetry data, seismic reflection profiles, and sediment cores are used to characterize the post-glacial stratigraphic framework and examine paleoseismic evidence in the lacustrine sediments. Stacked sequences of MTDs along the steep eastern flanks of the lake appear to grade basin-ward from thick, chaotic, blocky masses to thin, parallel-bedded turbidite beds. The discrete turbidite event layers are separated by fine-grained (silt and clay) lake sedimentation. The event layers are observed throughout the lake, but the physical characteristics of the deposits vary considerably depending on proximity to primary depocenters, steep slopes, and subaqueous deltas. A total of 30–34 event deposits are observed in the post-glacial record. Radiometric dating was used to reconstruct a detailed sedimentation history over the last ~5.5 k.y., develop an age model, and estimate the recurrence (365–405 yr) for the most recent 12 event layers. Based on sedimentological characteristics, temporal overlap with other regional paleoseismic chronologies, and recurrence estimates, at least 10 of the dated event layers appear to be sourced from slope failures triggered by intense shaking during megathrust ruptures; the recurrence interval for these 10 events is 440–560 yr. Thus, Ozette Lake contains one of the longest and most robust geological records of repeated shaking along the northern Cascadia subduction zone.
Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15249","usgsCitation":"Hall, J.S., Scholl, M.A., Shanley, J.B., Matt, S., and Uriarte, M., 2024, Forest cover lessens hurricane impacts on peak streamflow: Hydrological Processes, v. 38, no. 8, e15249, 15 p., https://doi.org/10.1002/hyp.15249.","productDescription":"e15249, 15 p.","ipdsId":"IP-145696","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":466952,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15249","text":"Publisher Index Page"},{"id":463115,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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Division","active":true,"usgs":true}],"preferred":true,"id":916574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matt, Serena 0000-0001-7489-1588","orcid":"https://orcid.org/0000-0001-7489-1588","contributorId":270681,"corporation":false,"usgs":true,"family":"Matt","given":"Serena","email":"","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":916577,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260972,"text":"70260972 - 2024 - Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau","interactions":[],"lastModifiedDate":"2024-11-19T19:30:42.94833","indexId":"70260972","displayToPublicDate":"2024-08-25T13:08:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau","docAbstract":"Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C- and N-related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi- and hyperspectral missions with targeted band placement, such as the proposed 26-band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.
","language":"English","publisher":"AGU","doi":"10.1029/2024JG008089","usgsCitation":"Yan, D., Reed, S., Rutherford, W., Javadian, M., Reibold, R.H., Villarreal, M.L., Poulter, B., Song, S., and Smith, W., 2024, Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau: Journal of Geophysical Research: Biogeosciences, v. 129, no. 8, e2024JG008089, 14 p., https://doi.org/10.1029/2024JG008089.","productDescription":"e2024JG008089, 14 p.","ipdsId":"IP-153887","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008089","text":"Publisher Index Page"},{"id":464299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Moab","otherGeospatial":"Sand Flats Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.52189096872196,\n 38.5875471924133\n ],\n [\n -109.52189096872196,\n 38.569668406221155\n ],\n [\n -109.46583788017041,\n 38.569668406221155\n ],\n [\n -109.46583788017041,\n 38.5875471924133\n ],\n [\n -109.52189096872196,\n 38.5875471924133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Yan, Dong","contributorId":346341,"corporation":false,"usgs":false,"family":"Yan","given":"Dong","affiliations":[{"id":82831,"text":"Information and Data Center, China Renewable Energy Engineering Institute, Beijing 100120, China.","active":true,"usgs":false}],"preferred":false,"id":918762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":918763,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutherford, William A.","contributorId":346342,"corporation":false,"usgs":false,"family":"Rutherford","given":"William A.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918764,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Javadian, Mostafa","contributorId":346343,"corporation":false,"usgs":false,"family":"Javadian","given":"Mostafa","email":"","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918765,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":918766,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":918767,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poulter, Benjamin","contributorId":346344,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":82832,"text":"National Aeronautics and Space Administration, Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":918768,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Song, Shujun","contributorId":346345,"corporation":false,"usgs":false,"family":"Song","given":"Shujun","email":"","affiliations":[{"id":82833,"text":"nformation and Data Center, China Renewable Energy Engineering Institute","active":true,"usgs":false}],"preferred":false,"id":918769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, William K.","contributorId":346346,"corporation":false,"usgs":false,"family":"Smith","given":"William K.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918770,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70258114,"text":"70258114 - 2024 - Acute toxicity of lampricides to non-target species of concern in the Lake Champlain watershed","interactions":[],"lastModifiedDate":"2024-12-10T15:14:48.791212","indexId":"70258114","displayToPublicDate":"2024-08-25T08:25:15","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":"Acute toxicity of lampricides to non-target species of concern in the Lake Champlain watershed","docAbstract":"Previous research evaluated the toxicity of the lampricide 4-nitro-3-(trifluoromethyl)phenol (TFM) and the combination of TFM with 1 % niclosamide (TFM:1%Nic) to multiple non-target species in the Laurentian Great Lakes. However, few toxicity studies have been conducted for species of concern in Lake Champlain (NY and VT). We conducted 12-hour flow-through toxicity tests with 4 species of native mussels, 6 species of fish, and 1 amphibian species. All tests included exposure of invasive larval Petromyzon marinus (sea lamprey) and were conducted with concentrations that bracketed the predicted minimum lethal concentration required to control larval sea lamprey. Mussel species’ NOEC, LOEC, LC25, and LC50 values ranged from 1.33 to 2.12, 1.71–2.66, 1.75–3.05, and 2.03–4.84 times field determined LC99.9s for sea lamprey (×SLLC99.9) in TFM-only toxicity tests, and from 1.36 to 1.70, 1.68–2.03, 1.86–2.10, and 2.35–2.68 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Fish species NOEC, LOEC, LC25, and LC50 values ranged from 0.60 to 1.89, 0.73–2.13, 0.72–2.11, and 0.76–2.18 × SLLC99.9 in TFM-only toxicity tests, and from 0.64 to 2.48, 0.85–3.10, 0.74–3.05, and 0.78–3.16 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Amphibian species NOEC, LOEC, LC25, and LC50 values ranged from 0.74 to 0.75, 0.85–0.95, 0.83–0.87, and 0.85–0.91 × SLLC99.9 in TFM-only toxicity tests, and from 0.63 to 0.65, 0.80–0.88, 0.77–0.82, and 0.78–0.87 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Generally, mussel species were tolerant, fish sensitivities were variable, and the amphibian species was sensitive to TFM and TFM:1%Nic.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102422","usgsCitation":"Neuderfer, G.N., Durfey, L.E., Calloway, M.T., Smith, S.J., and Schueller, J., 2024, Acute toxicity of lampricides to non-target species of concern in the Lake Champlain watershed: Journal of Great Lakes Research, v. 50, no. 6, 102422, 9 p., https://doi.org/10.1016/j.jglr.2024.102422.","productDescription":"102422, 9 p.","ipdsId":"IP-160364","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":433491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Vermont","otherGeospatial":"Lake Champlain watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.64408912592127,\n 44.99762815971462\n ],\n [\n -73.64408912592127,\n 43.70578516904615\n ],\n [\n -72.80563760709894,\n 43.70578516904615\n ],\n [\n -72.80563760709894,\n 44.99762815971462\n ],\n [\n -73.64408912592127,\n 44.99762815971462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Neuderfer, Gary N.","contributorId":343867,"corporation":false,"usgs":false,"family":"Neuderfer","given":"Gary","email":"","middleInitial":"N.","affiliations":[{"id":82230,"text":"New York State Department of Environmental Conservation, Albany, NY (Retired)","active":true,"usgs":false}],"preferred":false,"id":912233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durfey, Lance E.","contributorId":343868,"corporation":false,"usgs":false,"family":"Durfey","given":"Lance","email":"","middleInitial":"E.","affiliations":[{"id":82230,"text":"New York State Department of Environmental Conservation, Albany, NY (Retired)","active":true,"usgs":false}],"preferred":false,"id":912234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Calloway, Michael T.","contributorId":343869,"corporation":false,"usgs":false,"family":"Calloway","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":82233,"text":"Federal Energy Regulatory Commission, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":912235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Stephen J.","contributorId":38926,"corporation":false,"usgs":false,"family":"Smith","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":912236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":912237,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259732,"text":"70259732 - 2024 - Documentation of a probable spawning run of cisco Coregonus artedi in the Spanish River, Ontario, Canada","interactions":[],"lastModifiedDate":"2024-12-11T15:53:02.24917","indexId":"70259732","displayToPublicDate":"2024-08-25T06:34:41","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":"Documentation of a probable spawning run of cisco Coregonus artedi in the Spanish River, Ontario, Canada","docAbstract":"Mid-sized mammals (i.e., mesomammals) fulfill important ecological roles, serving as essential scavengers, predators, pollinators, and seed dispersers in the ecosystems they inhabit. Consequently, declines in mesomammal populations have the potential to alter ecological processes and fundamentally change ecosystems. However, ecosystems characterized by high functional redundancy, where multiple species can fulfil similar ecological roles, may be less impacted by the loss of mesomammals and other vertebrates. The Greater Everglades Ecosystem in southern Florida is a historically biodiverse region that has recently been impacted by multiple anthropogenic threats, most notably the introduction of the Burmese python (Python molurus bivittatus). Since pythons became established, mesomammal populations have become greatly reduced. To assess whether these declines in mesomammals have affected two critical ecosystem functions—scavenging and frugivory—we conducted experiments in areas where mesomammals were present and absent. We did not observe significant differences in scavenging or frugivory efficiency in areas with and without mesomammals, but we did observe significant differences in the communities responsible for scavenging and frugivory. Despite the observed evidence of redundancy, the changes in community composition could potentially lead to indirect consequences on processes like seed dispersal and disease dynamics within this ecosystem, emphasizing the need for further study.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-66534-8","usgsCitation":"McKee, R.K., Taillie, P.J., Hart, K., Lopez, C.L., Sanjar, A., and McCleery, R.A., 2024, Ecological function maintained despite mesomammal declines: Scientific Reports, v. 14, 19668, 11 p., https://doi.org/10.1038/s41598-024-66534-8.","productDescription":"19668, 11 p.","ipdsId":"IP-147236","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439194,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-66534-8","text":"Publisher Index Page"},{"id":433494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.11633743096398,\n 26.909667893106402\n ],\n [\n -82.11633743096398,\n 25.129105524826187\n ],\n [\n -79.91364452173318,\n 25.129105524826187\n ],\n [\n -79.91364452173318,\n 26.909667893106402\n ],\n [\n -82.11633743096398,\n 26.909667893106402\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Rebecca K.","contributorId":341474,"corporation":false,"usgs":false,"family":"McKee","given":"Rebecca","email":"","middleInitial":"K.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":912300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taillie, Paul J.","contributorId":203647,"corporation":false,"usgs":false,"family":"Taillie","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":912301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":912302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez, Christopher L.","contributorId":343897,"corporation":false,"usgs":false,"family":"Lopez","given":"Christopher","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":912303,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sanjar, Adam","contributorId":343898,"corporation":false,"usgs":false,"family":"Sanjar","given":"Adam","email":"","affiliations":[{"id":82250,"text":"University of Florida, University of Texas Rio Grande Valley","active":true,"usgs":false}],"preferred":false,"id":912304,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":912305,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257794,"text":"70257794 - 2024 - Subduction zone geometry modulates the megathrust earthquake cycle: Magnitude, recurrence, and variability","interactions":[],"lastModifiedDate":"2024-08-27T13:50:35.987787","indexId":"70257794","displayToPublicDate":"2024-08-24T08:47:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Subduction zone geometry modulates the megathrust earthquake cycle: Magnitude, recurrence, and variability","docAbstract":"Megathrust geometric properties exhibit some of the strongest correlations with maximum earthquake magnitude in global surveys of large subduction zone earthquakes, but the mechanisms through which fault geometry influences subduction earthquake cycle dynamics remain unresolved. Here, we develop 39 models of sequences of earthquakes and aseismic slip (SEAS) on variably-dipping planar and variably-curved nonplanar megathrusts using the volumetric, high-order accurate code tandem to account for fault curvature. We vary the dip, downdip curvature and width of the seismogenic zone to examine how slab geometry mechanically influences megathrust seismic cycles, including the size, variability, and interevent timing of earthquakes. Dip and curvature control characteristic slip styles primarily through their influence on seismogenic zone width: wider seismogenic zones allow shallowly-dipping megathrusts to host larger earthquakes than steeply-dipping ones. Under elevated pore pressure and less strongly velocity-weakening friction, all modeled fault geometries host uniform periodic ruptures. In contrast, shallowly-dipping and sharply-curved megathrusts host multi-period supercycles of slow-to-fast, small-to-large slip events under higher effective stresses and more strongly velocity-weakening friction. We discuss how subduction zones' maximum earthquake magnitudes may be primarily controlled by the dip and dimensions of the seismogenic zone, while second-order effects from structurally-derived mechanical heterogeneity modulate the recurrence frequency and timing of these events. Our results suggest that enhanced co- and interseismic strength and stress variability along the megathrust, such as induced near areas of high or heterogeneous fault curvature, limits how frequently large ruptures occur and may explain curved faults' tendency to host more frequent, smaller earthquakes than flat faults.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JB029191","usgsCitation":"Biemiller, J.B., Gabriel, A., May, D., and Staisch, L.M., 2024, Subduction zone geometry modulates the megathrust earthquake cycle: Magnitude, recurrence, and variability: Journal of Geophysical Research: Solid Earth, v. 129, no. 8, e2024JB029191, 24 p., https://doi.org/10.1029/2024JB029191.","productDescription":"e2024JB029191, 24 p.","ipdsId":"IP-156936","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":433192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Biemiller, James Burkhardt 0000-0001-6663-7811","orcid":"https://orcid.org/0000-0001-6663-7811","contributorId":343684,"corporation":false,"usgs":true,"family":"Biemiller","given":"James","email":"","middleInitial":"Burkhardt","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":911691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gabriel, Alice-Agnes","contributorId":204611,"corporation":false,"usgs":false,"family":"Gabriel","given":"Alice-Agnes","email":"","affiliations":[{"id":36958,"text":"LMU Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":911692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Dave","contributorId":343685,"corporation":false,"usgs":false,"family":"May","given":"Dave","email":"","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":911693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":911694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257801,"text":"70257801 - 2024 - Leveraging local habitat suitability models to enhance restoration benefits for species of conservation concern","interactions":[],"lastModifiedDate":"2024-11-22T15:57:55.679077","indexId":"70257801","displayToPublicDate":"2024-08-24T06:47:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging local habitat suitability models to enhance restoration benefits for species of conservation concern","docAbstract":"Efforts to restore habitats and conserve wildlife species face many challenges that are exacerbated by limited funding and resources. Habitat restoration actions are often conducted across a range of habitat conditions, with limited information available to predict potential outcomes among local sites and identify those that may lead to the greatest returns on investment. Using the Gunnison sage-grouse (Centrocercus minimus) as a case study, we leveraged existing resource selection function models to identify areas of high restoration potential across landscapes with variable habitat conditions and habitat-use responses. We also tested how this information could be used to improve restoration planning. We simulated change in model covariates across crucial habitats for a suite of restoration actions to generate heatmaps of relative habitat suitability improvement potential, then assessed the degree to which use of these heatmaps to guide placement of restoration actions could improve suitability outcomes. We also simulated new or worsening plant invasions and projected the resulting loss or degradation of habitats across space. We found substantial spatial variation in projected changes to habitat suitability and new habitat created, both across and among crucial habitats. Use of our heatmaps to target placement of restoration actions improved habitat suitability nearly fourfold and increased new habitat created more than 15-fold, compared to placements unguided by heatmaps. Our decision-support products identified areas of high restoration potential across landscapes with variable habitat conditions and habitat-use responses. We demonstrate their utility for strategic targeting of habitat restoration actions, facilitating optimal allocation of limited management resources to benefit species of conservation concern.
The purpose of this report is to provide the Marine Corps with an annual summary of abundance, breeding activity, demography, and habitat use of endangered Least Bell’s Vireos (Vireo bellii pusillus) at Marine Corps Base Camp Pendleton (MCBCP or “Base”). Surveys for the Least Bell's Vireo were completed at MCBCP, California, between April 4 and July 12, 2022. Core survey areas and a subset of non-core areas in drainages containing riparian habitat suitable for vireos were surveyed two to four times. We detected 571 territorial male vireos and 14 transient vireos in core survey areas. An additional 90 territorial male vireos and 2 transients were detected in non-core survey areas. Transient vireos were detected on 7 of the 11 drainages/sites surveyed (core and non-core areas). Of the vireo territories in core areas, 90 percent were on the four most populated drainages, with the Santa Margarita River containing 73 percent of all territories in areas surveyed on Base. In core areas, 81 percent of male vireos were confirmed as paired; 61 percent of male vireos in non-core areas were confirmed as paired.
The number of documented Least Bell’s Vireo territories in core survey areas on MCBCP increased 4 percent from 2021 to 2022. In three core survey area drainages, the number of territories increased by at least two, and in five core survey area drainages, the number of vireo territories decreased by at least two between 2021 and 2022. The increase in the number of vireo territories on MCBCP was consistent with population changes at the lower San Luis Rey River (7-percent increase), but not with Marine Corps Air Station, Camp Pendleton (10-percent decrease).
A wildfire in July 2021 burned approximately 22 hectares of vireo habitat on the Santa Margarita River. There was no difference in the number of vireo territories within the fire perimeter before the fire (three territories in 2021) and after the fire (three territories in 2022).
Most core-area vireos (52 percent, including transients) used mixed willow (Salix spp.) riparian habitat. An additional 8 percent of birds occupied willow habitat co-dominated by Western sycamores (Platanus racemosa) or Fremont cottonwoods (Populus fremontii). Riparian scrub composed of mule fat (Baccharis salicifolia), sandbar willow (S. exigua), or blue elderberry (Sambucus mexicana) was used at 37 percent of vireo territories. Upland scrub was used by 2 percent of the vireos, and 1 percent of vireo territories were in drier habitats co-dominated by coast live oak (Quercus agrifolia) and sycamore.
In 2019, MCBCP began operating an artificial seep along the Santa Margarita River; then, in 2021, two additional artificial seeps became operational. The artificial seeps pumped water to the surface starting in March and ending in August each year during daylight hours and were designed to increase the amount of surface water to enhance Southwestern Willow Flycatcher (Empidonax traillii extimus) breeding habitat. Although this enhancement was designed to benefit flycatchers, few flycatchers have inhabited the seep areas within the past several years; therefore, vireos were selected as a surrogate species to determine effects of the habitat enhancement. This report presents the third year of analyses of vireo and vegetation response to the artificial seeps.
We sampled vegetation in two Seep sites and two Reference sites to determine the effects of surface water enhancement by seep pumps installed along the Santa Margarita River. Total vegetation cover below 2 meters (m) was greater at Seep sites than at Reference sites. Conversely, there was more non-native vegetation cover above 2 m (from 2 to 4 m) at Reference sites than at Seep sites. Soil moisture was greater at Seep sites than at Reference sites and decreased with increasing distance from the seep outlets. Soil moisture was positively correlated with total foliage cover and woody cover at most height categories. Soil moisture was not correlated with total herbaceous cover at any height category, although it was positively correlated with native herbaceous cover from 1 to 2 m and negatively correlated with non-native cover from 2 to 4 m. The number of vireo fledglings produced per egg was positively correlated with woody cover from 0 to 2 m but negatively correlated with herbaceous cover from 0 to 2 m. The number of fledglings produced per pair was negatively correlated with herbaceous and non-native vegetation cover below 2 m.
The U.S. Geological Survey has been color banding Least Bell’s Vireos on Marine Corps Base Camp Pendleton since 1995. By the end of 2021, 978 Least Bell’s Vireos had been color banded on Base. In 2022, we continued to color band and resight color banded Least Bell’s Vireos to evaluate adult site fidelity, between-year movement, and the effect of surface-water enhancement on vireo site fidelity and between-year movement. We banded 135 Least Bell's Vireos for the first time during the 2022 season. Birds banded included 4 adult vireos and 131 juveniles. All adult vireos were banded with unique color combinations. The juvenile vireos (all nestlings) were banded with a single gold numbered federal band on the left leg.
There were 43 Least Bell's Vireos banded before the 2022 breeding season that were resighted and identified on Base in 2022. Of these vireos, 39 were banded on Base, 3 were originally banded on the San Luis Rey River, and 1 was banded at Marine Corps Air Station, Camp Pendleton. Adult birds of known age ranged from 1 to at least 7 years old.
Base-wide survival of vireos was affected by sex, age, and year. Males had a significantly higher survival rate than females. Adults had a higher survival rate than first-year vireos. Survival for adults and first-year birds was lowest from 2020 to 2021 and highest from 2012 to 2013. The return rate of adult vireos to Seep or Reference sites was not affected by whether they were originally banded at a Seep versus Reference site.
Most of the returning adult vireos showed strong between-year site fidelity. Of the adults detected in 2021 and 2022, 89 percent (92 percent of males; 67 percent of females) returned to within 100 m of their previous territory. The average between-year movement for returning adult vireos was 0.1±0.2 kilometers (km). The average movement of first-year vireos detected in 2022 that fledged from a known nest on MCBCP in 2021 was 1.6±1.8 km.
Vireo territory density at the Seep and Reference sites was similar before the seep pumps were installed. Although vireo territory density at Seep sites appeared greater than at Reference sites after the seep pumps were installed, the difference was not significant.
We monitored Least Bell’s Vireo pairs to evaluate the effects of surface-water enhancement on nest success and breeding productivity. We monitored vireo nesting activity at 25 territories in 2 Seep sites and 25 territories in 2 Reference sites between March 31 and July 28. All territories except one were occupied by pairs, and all were “fully monitored,” meaning all nesting attempts were monitored at these territories. During the monitoring period, 97 nests (49 in Seep sites and 48 in Reference sites) were monitored.
Breeding productivity was similar at the Seep and Reference sites (2.7 and 3.3 young fledged per pair, respectively), although more pairs at Reference sites than Seep sites fledged at least one young (96 versus 76 percent, respectively). There were no other differences in breeding productivity between Seep site pairs and Reference site pairs. According to the best model, daily nest survival in 2022 was not related to whether the territory was in a Seep versus a Reference site. Completed nests at the Seep sites had similar fledging success as nests at Reference sites in 2022. At Seep sites, 56 percent of nests fledged young whereas 67 percent of Reference nests successfully fledged young. Predation was believed to be the primary source of nest failure at both sites. Predation accounted for 80 percent and 73 percent of nest failures at Seep and Reference sites, respectively. Failure of the remaining nests was attributed to infertile eggs and other unknown causes.
Vireos placed their nests in 12 plant species in 2022. We detected no differences in nest placement between successful and unsuccessful vireo nests or between Seep and Reference sites.
Precipitation appeared to play a role in fluctuations in the vireo population on MCBCP; however, it could not be directly linked to annual vireo breeding productivity. One possible factor that may be confounding the relationship between vireo breeding productivity and precipitation may be nest parasitism by Brown-headed Cowbirds (Molothrus ater) in the region, especially on the nearby San Luis Rey River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241006","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":"Lynn, S., Treadwell, M., and Kus, B.E., 2024, Distribution, abundance, and breeding activities of the Least Bell's Vireo at Marine Corps Base Camp Pendleton, California—2022 annual report: U.S. Geological Survey Open-File Report 2024–1006, 82 p., https://doi.org/10.3133/ofr20241006.","productDescription":"x, 82 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-147619","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":433041,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241006/full"},{"id":433040,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1006/images"},{"id":433039,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1006/ofr20241006.xml"},{"id":433038,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1006/ofr20241006.pdf","text":"Report","size":"16 MB"},{"id":433037,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1006/coverthb.jpg"}],"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 -118.00752092448062,\n 33.74785275971904\n ],\n [\n -118.00752092448062,\n 33.11976647292282\n ],\n [\n -116.85834882258109,\n 33.11976647292282\n ],\n [\n -116.85834882258109,\n 33.74785275971904\n ],\n [\n -118.00752092448062,\n 33.74785275971904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
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Sacramento, California 95819
Plant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.
The Netherlands, Western Europe.
We used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.
We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.
Our model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.
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2024 - Distribution, abundance, and breeding activities of the Least Bell's Vireo at Marine Corps Base Camp Pendleton, California—2021 annual report","interactions":[],"lastModifiedDate":"2024-08-26T10:53:04.660649","indexId":"ofr20231096","displayToPublicDate":"2024-08-23T10:32:26","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":"2023-1096","displayTitle":"Distribution, Abundance, and Breeding Activities of the Least Bell's Vireo at Marine Corps Base Camp Pendleton, California—2021 Annual Report","title":"Distribution, abundance, and breeding activities of the Least Bell's Vireo at Marine Corps Base Camp Pendleton, California—2021 annual report","docAbstract":"The purpose of this report is to provide the Marine Corps with an annual summary of abundance, breeding activity, demography, and habitat use of endangered Least Bell’s Vireos (Vireo bellii pusillus) at Marine Corps Base Camp Pendleton (MCBCP or “Base”). Surveys for the Least Bell's Vireo were completed at MCBCP, California, between April 5 and July 13, 2021. Core survey areas and a subset of non-core areas in drainages containing riparian habitat suitable for vireos were surveyed three to four times. We detected 551 territorial male vireos and 26 transient vireos in core survey areas. An additional 98 territorial male vireos were detected in non-core survey areas. Transient vireos were detected on 8 of the 10 drainages/sites surveyed (core and non-core areas). Of the vireo territories in core areas, 89 percent were on the four most populated drainages, with the Santa Margarita River containing 70 percent of all territories in areas surveyed on Base. In core areas, 75 percent of male vireos were confirmed as paired; 76 percent of male vireos in non-core areas were confirmed as paired.
The number of documented Least Bell’s Vireo territories in core survey areas on MCBCP decreased 18 percent from 2020 to 2021. The number of territories in all but two core survey area drainages decreased by one or more between 2020 and 2021. The decrease in vireo numbers on MCBCP (18 percent) was consistent with population changes in surrounding areas, including the lower San Luis Rey River (24-percent decrease) and the middle San Luis Rey River (6-percent decrease).
Most core-area vireo territories (59 percent of males) were in willow (Salix spp.) riparian habitat. An additional 7 percent of birds occupied willow habitat co-dominated by Western sycamores (Platanus racemosa) or Fremont cottonwoods (Populus fremontii). Of all the territories surveyed, 25 percent were in riparian scrub dominated by mule fat (Baccharis salicifolia) or sandbar willow (S. exigua). Upland scrub was used by 8 percent of vireos; 1 percent of vireo territories were in non-native vegetation, and less than 1 percent of vireo territories were in alder or drier habitats co-dominated by coast live oak (Quercus agrifolia) and sycamore.
In 2019, MCBCP began operating an artificial seep along the Santa Margarita River; then, in 2021, two additional artificial seeps became operational. The artificial seeps pumped water to the surface starting in March and ending in August each year during daylight hours and were designed to increase the amount of surface water present to enhance Southwestern Willow Flycatcher (Empidonax traillii extimus) breeding habitat. Although this enhancement was designed to benefit flycatchers, few flycatchers have inhabited the seep areas within the past several years; therefore, vireos were selected as a surrogate species to determine effects of the habitat enhancement. This report presents the second year of analyses of vireo and vegetation response to the artificial seeps.
We sampled vegetation in two Seep sites and two Reference sites to determine the effects of a new water diversion dam that was completed in 2019 and two seep pumps that were installed to enhance surface water along the Santa Margarita River in 2019 and 2021. We measured higher total vegetation cover below 2 meters (m) at Seep sites than at Reference sites and lower total vegetation cover above 5 m at Seep sites than at Reference sites. Native herbaceous cover was also higher below 4 m at Seep sites than at Reference sites. Woody cover was lower above 5 m at Seep sites than at Reference sites. Soil moisture did not differ between Seep and Reference sites.
The U.S. Geological Survey has been color banding Least Bell’s Vireos on Marine Corps Base Camp Pendleton since 1995. In 2021, we continued to color band and resight color banded Least Bell’s Vireos to evaluate adult site fidelity, between-year movement, and the effect of surface-water enhancement on vireo site fidelity and between-year movement. We banded 164 Least Bell's Vireos for the first time during the 2021 season. Birds banded included 3 adult vireos and 161 juvenile vireos. All adult vireos were banded with unique color combinations. The juvenile vireos (all nestlings) were banded with a single gold numbered federal band on the right leg.
There were 52 Least Bell's Vireos banded before the 2021 breeding season that were resighted and identified on Base in 2021. Of these vireos, 45 were banded on Base, 6 were originally banded on the San Luis Rey River, and 1 was banded at Marine Corps Air Station, Camp Pendleton. Adult birds of known age ranged from 1 to at least 7 years old.
Base-wide survival of vireos was affected by sex, age, and year. Males had a slightly but significantly higher survival rate than females. Adults had a higher survival rate than first-year vireos. Survival of both adults and first-year birds was high from 2007 to 2008 and from 2012 to 2013 and low from 2020 to 2021. The return rate of adult vireos to Seep or Reference sites ranged from 45 to 57 percent.
Most returning adult vireos showed strong between-year site fidelity. Of the adults present in 2020 and 2021, 84 percent (94 percent of males; no females) returned to within 100 m of their previous territory. The average between-year movement for returning adult vireos was 0.1±0.2 kilometer (km). The average movement of first-year vireos detected in 2021 that fledged from a known nest on MCBCP in 2020 was 1.1±0.7 km.
We monitored Least Bell's Vireo pairs to evaluate the effects of surface-water enhancement on nest success and breeding productivity. Vireos were monitored at two Seep sites and two Reference sites. Early in 2021, a seep was installed in a 2020 Reference site, which changed the status of this monitoring site from Reference to Seep.
Nesting activity was monitored between April 5 and July 22 in 50 territories within the Seep and Reference sites (25 at Seep sites and 25 at Reference sites). All territories, except one, were occupied by pairs and all were fully monitored, meaning all nesting attempts were monitored at these territories. During the monitoring period, 97 nests (42 in Seep sites and 55 in Reference sites) were monitored.
Breeding productivity was similar at the Seep site and Reference sites (3.6 and 3.4 young per pair, respectively), with 84 percent of Seep pairs and 88 percent of Reference pairs successfully fledging at least one young in 2021. Seep sites had a higher proportion of all eggs that hatched and also a higher proportion of nests with eggs that hatched than Reference sites. Seep sites and References sites had similar proportions of hatchlings that fledged and nests with hatchlings that fledged. According to the best model, daily nest survival in 2021 was higher in Seep sites than in Reference sites. Completed nests at the Seep site were more likely to be successful than nests at Reference sites in 2021. At Seep sites, 75 percent of nests fledged young, whereas 53 percent of nests at Reference successfully fledged young. Vireos at Reference sites had to expend more energy in extra nest-building and egg-laying to produce a similar number of young as vireos at Seep sites. Predation was believed to be the primary source of nest failure at both sites. Predation accounted for 100 percent and 83 percent of nest failures at Seep and Reference sites, respectively. Failure of the remaining nests was attributed to infertile eggs and other unknown causes.
There were 11 plant species used as hosts for vireo nests in 2021. Successful vireo nests at Reference sites were further from the edge of host plants (closer to the center) and further from the edge of the nest plant clump than unsuccessful nests. Vireo nests at Seep sites were further from the edge of the host plant and the nest plant clump than vireo nests at Reference sites.
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819