We hypothesize that aquatic ecosystem services are likely to be inequitably accessible and addressing this hypothesis requires systematic assessment at regional and national scales. We used existing data from large-scale aquatic monitoring programs (National Coastal Condition Assessment, National Lakes Assessment) to examine relationships between ecosystem condition, approximating a subset of cultural and provisioning services, and inequality (population below poverty level, minority population). We also assessed whether monitoring sites equitably represented the gradient of socioeconomic backgrounds. Several water quality indicators were associated with significantly different minority and low-income percentages; however, the effect size was generally small, with the exception of nitrogen condition status. Minority communities were somewhat under-represented when comparing the distribution of all census blocks to those in proximity to monitoring sites. Analyses were sensitive to the skewed distribution of monitoring sites with a low frequency of observations at the more socially vulnerable part of the gradient. We discuss implications of these findings for improving the representation of vulnerable communities in large-scale monitoring programs.
In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well-suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.
Delineating wildlife population boundaries is important for effective population monitoring and management. The bobcat (Lynx rufus) is a highly mobile generalist carnivore that is ecologically and economically important. We sampled 1225 bobcats harvested in South Dakota, USA (2014–2019), of which 878 were retained to assess genetic diversity and infer population genetic structure using 17 microsatellite loci. We assigned individuals to genetic clusters (K) using spatial and nonspatial Bayesian clustering algorithms and quantified differentiation (FST and GST″) among clusters. We found support for population genetic structure at K = 2 and K = 4, with pairwise FST and GST″ values indicating weak to moderate differentiation, respectively, among clusters. For K = 2, eastern and western clusters aligned closely with historical bobcat management units and were consistent with a longitudinal suture zone for bobcats previously identified in the Great Plains. We did not observe patterns of population genetic structure aligning with major rivers or highways. Genetic divergence observed at K = 4 aligned roughly with ecoregion breaks and may be associated with environmental gradients, but additional sampling with more precise locational data may be necessary to validate these patterns. Our findings reveal that cryptic population structure may occur in highly mobile and broadly distributed generalist carnivores, highlighting the importance of considering population structure when establishing population monitoring programs or harvest regulations. Our study further demonstrates that for elusive furbearers, harvest can provide an efficient, broad-scale sampling approach for genetic population assessments.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11411","usgsCitation":"Fetherston, S.C., Lonsinger, R.C., Perkins, L.B., Lehman, C.P., Adams, J., and Waits, L., 2024, Genetic analysis of harvest samples reveals population structure in a highly mobile generalist carnivore: Ecology and Evolution, v. 14, no. 5, e11411, 13 p., https://doi.org/10.1002/ece3.11411.","productDescription":"e11411, 13 p.","ipdsId":"IP-157245","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11411","text":"Publisher Index Page"},{"id":433052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.76565071228751,\n 46.8024638543848\n ],\n [\n -104.76565071228751,\n 42.035583213643264\n ],\n [\n -95.53713508728745,\n 42.035583213643264\n ],\n [\n -95.53713508728745,\n 46.8024638543848\n ],\n [\n -104.76565071228751,\n 46.8024638543848\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Fetherston, Stuart C.","contributorId":341222,"corporation":false,"usgs":false,"family":"Fetherston","given":"Stuart","email":"","middleInitial":"C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Lora B.","contributorId":341223,"corporation":false,"usgs":false,"family":"Perkins","given":"Lora","email":"","middleInitial":"B.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":908107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Chadwick P.","contributorId":341224,"corporation":false,"usgs":false,"family":"Lehman","given":"Chadwick","email":"","middleInitial":"P.","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":908108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Jennifer R.","contributorId":341225,"corporation":false,"usgs":false,"family":"Adams","given":"Jennifer R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":908109,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":341226,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":908110,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254286,"text":"sir20245031 - 2024 - Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016","interactions":[],"lastModifiedDate":"2026-02-03T18:24:23.163301","indexId":"sir20245031","displayToPublicDate":"2024-05-22T11:33:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5031","displayTitle":"Magnitude and Frequency of Floods in the Coastal Plain Region of Louisiana, 2016","title":"Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016","docAbstract":"To improve flood-frequency estimates for rural streams in the Coastal Plain region of Louisiana, generalized least-squares regression techniques were used to relate corresponding annual exceedance probability streamflows for 211 streamgages in the region to a suite of explanatory variables that include physical, climatic, pedologic, and land-use characteristics of the streamgage drainage area. The resulting generalized least-squares models can be used to estimate selected annual exceedance probability streamflows for rural ungaged locations in the Coastal Plain region of Louisiana. For the 211 streamgages in the Coastal Plain region of Louisiana and surrounding States, annual peak-streamflow data available through the 2016 water year were used in this study. Two unique flood regions, the Mississippi Alluvial Plain and Coastal Plain, were identified as separate hydrologic regions based on statistical evaluation and significance of categorical variables representing the regions regressed against the 1-percent annual exceedance probability streamflow (the 100-year flood). Regional regression equations for estimating annual exceedance probability streamflow for the Mississippi Alluvial Plain region have been previously published; therefore, the purpose of this study was to generate updated regional regression equations for the Coastal Plain region of Louisiana. The final regression models used drainage area and channel slope as explanatory variables based on performance metrics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245031","issn":"2328-0328","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Ensminger, P.A., Wagner, D.M., and Whaling, A., 2024, Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016: U.S. Geological Survey Scientific Investigations Report 2024–5031, 18 p., https://doi.org/10.3133/sir20245031.","productDescription":"Report: viii, 18 p.; 2 Data Releases","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-091992","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":428761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5031/coverthb.jpg"},{"id":428762,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5031/images"},{"id":428766,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":499458,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117008.htm","linkFileType":{"id":5,"text":"html"}},{"id":428767,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B48P2W","text":"USGS data release","linkHelpText":"Flood-frequency of rural, non-tidal streams in Louisiana and part of Arkansas, Mississippi, and Texas, 1877–2016"},{"id":428765,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245031/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5031 HTML"},{"id":428764,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5031 XML"},{"id":428763,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.pdf","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5031"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.00967112876856,\n 29.444945586186265\n ],\n [\n -88.68056894510033,\n 29.126157783971323\n ],\n [\n -88.60834825216723,\n 33.49872733992268\n ],\n [\n -89.99512218551115,\n 34.151214622331636\n ],\n [\n -90.94796783046621,\n 34.514518506267706\n ],\n [\n -93.48068301952253,\n 34.58384293733218\n ],\n [\n -94.52066293605651,\n 33.519758483995176\n ],\n [\n -94.62647823892294,\n 29.923162538843826\n ],\n [\n -94.00967112876856,\n 29.444945586186265\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-024-47872-7","usgsCitation":"Weiskopf, S.R., Isbell, F., Arce-Plata, M.I., Di Marco, M., Harfoot, M., Johnson, J., Lerman, S.B., Miller, B.W., Morelli, T.L., Mori, A.S., Weng, E., and Ferrier, S., 2024, Biodiversity loss reduces global terrestrial carbon storage: Nature Communications, v. 15, 4354, 12 p., https://doi.org/10.1038/s41467-024-47872-7.","productDescription":"4354, 12 p.","ipdsId":"IP-152335","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439525,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-024-47872-7","text":"Publisher Index Page"},{"id":434954,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WUFMU","text":"USGS data release","linkHelpText":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050"},{"id":429349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2024-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":901599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Isbell, Forest","contributorId":271280,"corporation":false,"usgs":false,"family":"Isbell","given":"Forest","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":901600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arce-Plata, Maria Isabel","contributorId":271276,"corporation":false,"usgs":false,"family":"Arce-Plata","given":"Maria","email":"","middleInitial":"Isabel","affiliations":[{"id":54487,"text":"University of Montreal","active":true,"usgs":false}],"preferred":false,"id":901601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Di Marco, Moreno","contributorId":336960,"corporation":false,"usgs":false,"family":"Di Marco","given":"Moreno","email":"","affiliations":[{"id":35391,"text":"Sapienza University of Rome","active":true,"usgs":false}],"preferred":false,"id":901602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harfoot, Mike","contributorId":271279,"corporation":false,"usgs":false,"family":"Harfoot","given":"Mike","email":"","affiliations":[{"id":56332,"text":"UNEP WCMC","active":true,"usgs":false}],"preferred":false,"id":901603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Justin A.","contributorId":211868,"corporation":false,"usgs":false,"family":"Johnson","given":"Justin A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":901604,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lerman, Susannah B.","contributorId":171615,"corporation":false,"usgs":false,"family":"Lerman","given":"Susannah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":901605,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":901606,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":901607,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mori, Akira S.","contributorId":271281,"corporation":false,"usgs":false,"family":"Mori","given":"Akira","email":"","middleInitial":"S.","affiliations":[{"id":49222,"text":"Yokohama National University","active":true,"usgs":false}],"preferred":false,"id":901608,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weng, Ensheng 0000-0002-1858-4847","orcid":"https://orcid.org/0000-0002-1858-4847","contributorId":267936,"corporation":false,"usgs":false,"family":"Weng","given":"Ensheng","email":"","affiliations":[{"id":49221,"text":"NASA Goddard Institute for Space Studies","active":true,"usgs":false}],"preferred":false,"id":901609,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ferrier, Simon 0000-0001-7884-2388","orcid":"https://orcid.org/0000-0001-7884-2388","contributorId":245542,"corporation":false,"usgs":false,"family":"Ferrier","given":"Simon","email":"","affiliations":[{"id":49219,"text":"Commonwealth Scientific and Industrial Research Organisation","active":true,"usgs":false}],"preferred":false,"id":901610,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70254632,"text":"70254632 - 2024 - The SCEC/USGS community stress drop validation study using the 2019 Ridgecrest earthquake sequence","interactions":[],"lastModifiedDate":"2024-06-06T14:45:23.008816","indexId":"70254632","displayToPublicDate":"2024-05-22T09:41:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"The SCEC/USGS community stress drop validation study using the 2019 Ridgecrest earthquake sequence","docAbstract":"We introduce a community stress drop validation study using the 2019 Ridgecrest, California, earthquake sequence, in which researchers are invited to use a common dataset to independently estimate comparable measurements using a variety of methods. Stress drop is the change in average shear stress on a fault during earthquake rupture, and as such is a key parameter in many ground motion, rupture simulation, and source physics problems in earthquake science. Spectral stress drop is commonly estimated by fitting the shape of the radiated energy spectrum, yet estimates for an individual earthquake made by different studies can vary hugely. In this community study, sponsored jointly by the U. S. Geological Survey and Southern/Statewide California Earthquake Center, we seek to understand the sources of variability and uncertainty in earthquake stress drop through quantitative comparison of submitted stress drops. The publicly available dataset consists of nearly 13,000 earthquakes of M1 to 7 from two weeks of the 2019 Ridgecrest sequence recorded on stations within 1-degree. As a community study, findings are shared through workshops and meetings and all are invited to join at any time, at any interest level.
","language":"English","publisher":"McGill Library","doi":"10.26443/seismica.v3i1.1009","usgsCitation":"Baltay Sundstrom, A.S., Abercrombie, R., Chu, S.X., and Taira, T., 2024, The SCEC/USGS community stress drop validation study using the 2019 Ridgecrest earthquake sequence: Seismica, v. 3, no. 1, 1009, 12 p., https://doi.org/10.26443/seismica.v3i1.1009.","productDescription":"1009, 12 p.","ipdsId":"IP-154164","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":439528,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v3i1.1009","text":"Publisher Index Page"},{"id":429572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":902126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abercrombie, Rachel E.","contributorId":293131,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel E.","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":902127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chu, Shanna Xianhui 0000-0001-5974-183X","orcid":"https://orcid.org/0000-0001-5974-183X","contributorId":337161,"corporation":false,"usgs":true,"family":"Chu","given":"Shanna","email":"","middleInitial":"Xianhui","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":902128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taira, Taka’aki 0000-0002-6170-797X","orcid":"https://orcid.org/0000-0002-6170-797X","contributorId":222985,"corporation":false,"usgs":false,"family":"Taira","given":"Taka’aki","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":902129,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259161,"text":"70259161 - 2024 - Ecological dissimilarity matters more than geographical distance when predicting land surface indicators using machine learning","interactions":[],"lastModifiedDate":"2024-09-30T10:57:59.054915","indexId":"70259161","displayToPublicDate":"2024-05-22T05:55:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Ecological dissimilarity matters more than geographical distance when predicting land surface indicators using machine learning","docAbstract":"Accurately predicting species’ distributions is critical for the management and conservation of fish and wildlife populations. Joint Species Distribution Models (JSDMs) account for dependencies between species often ignored by traditional species distribution models. We evaluated how a JSDM approach could improve predictive strength for stream fish communities within large watersheds (the Chesapeake Bay Watershed, USA), using a cross-validation study of JSDMs fit to data from over 50 species. Our results suggest that conditional predictions from JSDMs have the potential to make large improvements in predictive accuracy for many species, particularly for more generalist species where single species models may not perform well. For some species there was no added explanatory effect from conditional information, most of which already exhibited strong marginal predictive ability. For several rare species there were significant improvements in occurrence predictions, while the results for two invasive species considered did not show the same improvements. Overall, the optimal number of species to condition upon, as well as the effects of conditioning upon an increasing number of species, varied widely among species.
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twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":901678,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254412,"text":"70254412 - 2024 - Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection","interactions":[],"lastModifiedDate":"2024-05-23T11:41:43.245011","indexId":"70254412","displayToPublicDate":"2024-05-21T06:36:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection","docAbstract":"Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (Odocoileus hemionus) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (n = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; n = 8) and others in which CWD was not detected (n = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32/91 deaths attributed to CWD and 27/91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than starved deer. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals who are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around 4 months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.
Ten state wildlife management agencies in the United States, including six within the Southeast, have delayed their spring wild turkey (Meleagris gallopavo) hunting seasons since 2017 by five or more days to address concerns related to the potential effects of hunting on wild turkey seasonal productivity. One hypothesis posits that if the spring hunting season is too early, there may be insufficient time for males to breed hens before being harvested, thus leading to reduced seasonal productivity. We conducted an experiment to determine whether delaying the wild turkey hunting season by 2 weeks in south-middle Tennessee would affect various reproductive rates. In 2021 and 2022, the Tennessee Fish and Wildlife Commission experimentally delayed the spring hunting season to open 14 days later than the traditional date (the Saturday closest to 1 April) in Giles, Lawrence, and Wayne counties. We monitored reproductive rates from 2017 to 2022 in these three counties as well as two adjacent counties, Bedford and Maury, that were not delayed. We used a Before-After-Control-Impact design to analyze the proportion of hens nesting, clutch size, hatchability, nest success, poult survival and hen survival with linear mixed-effect models and AIC model selection to detect relationships between the 14-day delay and reproductive parameters. We detected no relationship (p > .05) between the 14-day delay and any individual reproductive parameter. In addition, recruitment (hen poults per hen that survived until the next breeding season) was very low (<0.5) and did not increase because of the 14-day delay. The traditional Tennessee start date had been in place since 1986 while the turkey harvest increased markedly until about 2006 and more recently stabilized. Our data indicate that moving the start of the hunting season from a period just prior to peak nest initiation to 2 weeks later, to coincide with a period just prior to peak nest incubation initiation, resulted in no change to productivity or populations in wild turkey flocks in south-middle Tennessee.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11390","usgsCitation":"Quehl, J.O., Phillips, L.M., Johnson, V.M., Harper, C.A., Clark, J.D., Shields, R.D., and Buehler, D., 2024, Assessing wild turkey productivity before and after a 14-day delay in the start date of the spring hunting season in Tennessee: Ecology and Evolution, v. 14, no. 5, e11390, 16 p., https://doi.org/10.1002/ece3.11390.","productDescription":"e11390, 16 p.","ipdsId":"IP-160505","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":439555,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.11390","text":"External Repository"},{"id":432858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Quehl, Joseph O.","contributorId":342921,"corporation":false,"usgs":false,"family":"Quehl","given":"Joseph","email":"","middleInitial":"O.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":910484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Lindsey M.","contributorId":342923,"corporation":false,"usgs":false,"family":"Phillips","given":"Lindsey","email":"","middleInitial":"M.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":910485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Vincent M.","contributorId":342925,"corporation":false,"usgs":false,"family":"Johnson","given":"Vincent","email":"","middleInitial":"M.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":910486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harper, Craig A.","contributorId":146944,"corporation":false,"usgs":false,"family":"Harper","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":910487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":910488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shields, Roger D.","contributorId":342928,"corporation":false,"usgs":false,"family":"Shields","given":"Roger","email":"","middleInitial":"D.","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":910489,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buehler, David A.","contributorId":274719,"corporation":false,"usgs":false,"family":"Buehler","given":"David A.","affiliations":[{"id":56640,"text":"University of Tennesse","active":true,"usgs":false}],"preferred":false,"id":910490,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255130,"text":"70255130 - 2024 - Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences","interactions":[],"lastModifiedDate":"2024-06-12T13:55:34.610741","indexId":"70255130","displayToPublicDate":"2024-05-20T08:51:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences","docAbstract":"Estimating distributions for cryptic and highly range-restricted species induces unique challenges for species distribution modeling. In particular, bioclimatic covariates that are typically used to model species ranges at regional and continental scales may not show strong variation at scales of 100s and 10s of meters. This limits both the likelihood and usefulness of correlated occurrence to data typically used in distribution models. Here, we present analyses of species distributions, at 100 × 100 m resolution, for a highly range restricted salamander species (Shenandoah salamander, Plethodon shenandoah) and a closely related congener (red-backed salamander, Plethodon cinereus). We combined data across multiple survey types, account for seasonal variation in availability of our target species, and control for repeated surveys at locations– all typical challenges in range-scale monitoring datasets. We fit distribution models using generalized additive models that account for spatial covariates as well as unexplained spatial variation and spatial uncertainty. Our model accommodates different survey protocols using offsets and incorporates temporal variation in detection and availability resulting from survey-specific variation in temperature and precipitation. Our spatial random effect was crucial in identifying small-scale differences in the occurrence of each species and provides cell-specific estimates of uncertainty in the density of salamanders across the range. Counts of both species were seen to increase in the 3 days following a precipitation event. However, P. cinereus were observed even in extremely wet conditions, while surface activity of P. shenandoah was associated with a more narrow range. Our results demonstrate how a flexible analytical approach improves estimates of both distribution and uncertainty, and identify key abiotic relationships, even at small spatial scales and when scales of empirical data are mismatched. While our approach is especially valuable for species with small ranges, controlling for spatial autocorrelation, estimating spatial uncertainty, and incorporating survey-specific information in estimates can improve the reliability of distribution models in general.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11262","usgsCitation":"Werba, J.A., Miller, D., Brand, A., and Campbell Grant, E.H., 2024, Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences: Ecology and Evolution, v. 14, no. 5, e11262, 13 p., https://doi.org/10.1002/ece3.11262.","productDescription":"e11262, 13 p.","ipdsId":"IP-154028","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":439561,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.11262","text":"External Repository"},{"id":430009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Werba, Jo Avital 0000-0002-5295-7790","orcid":"https://orcid.org/0000-0002-5295-7790","contributorId":338728,"corporation":false,"usgs":true,"family":"Werba","given":"Jo","email":"","middleInitial":"Avital","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David A. W.","contributorId":332095,"corporation":false,"usgs":false,"family":"Miller","given":"David A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":903497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":304281,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254422,"text":"70254422 - 2024 - Accuracy, accessibility, and institutional capacity shape the utility of habitat models for managing and conserving rare plants on western public lands","interactions":[],"lastModifiedDate":"2024-05-23T12:06:27.449674","indexId":"70254422","displayToPublicDate":"2024-05-20T07:03:25","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy, accessibility, and institutional capacity shape the utility of habitat models for managing and conserving rare plants on western public lands","docAbstract":"Public lands are often managed for multiple uses ranging from energy development to rare plant conservation. Habitat models can help land managers assess and mitigate potential effects of projects on rare plants, but it is unclear how models are currently being used. Our goal was to better understand how staff in the Bureau of Land Management currently use habitat models to inform their decisions, and perceived challenges and benefits associated with that use. We first examined litigation documents to determine whether the agency has been challenged on its use of data for rare plants and found no relevant legal challenges. Second, we analyzed model use in National Environmental Policy Act (NEPA) documents and found no clear citations of habitat models. Finally, we conducted interviews with agency staff who analyze potential effects of proposed actions on rare plants in NEPA documents. The primary challenges interviewees faced in using models related to data organization and access, model quality and accuracy, and institutional capacity. Interviewees believed models could be used more to inform decisions and actions to conserve rare plants and rare plant habitat on public lands and recommended improving staff access to models, creating models for additional species, and addressing staffing limitations.
Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2).
","language":"English","publisher":"MDPI","doi":"10.3390/rs16101801","usgsCitation":"Nagler, P.L., Sall, I., Gomez-Sapiens, M., Barreto-Muñoz, A., Jarchow, C.J., Flessa, K.W., and Didan, K., 2024, Greenness and actual evapotranspiration in the unrestored riparian corridor of the Colorado River Delta in response to in-channel water deliveries in 2021 and 2022: Remote Sensing, v. 16, no. 10, 1801, 36 p., https://doi.org/10.3390/rs16101801.","productDescription":"1801, 36 p.","ipdsId":"IP-159485","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495053,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16101801","text":"Publisher Index Page"},{"id":494727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"Colorado River and Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.70373688763536,\n 32.71761400400064\n ],\n [\n -114.91753138575248,\n 32.615612719042275\n ],\n [\n -115.12542877690827,\n 32.29643421023498\n ],\n [\n -115.29207031009817,\n 32.062671657105\n ],\n [\n -114.77701945034427,\n 31.29475716550462\n ],\n [\n -114.11333757418808,\n 31.39877597118165\n ],\n [\n -114.7621430899238,\n 32.2396086059703\n ],\n [\n -114.70373688763536,\n 32.71761400400064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":947107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":947108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gomez-Sapiens, Martha","contributorId":195954,"corporation":false,"usgs":false,"family":"Gomez-Sapiens","given":"Martha","email":"","affiliations":[],"preferred":false,"id":947109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":947111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarchow, Christopher J.","contributorId":360495,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher","middleInitial":"J.","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":947112,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flessa, Karl W.","contributorId":175308,"corporation":false,"usgs":false,"family":"Flessa","given":"Karl","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":947110,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":947113,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254332,"text":"sir20235135 - 2024 - Reservoir evolution, downstream sediment transport, downstream channel change, and synthesis of geomorphic responses of Fall Creek and Middle Fork Willamette River to water years 2012–18 streambed drawdowns at Fall Creek Lake, Oregon","interactions":[],"lastModifiedDate":"2026-01-30T19:36:55.747271","indexId":"sir20235135","displayToPublicDate":"2024-05-17T15:00:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5135","displayTitle":"Reservoir Evolution, Downstream Sediment Transport, Downstream Channel Change, and Synthesis of Geomorphic Responses of Fall Creek and Middle Fork Willamette River to Water Years 2012–18 Streambed Drawdowns at Fall Creek Lake, Oregon","title":"Reservoir evolution, downstream sediment transport, downstream channel change, and synthesis of geomorphic responses of Fall Creek and Middle Fork Willamette River to water years 2012–18 streambed drawdowns at Fall Creek Lake, Oregon","docAbstract":"Fall Creek Dam impounds Fall Creek Lake, a 10-kilometer-long reservoir in western Oregon and is operated by the U.S. Army Corps of Engineers (USACE) primarily for flood-risk management (or flood control) in late autumn through early spring months, as well as for water quality, irrigation, recreation, and habitat in late spring through early autumn. Since 2011 (water year [WY] 2012), Fall Creek Lake has been temporarily drawn down each year to facilitate downstream passage of juvenile spring Chinook salmon (Oncorhynchus tshawytscha) through the 55-meter (m) high dam. This annual dam operation is temporary, typically lasting about 1–2 weeks from WY 2012 through 2020 (drawdown operations in WY 2022–24 have increased to more than 6 weeks). Drawdown of the reservoir results in lake levels being lowered to the elevation near the historical, pre-dam streambed. The annual streambed drawdowns of WY 2012–18 have improved fish passage and led the USACE to formally adopt streambed drawdowns as part of annual operations at Fall Creek Dam. However, temporarily lowering the lake to streambed creates free-flowing conditions in the reservoir that result in the erosion and episodic export of predominantly sand and finer-grained sediments (less than 2 millimeters [mm]) to the lower gravel-bed reaches of Fall Creek and the Middle Fork Willamette River. The introduction of large volumes of sand and finer-grain sediment into the dam-regulated reaches downstream from Fall Creek Dam prompted questions about the geomorphic responses to annual streambed drawdowns within Fall Creek Lake and downstream reaches along Fall Creek and the Middle Fork Willamette River. The U.S. Geological Survey (USGS) in partnership with USACE initiated a comprehensive geomorphic and sediment transport investigation to assess the coupled processes of reservoir erosion, sediment evacuation from Fall Creek Lake, and patterns of sediment transport and deposition in reaches downstream from the Fall Creek Dam that have resulted from annual streambed drawdowns.
The purpose of this report is to systematically describe the processes of sediment erosion, transport, and deposition at Fall Creek Lake and geomorphic interactions between reaches upstream and downstream from Fall Creek Dam that relate to dam operations. Specifically, this report focuses on evaluating geomorphic responses to streambed drawdowns from WY 2012 through 2018 and placing drawdown-induced geomorphic responses within the broader context of physiographic and historical conditions and dam operations of Fall Creek and Middle Fork Willamette Rivers. Key objectives for this study were to characterize changes in reservoir morphology and substrate at Fall Creek Lake, describe the character and temporal pattern of sediment transport downstream from Fall Creek Dam, characterize geomorphic changes in channel reaches downstream from the Fall Creek Dam, and relate these data to the annual streambed drawdowns of WY 2012–18. This study uses multiple independent monitoring and measurement approaches to assess site, reach, and river-scale geomorphic responses to drawdowns to inform dam and reservoir management. Patterns and processes of reservoir evolution were assessed with geomorphic mapping and volumetric analyses of topography through comparison of multiple digital surface models (DSMs). Just downstream from Fall Creek Dam, analyses of sediment export from the reservoir focused on suspended sediment but also incorporated bedload analyses to assess sediment sizes. Geomorphic assessments downstream from the dam used reach-scale and site-scale approaches to document changes in channel morphology and substrate, including site measurements of sand and finer-grained sediment deposition and in-channel bed-material, volumetric change analyses from comparison of digital elevation models (DEMs), and repeat geomorphic mapping. Findings from this study inform river management and dam operations by providing an understanding of (1) coupled upstream-downstream geomorphic responses to the Fall Creek Lake streambed drawdowns, (2) geomorphic responses of Fall Creek Lake streambed drawdowns in comparison to drawdowns at other large dams, (3) controls on reservoir erosion and downstream geomorphic responses, and (4) implications for future hydrogeomorphic changes that may result from continued drawdowns and monitoring activities to assess those changes.
To understand the volume and distribution of sediment accumulation in Fall Creek Lake since dam closure in 1965, decadal-scale sedimentation patterns (spanning approximately 1965–2016) are evaluated using a combination of storage curve analyses and geomorphic mapping. Short-term (drawdown event-scale) patterns of erosion, sedimentation, and sediment export downstream are evaluated using a combination of geomorphic mapping and change detection analyses that quantify the distribution and total volume of sediment erosion and deposition within Fall Creek Lake.
Geomorphic mapping of reservoir topography and analyses of historical datasets reveals four categories of landforms and sediment processes within Fall Creek Lake related to lake level operations:
Where the reservoir floor is mapped for this study as pelagic (deep water), deposition up to 3 meters (m) thick by lacustrine processes and burial of pre-dam topography with deposits thinning toward the edges of the valley floor and upstream areas of reservoir are observed. Despite over 50 years of sediment accumulation since dam construction, the main stream channels of Fall and Winberry Creeks (or reservoir thalwegs) through the reservoir are well defined, though their distinct morphology is likely influenced by a long history of recurring historical drawdowns to or near streambed since dam construction. Unregulated streamflow and sediment transport through the reservoir primarily are confined to these channels during the streambed drawdown periods. Erosional channel-like features created by drawdowns are carved through underlying, unconsolidated reservoir floor sediments and are most prominent in the lower reservoir below minimum conservation pool (the low pool elevation during winter flood season); sediment generated from the formation of these drawdown channels is more likely to be transported through and out of the reservoir than sediment deposits along the reservoir hillslopes at the valley margins that are separated from main channels by areas of low-gradient reservoir floor. Morphologic changes in the lower reservoir topography between January 2012 and November 2016 indicate overall net erosion of about 129,500 cubic meters (m3). The most prominent geomorphic changes occurred along the main channels of Fall and Winberry Creeks near the Fall Creek Dam where incision, lateral migration, and slumping banks resulted in vertical and lateral adjustments to channel position, whereas most changes fell below the detectable limit on higher-elevation reservoir floor surfaces except where erosion occurred along features mapped as drawdown channels.
USGS implemented a sediment monitoring program in WY 2013–18 to evaluate the quantity and character of reservoir sediment exported from Fall Creek Lake during streambed drawdowns. Turbidity and suspended sediments were monitored annually autumn through spring to span the WY 2013–18 streambed drawdowns; however, unequal monitoring timeframes each year reduced the ability to compare results and factors affecting sediment export from the reservoir difficult between years. These data were originally measured to develop regressions and compute suspended-sediment loads (SSL). Bedload sediment monitoring from a cableway at the Fall Creek streamgage was completed in the autumn-winter of WY 2013 and 2017. The limited number of samples and presumed variability in sediment supply from the reservoir precluded construction of streamflow and bedload discharge relations to compute more than instantaneous bedload.
Sand and finer-grained silts and clays were transported from the reservoir in suspension, though some coarser grains (up to 32 mm) were also mobilized and transported downstream from the dam as bedload. Observations of increased sediment transport downstream from Fall Creek Dam coincided with lake levels approaching about 3 m (10 feet [ft] or elevation 690 ft) above the streambed regulating outlets. Suspended-sediment loads computed for the full monitoring periods WY 2013–18 at the Fall Creek streamgage, located 1.4 kilometers (km) downstream from Fall Creek Dam, range from 54,700 metric tons (t) in WY 2013 to 13,900 t in WY 2018. Although the total annual SSL varied from year to year, the overall seasonal patterns of suspended sediment transport throughout each year were similar during monitoring in WY 2013-18. Suspended-sediment loads were low prior to the drawdown, then increased rapidly as lake levels lowered and approached the streambed. In the weeks following the drawdown period, as pool levels were increased, SSL remained slightly elevated above pre-drawdown levels but generally declined through the following winter and spring except during streamflow-driven pulses of suspended-sediment transport. WY 2013 had the greatest total computed SSL for each streambed drawdown and partial-year monitoring period. SSL computed for the partial-year period have generally decreased since WY 2013 and have varied by about 6,800 t with the exception of WY 2014. WY 2014 SSL reflects anomalously low sediment export due to low streamflows and freezing conditions that stabilized reservoir floor deposits. Bedload measurements in the short 1.4-km reach between Fall Creek Dam and the Fall Creek streamgage showed an inverse correlation between bedload transport rates and discharge, which probably reflects diminishing supply of coarse-sized sediment. Sand was more abundant (60–100 percent) than gravel in bedload samples confirming sand and finer-grained sediment dominated sediment evacuated from the reservoir during streambed drawdowns at Fall Creek Lake.
In the days, weeks, and months following streambed drawdown operations at Fall Creek Dam through WY 2018, sites downstream from the dam displayed a variety of geomorphic responses to reservoir sediment delivery within the main channel and overbank areas. Evaluation of streambed elevations at two streamgages located 1.4 km downstream from the dam on Fall Creek and 16.3 km downstream from the dam on the Middle Fork Willamette River indicated the effects of drawdown sediment on bed elevations were modest and transient. Repeat particle size measurements (October 2015 and September 2016) at five sites along Fall Creek and the Middle Fork Willamette River showed similar grain-sized distributions that do not reveal substantial deposition of fine-grained sediment related to the WY 2016 streambed drawdown. Altogether, these findings indicate that transport capacity in the main, low-flow channels of Fall Creek and Middle Fork Willamette River during WY 2012–18 was sufficient to mobilize and evacuate reservoir sediments from streambed drawdowns or other bank material and tributary sources. However, other monitoring for this study indicate low-velocity zones in off-channel areas are prime locations for sand and finer-grain sediment deposition. Patterns of overbank sediment accumulation indicate that the magnitude and timing of overbank deposition on bars and low-elevation floodplain varies with proximity to the dam, geomorphic setting, streamflows, and other factors. Sand and finer-grained reservoir sediments carried as suspended-sediment load in the reaches downstream from Fall Creek Dam were deposited in overbank areas as observed with clay-horizon markers during WY 2016–17. Overbank deposition quantified with Geomorphic Change Detection (GCD) software evaluated landform-scale patterns of erosion and deposition using repeat light detection and ranging (lidar) surveys at two sites in the Upper Fall Creek reach and one site in the Jasper reach for 3 years (2012–15) and one site in the Clearwater reach for 6 years (2009–15). Deposition thickness and spatial patterns from the GCD analysis were variable; some sites had dispersed but measurable deposition while at others, deposition was highly localized and exceeded 1 m in depth. Patterns of overbank deposition illustrate interactions among bar morphology, local hydraulics, and suspended-sediment transport dynamics that can create patches of highly localized deposition. The measured deposition at the two Fall Creek GCD sites likely resulted from reservoir sediments released from Fall Creek Lake during streambed drawdowns in WY 2016 and 2017 because the limited sediment inputs from bank material (geomorphically laterally stable reach) or tributaries (no significant tributaries) provided few other sediment sources. On the Middle Fork Willamette River, observed patterns of overbank deposition could reflect sediment sourced from upstream tributaries, bank erosion, or Fall Creek Lake streambed drawdown operations.
Despite the introduction of several thousand tons of reservoir sediment delivered from the Fall Creek Lake streambed drawdowns to below-dam river corridors, reach-scale mapping of channel features downstream from Fall Creek Dam shows minimal evidence of changes in channel planform or landforms that can be attributed to a drawdowns in WY 2012–16. On Upper Fall Creek reach, widespread increases in gravel bars or other in-channel sediment did not result from the five streambed drawdowns. The main changes attributable to sediment releases from Fall Creek Lake were localized increases in vegetated bar area, particularly on channel margin areas where sand and finer-grain sediment was deposited and rapidly colonized by vegetation. The area of mapped secondary water features decreased between 2005 and 2016, but that may be due to lower discharges depicted in the 2016 aerial photographs and less mapped area of inundation. Primary changes along the Lower Fall Creek reach include a 6.4 percent decrease in area of secondary water features between 2011 and 2016, and a nearly twofold increase in the area of unvegetated bars. Immediately downstream from the Fall Creek confluence, there were negligible changes in the location and areas of vegetated bars and the main wetted channel between 2005 and 2016, and local increases in bar area cannot be attributed solely to deposition of reservoir sediments from Fall Creek Lake because (1) areas along the Middle Fork Willamette River just upstream from the Fall Creek confluence display similar type and magnitude of changes and (2) some of the increases at the confluence area pre-date the drawdowns. The cumulative effect of sediment releases from Fall Creek Lake streambed drawdowns from WY 2012 to 2016 on downstream channel planform and landforms are modest compared to the river-scale transformations and planform changes that occurred in the decades following dam construction.
Multiple aspects of Fall Creek Dam infrastructure and operations exert first-order controls on the magnitudes of reservoir erosion that occur during the streambed drawdowns and ultimately determine the sediment delivery to downstream reaches. Key aspects of the dam and its operations that are most relevant to assessing geomorphic responses to streambed drawdowns include the (1) dam infrastructure, including configuration and size of regulating outlets and their proximity to the streambed which dictates the capacity and competence of the river to deliver sediment to downstream reaches and mode of sediment transport as suspended-sediment load or bedload; (2) frequency of historical drawdowns and long-term, year-round dam operations and lake level management, which partly dictate reservoir morphology and locations and magnitudes of readily erodible materials; (3) dam operations and hydroclimatic conditions during the streambed drawdown (including length of the drawdown and streamflows entering the reservoir), which directly control the timing, duration and magnitude of reservoir erosion and sediment evacuation; and (4) dam operations following the streambed drawdown operation that regulate streamflows (and thereby sediment transport conditions) downstream of Fall Creek Dam which primarily reflect interactions between hydroclimatic conditions and flood control operations.
Patterns of sediment erosion and evacuation observed in this study at Fall Creek Lake from WY 2012–18 suggest that reservoir erosion during annual streambed drawdowns may remain similar or decrease in future years assuming (1) annual streambed drawdown operations are implemented in similar manner as the WY 2012–18 drawdowns (in terms of duration, late autumn or early winter implementation, rate of pool-level lowering to reach streambed, and other factors), (2) streambed drawdowns coincide with similar conditions as were observed WY 2012–18 (similar sediment yield into reservoir, low reservoir inflows, limited precipitation, moderate air temperature), and (3) no major geomorphic changes in the main reservoir channels of Fall and Winberry Creeks occur (for example, channel avulsion). Under such conditions, it is hypothesized that the stream channel within the reservoir would achieve a quasi-equilibrium state with respect to annual influx and export of sediment and aided by the substantial amount of in-channel bedrock, will remain laterally stable without erosion across reservoir deposits.
Patterns of sediment transport measured at the Fall Creek streamgage downstream from Fall Creek Dam provide insight into the potential effects of future streambed drawdowns at Fall Creek Lake. Analyses of suspended sediment measured in WY 2013–18 show a major reduction in suspended-sediment loads between WY 2013 and later years, indicating streamflows transporting sediment through the reservoir to downstream reaches during streambed drawdowns have become supply limited. The 6-year suspended-sediment monitoring and sampling program is insufficient to make predictions about future sediment transport conditions because of uneven monitoring periods and varying controls on reservoir sediment erosion. It is likely that future suspended-sediment loads will be variable but similar to those observed in WY 2015–18 if operational, climatic, and geomorphological factors remain similar to those monitored WY 2015–18. Suspended-sediment loads downstream from Fall Creek Lake will likely remain highest when regulating outlets are fully open and Fall Creek is free flowing with the reservoir fully drained with little to no residual pool. Over time, it is possible that the suspended-sediment loads would reflect mobilization of reservoir sediment deposited in the previous year rather than erosion of sediment deposited years or decades earlier. Bedload is likely to remain a small fraction of the total sediment load evacuated from the reservoir and is relatively modest compared with pre-dam bedload transport rates because most coarse sediment remains trapped by the dam.
If sediment releases from Fall Creek Lake and ensuing streamflow conditions follow a similar pattern in the future as was assessed in this study spanning WY 2012–18, near-term geomorphic adjustments downstream of the dam are expected to be modest. Barring major operational, climatic, and geomorphological changes, local site-scale deposition on bars, overbank areas, or off-channel features that persists several months after the streambed drawdown will likely continue to be highly variable, ranging from negligible to several centimeters of deposition. At the landform-scale, low velocity areas nearest to Fall Creek Dam will likely continue to undergo rapid deposition immediately during and after a streambed drawdown event, similar to patterns observed for WY 2012–18. Some of the sediment entering these off-channel features and margin areas may be temporarily stored, then later remobilized and dispersed farther downstream. But if newly deposited sediment persists through the following spring, there is a greater likelihood that local vegetation will establish, reinforce deposited material, and trap sediment during later drawdowns. The reach-scale geomorphic changes may become more apparent if (1) streambed drawdowns continued for several decades, and geomorphic changes were measured at decadal scales or (2) the amount of sediment introduced to downstream reaches substantially increased and (or) sediment transport capacity decreased. The continued streamflow regulation of Fall Creek Dam after sediment releases provides an opportunity to strategically manage streamflows during and after the streambed drawdowns to minimize downstream sediment impacts and ensure other operational thresholds are satisfied.
This study provides a comprehensive foundation of datasets and geomorphic analyses to inform dam operations at Fall Creek Lake, monitor sediment transport downstream, and consider operational schemes for future drawdowns. The datasets from this study also provide baselines of sediment transport and geomorphic conditions to assess future changes in reservoir and downstream environments. Future monitoring could be tailored to address specific questions regarding the long-term geomorphic effects of streambed drawdowns on fluvial habitats, flood hazards, cultural resources, or downstream water quality. Future monitoring activities could focus on the relevant geomorphic processes and spatial domains within the three categories used for this study: (1) reservoir erosion and net sediment evacuation, (2) sediment delivery to downstream reaches, including magnitude and temporal pattern of sediment transport, and (3) geomorphic responses of downstream reaches to sediment delivery. Specifically, high priority future monitoring activities could include:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
The U.S. Geological Survey (USGS) delivers high-quality data, technologies, and decision-support tools to help managers both reduce existing populations and control the spread of dreissenid mussels. The USGS researches ecology, biology, risk assessment, and early detection and rapid response methods; provides decision support; and develops and tests control measures.
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We surveyed for Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the upper San Luis Rey River near Lake Henshaw in Santa Ysabel, California, in 2023. Surveys were completed at four locations: three downstream from Lake Henshaw, where surveys previously occurred from 2015 to 2022 (Rey River Ranch [RRR], Cleveland National Forest [CNF], Vista Irrigation District [VID]), and one at VID Lake Henshaw (VLH) that has been surveyed annually since 2018. There were a minimum of 74 territorial flycatchers detected at 1 location (VLH), and 12 transient flycatchers of unknown subspecies detected at 2 locations (CNF and VLH). At VLH, we detected a minimum of 31 males, 40 females, and 3 flycatchers of unknown sex. In total, 51 territories were established, containing 40 pairs and 11 flycatchers of undetermined breeding status (8 males and 3 flycatchers of unknown sex). Of the 40 pairs, 9–11 pairs were monogamous (1 male and 1 female), and 29–31 pairs were polygynous (1 male paired with more than 1 female). For the first time since annual surveys began in 2015, no territorial flycatchers were detected downstream from Lake Henshaw. Brown-headed cowbirds (Molothrus ater; cowbird) were detected at all four survey locations. No banded flycatchers were detected during surveys.
Flycatchers used three habitat types in the survey area: (1) mixed willow riparian, (2) willow-cottonwood, and (3) oak-sycamore. Of the flycatcher locations, 86 percent were in habitat characterized as mixed willow riparian, and 95 percent were in habitat with greater than 95-percent native plant cover. Exotic vegetation was not prevalent in the survey area.
There were five nests incidentally located during surveys: one failed, one was seen with eggs on the last visit, and the outcome of the remaining three nests was unknown. One of these nests was parasitized by cowbirds, and a second nest was suspected to contain a cowbird nestling. Adult flycatchers in two territories were observed feeding cowbird fledglings. No juvenile flycatchers were detected during surveys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1194","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2024, Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Upper San Luis Rey River, San Diego County, California—2023 data summary: U.S. Geological Survey Data Report 1194, 13 p., https://doi.org/10.3133/dr1194.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-159183","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":428713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VC5Y4","text":"USGS Data Release","description":"Howell, S.L., and Kus, B.E., 2022, Southwestern willow flycatcher (Empidonax traillii extimus) surveys and nest monitoring in San Diego County, California (ver. 3.0, January 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P96VC5Y4.","linkHelpText":"Southwestern willow flycatcher (Empidonax traillii extimus) surveys and nest monitoring in San Diego County, California (ver. 3.0, January 2024)"},{"id":428708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1194/coverthb.jpg"},{"id":428709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1194/dr1194.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":428710,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1194/dr1194.xml"},{"id":428711,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1194/images"},{"id":428712,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1194/full"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Upper San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.67091887744101,\n 33.35\n ],\n [\n -117.01171343822598,\n 33.35\n ],\n [\n -117.01171343822598,\n 33.1167\n ],\n [\n -116.67091887744101,\n 33.1167\n ],\n [\n -116.67091887744101,\n 33.35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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Limited data, time, and funding lead conservation managers to make difficult choices in managing species recovery. Coupled dynamical models are relied upon for decision support, but their application to empirical predator-prey systems has generally been restricted to small, tractable species. To broaden their use in conservation decision-making, we developed a model suitable for predicting the population dynamics of a larger apex carnivore and its prey. We selected southern sea otters (Enhydra lutris nereis) and their primary estuarine prey as our case study and parameterized the dynamical model with data on sea otter, clam, and crab abundances; predator-prey interactions; and sea otter bioenergetics collected from Elkhorn Slough, CA. Our model, having integrated all these salient factors, was able to successfully reproduce trends in taxa abundance as well as shifts in sea otter diet composition and energy intake rates. Rich data inputs allow the model to predict population dynamics over realistic temporal scales not only for the site of data collection, but also for similar estuaries uncolonized by sea otters. Based on model projections parameterized with prey survey data from two such estuaries, Tomales Bay and Drakes Estero, we predict the sites could support over 160 sea otters and may hold potential to further species recovery. In systems with good data availability, the model has high predictive power and can provide multi-taxa projections useful for making informed management decisions.
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N.","contributorId":337991,"corporation":false,"usgs":false,"family":"Castorani","given":"Max","email":"","middleInitial":"C. N.","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":902853,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Becker, Benjamin H.","contributorId":207275,"corporation":false,"usgs":false,"family":"Becker","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":37509,"text":"Point Reyes National Seashore, Point Reyes Station, CA","active":true,"usgs":false}],"preferred":true,"id":902854,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hughes, Brent B.","contributorId":201240,"corporation":false,"usgs":false,"family":"Hughes","given":"Brent","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":902855,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70254909,"text":"70254909 - 2024 - Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)","interactions":[],"lastModifiedDate":"2024-09-16T15:30:09.8264","indexId":"70254909","displayToPublicDate":"2024-05-16T09:06:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)","title":"Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)","docAbstract":"Although tool use may enhance resource utilization, its fitness benefits are difficult to measure. By examining longitudinal data from 196 radio-tagged southern sea otters (Enhydra lutris nereis), we found that tool-using individuals, particularly females, gained access to larger and/or harder-shelled prey. These mechanical advantages translated to reduced tooth damage during food processing. We also found that tool use diminishes trade-offs between access to different prey, tooth condition, and energy intake, all of which are dependent on the relative prey availability in the environment. Tool use allowed individuals to maintain energetic requirements through the processing of alternative prey that are typically inaccessible with biting alone, suggesting that this behavior is a necessity for the survival of some otters in environments where preferred prey are depleted.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.adj6608","usgsCitation":"Law, C.J., Tinker, M., Fujii, J.A., Nicholson, T., Staedler, M.M., Tomoleoni, J.A., Young, C., and Mehta, R.S., 2024, Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis): Science, v. 384, no. 6697, p. 798-802, https://doi.org/10.1126/science.adj6608.","productDescription":"5 p.","startPage":"798","endPage":"802","ipdsId":"IP-162451","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":429739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"384","issue":"6697","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Law, Chris J.","contributorId":337988,"corporation":false,"usgs":false,"family":"Law","given":"Chris","email":"","middleInitial":"J.","affiliations":[{"id":81059,"text":"University of Washington, University of Texas","active":true,"usgs":false}],"preferred":false,"id":902838,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":221787,"corporation":false,"usgs":false,"family":"Tinker","given":"M. Tim","affiliations":[{"id":40428,"text":"University of California, Santa Cruz; former USGS PI","active":true,"usgs":false}],"preferred":false,"id":902839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":902840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicholson, Teri","contributorId":214308,"corporation":false,"usgs":false,"family":"Nicholson","given":"Teri","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":902841,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":902842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":902843,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, Colleen","contributorId":337989,"corporation":false,"usgs":false,"family":"Young","given":"Colleen","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":902844,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mehta, Rita S.","contributorId":189577,"corporation":false,"usgs":false,"family":"Mehta","given":"Rita","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":902845,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254235,"text":"cir1517 - 2024 - U.S. Geological Survey data strategy 2023–33","interactions":[],"lastModifiedDate":"2024-08-09T20:12:16.982203","indexId":"cir1517","displayToPublicDate":"2024-05-15T12:45:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1517","displayTitle":"U.S. Geological Survey Data Strategy 2023–33","title":"U.S. Geological Survey data strategy 2023–33","docAbstract":"The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publications, and data release, ultimately contributes to the transparency and availability of science. Foundational Government directives and laws, such as the Foundations for Evidence-Based Policymaking Act of 2018 (Public Law 115–435, 132 Stat. 5529) as well as Executive Order 13642, “Making Open and Machine Readable the New Default for Government Information,” provide a framework for addressing strategic data management. The USGS Data Strategy builds on that framework by outlining high-level goals and objectives that serve as a long-term, decadal guide toward achieving a broad, data-focused vision.
Benefits of the USGS Data Strategy are many. The USGS will contribute to open science by increasing efficiencies in the consistent management of valuable data assets; driving innovation that results in modernized capabilities to ensure data are analysis ready; increasing data skills across the Bureau to enhance workforce data literacy; broadening capacity to understand and address needs of stakeholders; and measuring progress in producing findable, accessible, interoperable, and reusable (FAIR) data products.
The major goals and objectives of the USGS Data Strategy promote maximizing the utility of USGS data based on stakeholder needs, promoting data innovation, coordinating common data practices, modernizing our USGS enterprise data architecture, and enhancing our data-centric culture. The goals and objectives in the strategy align with other Bureau strategic plans, guidance, and directives from the Department of the Interior and the Federal Government. This strategy is a key component to strengthen the Bureau’s data ecosystem to ensure a relevant, long-term capacity that supports internal needs and achieves its scientific mission in the most efficient and effective manner.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1517","usgsCitation":"Hutchison, V.B., Burley, T.E., Blasch, K.W., Exter, P.E., Gunther, G.L., Shipman, A.J., Kelley, C.M., and Morris, C.A., 2024, U.S. Geological Survey data strategy 2023–33: U.S. Geological Survey Circular 1517, 7 p., https://doi.org/10.3133/cir1517.","productDescription":"v, 7 p.","onlineOnly":"Y","ipdsId":"IP-154183","costCenters":[{"id":5071,"text":"Office of Administration","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true},{"id":80860,"text":"Office of the Associate Chief Information Officer","active":true,"usgs":true}],"links":[{"id":428771,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1517/full"},{"id":428770,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1517/cir1517.xml"},{"id":428769,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1517/images"},{"id":428707,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1517/cir1517.pdf","text":"Report","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1517"},{"id":428706,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1517/coverthb.jpg"}],"contact":"Director, USGS Science Analytics and Synthesis
U.S. Geological Survey
Box 25046, MS 302
Denver, CO 80225
Eye loss has been a long-standing interest in evolutionary biology. Many organisms that inhabit environments without light penetration, for example the deep sea, exhibit eye loss and thus become blind. However, water-depth distribution of eyes in marine organisms is poorly understood. Ostracods are widely distributed crustaceans, and many sighted marine ostracods have eye tubercles (lenses) on their shells. Since eye tubercles are visible on the shells illustrated in much literature, it is easy to determine their presence or absence via a literature survey. Here, we used a large Arctic-wide ostracod census dataset (Arctic Ostracode Database) to calculate the eye index (the percentage of species with eyes), and compare them with water depth and light availability. As water depth increases, eye index values decrease and become constantly zero in water deeper than 1000 m. Similar decline of sighted species with increasing depth is also known in isopods and amphipods, suggesting that it may be common in other crustaceans and perhaps in deep-sea organisms in general. We also show that eye index values increase as light availability increases. This study is the first to quantify how distributions of sighted and blind species change with light availability, giving baseline information on vision in the deep sea.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.12584","usgsCitation":"Zhang, J., Yasuhara, M., Wei, C., Tian, S.Y., Aye, K.K., Gemery, L., Cronin, T.M., Frenzel, P., and Horne, D.J., 2024, Sight and blindness: The relationship between ostracod eyes, water depth, and light availability in the Arctic Ocean: Limnology and Oceanography, v. 69, no. 6, p. 1418-1428, https://doi.org/10.1002/lno.12584.","productDescription":"11 p.","startPage":"1418","endPage":"1428","ipdsId":"IP-159915","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":439590,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.12584","text":"Publisher Index Page"},{"id":428845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Jingwen","contributorId":336196,"corporation":false,"usgs":false,"family":"Zhang","given":"Jingwen","email":"","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":900116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yasuhara, Moriaki","contributorId":178705,"corporation":false,"usgs":false,"family":"Yasuhara","given":"Moriaki","email":"","affiliations":[],"preferred":false,"id":900117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wei, Chih-Lin","contributorId":336198,"corporation":false,"usgs":false,"family":"Wei","given":"Chih-Lin","email":"","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":900118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tian, Skye Yunshu","contributorId":336200,"corporation":false,"usgs":false,"family":"Tian","given":"Skye","email":"","middleInitial":"Yunshu","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":900119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aye, Kyawt K. T.","contributorId":336202,"corporation":false,"usgs":false,"family":"Aye","given":"Kyawt","email":"","middleInitial":"K. T.","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":900120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gemery, Laura 0000-0003-1966-8732","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":245413,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":900121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cronin, Thomas M. 0000-0001-9522-3992 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-9522-3992","contributorId":304640,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":900122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frenzel, Peter","contributorId":336790,"corporation":false,"usgs":false,"family":"Frenzel","given":"Peter","affiliations":[],"preferred":false,"id":901050,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Horne, David J.","contributorId":113597,"corporation":false,"usgs":true,"family":"Horne","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":901051,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255975,"text":"70255975 - 2024 - Atlas of microscopic images of biochar using reflected light microscopy in biochar characterization","interactions":[],"lastModifiedDate":"2024-07-11T13:27:32.525256","indexId":"70255975","displayToPublicDate":"2024-05-15T08:21:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18004,"text":"Indiana Journal of Earth Sciences","onlineIssn":"2642-1550","active":true,"publicationSubtype":{"id":10}},"title":"Atlas of microscopic images of biochar using reflected light microscopy in biochar characterization","docAbstract":"Derived through the thermochemical conversion of biomass, biochar is a carbon-rich substance recognized for its significance in environmental applications and sustainable agriculture. As interest in its utilization continues to rise, it becomes crucial to comprehend how the source material and pyrolysis parameters influence the properties of biochar and, consequently, to research the suitability of various analytical methods for characterizing it. Despite the current utilization of numerous physical and chemical methods, the untapped potential of reflected light microscopy warrants further exploration.
While a few recent studies suggest a correlation between certain microscopic characteristics and selected physical and chemical properties of biochar, the data are limited and difficult to compare. This is primarily due to variations in the types of original biomass used and lack of information about pyrolysis conditions. Moreover, because only a limited number of photographs taken under a reflected light microscope are publicly available to-date, it is difficult to evaluate morphological differences between various biochars and other organic materials such as inertinites from coal, charcoal, etc.
To address limited availability of publicly available data, this “Atlas of Microscopic Images of Biochar” presents a collection of more than 300 images contributed by researchers from Poland, the United States, Canada, Australia, Brazil, and Denmark. These photomicrographs capture optical characteristics of a diverse array of biochar, demonstrating its unique morphological and structural features. This visual documentation can serve as a valuable resource for researchers, industry professionals, educators, and enthusiasts interested in investigating the complexities of biochar forms.
","language":"English","publisher":"Indiana Geological and Water Survey at Indiana University","doi":"10.14434/ijes.v6i1.37623","usgsCitation":"Drobniak, A., Mastalerz, M., Knauth, W., Adarkani, O., dos Santos, T.A., de Faria, V.C., Congo, T., Hackley, P.C., Hatcherian, J.J., Hower, J.C., Petersen, H.I., Reyes, J., and Sanei, H., 2024, Atlas of microscopic images of biochar using reflected light microscopy in biochar characterization: Indiana Journal of Earth Sciences, v. 6, 9 p., https://doi.org/10.14434/ijes.v6i1.37623.","productDescription":"9 p.","ipdsId":"IP-162580","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":439591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14434/ijes.v6i1.37623","text":"Publisher Index Page"},{"id":430954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2024-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Drobniak, Agnieszka","contributorId":330811,"corporation":false,"usgs":false,"family":"Drobniak","given":"Agnieszka","affiliations":[{"id":79025,"text":"University of Silesia in Katowice, Faculty of Natural Sciences, ul. Będzińska 60, 41-200 Sosnowiec, Poland","active":true,"usgs":false}],"preferred":false,"id":906217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastalerz, Maria","contributorId":330812,"corporation":false,"usgs":false,"family":"Mastalerz","given":"Maria","affiliations":[{"id":79026,"text":"Indiana University, Indiana Geological and Water Survey, 1001 E. 10th St., Bloomington, IN 47405, United States","active":true,"usgs":false}],"preferred":false,"id":906218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knauth, Will","contributorId":340115,"corporation":false,"usgs":false,"family":"Knauth","given":"Will","email":"","affiliations":[{"id":81483,"text":"Centre for Biomass Energy Research and Education, University of Silesia in Katowice","active":true,"usgs":false}],"preferred":false,"id":906219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adarkani, Omid","contributorId":340116,"corporation":false,"usgs":false,"family":"Adarkani","given":"Omid","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":906220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"dos Santos, Telma Ataide","contributorId":340117,"corporation":false,"usgs":false,"family":"dos Santos","given":"Telma","email":"","middleInitial":"Ataide","affiliations":[{"id":81486,"text":"TAS Petrographic Consulting & Auditing","active":true,"usgs":false}],"preferred":false,"id":906221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"de Faria, Valdeci Caio","contributorId":340118,"corporation":false,"usgs":false,"family":"de Faria","given":"Valdeci","email":"","middleInitial":"Caio","affiliations":[{"id":81487,"text":"Gerdau Ouro Branco","active":true,"usgs":false}],"preferred":false,"id":906222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Congo, Tara","contributorId":330818,"corporation":false,"usgs":false,"family":"Congo","given":"Tara","email":"","affiliations":[{"id":79031,"text":"The University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":906223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":906224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":906225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hower, James C.","contributorId":330827,"corporation":false,"usgs":false,"family":"Hower","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":79038,"text":"University of Kentucky Center for Applied Energy Research, USA","active":true,"usgs":false}],"preferred":false,"id":906226,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Petersen, Henrik I.","contributorId":330843,"corporation":false,"usgs":false,"family":"Petersen","given":"Henrik","email":"","middleInitial":"I.","affiliations":[{"id":79049,"text":"Geological Survey of Denmark and Greenland, Denmark","active":true,"usgs":false}],"preferred":false,"id":906227,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Reyes, Julito","contributorId":330844,"corporation":false,"usgs":false,"family":"Reyes","given":"Julito","affiliations":[{"id":79030,"text":"Geological Survey of Canada-Calgary","active":true,"usgs":false}],"preferred":false,"id":906228,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sanei, Hamed","contributorId":168753,"corporation":false,"usgs":false,"family":"Sanei","given":"Hamed","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":906229,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254205,"text":"sir20245040 - 2024 - Evaporation from the interior of Lake Okeechobee—A large freshwater lake in Florida, 2013–16","interactions":[],"lastModifiedDate":"2026-02-03T19:19:23.373341","indexId":"sir20245040","displayToPublicDate":"2024-05-14T14:30:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5040","displayTitle":"Evaporation From the Interior of Lake Okeechobee—A Large Freshwater Lake in Florida, 2013–16","title":"Evaporation from the interior of Lake Okeechobee—A large freshwater lake in Florida, 2013–16","docAbstract":"In 2012, a platform at the approximate center of Lake Okeechobee in central Florida was instrumented to continuously measure evaporation with the Bowen-ratio energy-budget method as part of a long-term partnership between the South Florida Water Management District and the U.S. Geological Survey. The primary goal for the study was to quantify daily rates of open-water evaporation. A secondary goal was to assess differences in evaporation rates among alternate methods and determine if instrumentation and operational expenses associated with the Bowen-ratio method could be reduced.
Mean annual evaporation from Lake Okeechobee for 2013–16 was about 1,825 millimeters per year. Annual evaporation from 2013 to 2016 was 1,760, 1,840, 1,810, and 1,890 millimeters per year, respectively. These evaporation rates are among the highest rates observed in Florida based on scientifically vetted methods such as evaporation pans, lysimeters, eddy-covariance, or Bowen-ratio methods. The high evaporation rates are largely a result of frequent clear-sky conditions over the interior of Lake Okeechobee, which allows solar radiation to reach the water surface and drive open-water evaporation. Cloud formation over the interior of Lake Okeechobee is suppressed because of a relatively large heat capacity for water that buffers convective fluxes of air that form clouds while rising and cooling.
Estimated evaporation rates obtained using five alternative methods were compared to measured Bowen-ratio energy-budget daily, monthly, and annual evaporation: the Penman, Priestly-Taylor, Mass-Transfer, Simple, and Turc equations. All five methods performed relatively well (within 10 percent of the Bowen ratio annual totals). The Penman, Priestley-Taylor, and Mass-Transfer methods captured relatively large evaporation rates that occurred in the winter due to cold fronts, because these methods account for large wind speeds and vapor pressure deficits associated with the regional cold fronts. For operational implementation, the Simple, Mass-Transfer, or Turc methods are likely preferable because of their simplicity, limited data requirements, and improved accuracy for computing monthly and annual evaporation totals. The Turc equation computed monthly evaporation within 8 percent of the Bowen-ratio method, while requiring only air temperature and solar radiation data. The Simple equation achieved similar accuracy while requiring only solar radiation data.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Groundwater quality in the western part of the Mojave Desert in San Bernardino County, California, was investigated in 2018 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program Priority Basin Project. The Mojave Basin Domestic-Supply Aquifer study unit (MOBS) region was divided into two study areas—floodplain and regional—to assess differences between the two major aquifers used for drinking water supply in the area. This assessment characterized the quality of ambient groundwater and not the quality of treated drinking water.
The study included three components: (1) a status assessment, which characterized the quality of groundwater resources used for domestic drinking-water supply in the floodplain and regional study areas; (2) a brief understanding assessment, which evaluated factors that could potentially affect the quality of groundwater used by domestic wells in the region; and (3) a comparative assessment between the groundwater resources used by domestic wells and public-supply wells in the two study areas. The domestic-well assessment was based on data collected by the U.S. Geological Survey from 48 domestic wells in January–May 2018. The public-supply assessment was based on data for samples from 322 public-supply wells in 2008–18, either collected by the U.S. Geological Survey or compiled from the California State Water Resources Control Boards Division of Drinking Water publicly available database.
Concentrations of water-quality constituents in ambient groundwater were compared to regulatory and non-regulatory benchmarks typically used by the State of California and Federal agencies as health-based or aesthetic standards for public drinking water. Relative concentrations, defined as the measured concentration divided by the benchmark concentration, were classified as high (greater than 1.0), moderate (greater than 0.5 for inorganic constituents or 0.1 for organic and special-interest constituents, and not high), or low (concentrations lower than moderate). The floodplain and regional study areas were divided into 15 and 35 grid cells, respectively, and grid-based methods were used to compute the areal proportions of the two study areas with high, moderate, or low relative concentrations of individual constituents and classes of constituents.
For the domestic-supply assessment, one or more inorganic constituents with health-based benchmarks were detected at high relative concentrations in 58 percent of the regional study area and 13 percent of the floodplain study area. The inorganic constituents with health-based benchmarks detected at high relative concentrations in the regional study area were arsenic, chromium and hexavalent chromium, fluoride, adjusted gross alpha particle activity, uranium, molybdenum, strontium, and nitrate; only arsenic was detected at high relative concentrations in the floodplain study area. One or more inorganic constituents with secondary maximum contaminant level benchmarks were detected at high concentrations in 15 and 6.7 percent of the regional and floodplain study areas, respectively. The constituents detected at high relative concentrations in the regional study area were total dissolved solids, chloride, sulfate, and iron; only total dissolved solids and sulfate were detected at high relative concentrations in the floodplain study area.
Organic constituents were not detected at moderate or high relative concentrations in either the regional or floodplain study areas. Volatile organic compounds were detected at low relative concentrations in 21 and 27 percent of the regional and floodplain study areas, respectively, and pesticides were detected at low relative concentrations in 9.1 and 20 percent of the regional and floodplain study areas, respectively. The only individual organic constituent detected in more than 10 percent of either study area was the trihalomethane trichloromethane. Total coliform bacteria were detected in 15 and 27 percent of the grid wells in the regional and floodplain study areas, respectively.
The greater prevalence of high relative concentrations of many inorganic constituents in the regional study area compared to the floodplain area likely indicates the greater diversity of geologic material at depth in aquifer material and generally finer-grained alluvium compared to the floodplain study area combined with generally older groundwater that has had more contact time with aquifer materials. In general, trace element concentrations (1) increased with increasing groundwater age, (2) increased with distance from recharge sources in the mountains, and (3) increased with closer proximity to some types of geological units. In general, groundwater from domestic wells in the floodplain study area is young, with most samples containing a component of modern groundwater based on tritium and unadjusted carbon-14 activities, whereas groundwater from domestic wells in the regional study area generally is old, with most samples having unadjusted carbon-14 ages of 5,000–40,000 years.
Public-supply wells in MOBS generally were deeper than domestic wells and presumably are in contact with older, more weathered alluvium that may have more mobile trace elements, such as arsenic or uranium. However, only 26 percent of the public-supply regional study area had high relative concentrations of inorganic constituents, compared to 58 percent for the domestic regional study area. The percentages of the public-supply and domestic floodplain study areas with high relative concentrations of inorganic constituents were 11 and 13 percent, respectively. The ages of groundwater used by public-supply and domestic wells in each study area were similar, which was not expected given the greater depth of the public-supply wells. Three potential factors may contribute to these results: (1) greater spatial footprint of domestic well network, which may result in domestic wells pumping groundwater from fractured bedrock or mineralized areas not used by public-supply wells; (2) greater pumping rates in public-supply wells, resulting in more water being withdrawn from coarse-grained, heterogeneous alluvium than finer-grained layers, which may have higher concentrations of (or more mobile) inorganic constituents; and (3) a greater degree of well management with public-supply wells, which may include pausing use of or decommissioning wells if treating or blending water is not feasible to lower constituent concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245019","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Groover, K.D., Fram, M.S., and Levy, Z.F., 2024, Status and understanding of groundwater quality in the Mojave Basin Domestic-Supply Aquifer study unit, 2018—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2024–5019, 62 p., https://doi.org/10.3133/sir20245019.","productDescription":"x, 62 p.","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-110004","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499448,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116979.htm","linkFileType":{"id":5,"text":"html"}},{"id":428611,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5019/sir20245019.XML"},{"id":428610,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5019/images"},{"id":428608,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5019"},{"id":428607,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5019/sir20245019.pdf","text":"Report","size":"14.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5019"},{"id":428606,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5019/sir20245019.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 -117.51859835894652,\n 35.183647408915874\n ],\n [\n -117.51859835894652,\n 34.28986048082601\n ],\n [\n -116.15629367144663,\n 34.28986048082601\n ],\n [\n -116.15629367144663,\n 35.183647408915874\n ],\n [\n -117.51859835894652,\n 35.183647408915874\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Thirty‐seven portable accelerometers were deployed in the eastern San Francisco Bay communities of Walnut Creek and Concord to study site response in a fault‐bounded, urban, sedimentary basin. Local earthquakes were recorded for a period of two years from 2017 to 2019 resulting in 101 well‐recorded events. Site response is estimated by two methods: the reference site spectral ratio method and a source‐site spectral inversion method. The reference site spectral ratio method allows investigation of the variability of site amplification with source azimuth and frequency. The source‐site spectral inversion method yields the best least‐squares fit to site response for a database of ground‐motion records. Both methods show substantial amplification in the Walnut Creek–Concord basin below 2 Hz indicating strong surface‐wave development. Greater amplification is seen for sources aligned along the long axis of the basin. Inversion using close‐in sources at short distances yields lower amplification at longer periods than the entire data set due to reduced surface‐wave generation for steeper angles of incidence. Inversion of site response spectra for shallow shear‐wave velocity using a global search algorithm yields VS30 values consistent with generalized mapping results based on geology and topography but with greater variability due to local site variations. 3D finite‐element modeling shows greater amplification in the Walnut Creek–Concord basin with a basin‐edge effect likely contributing to higher ground motions. Topography is also seen to lead to increased scattering and shadowing effects.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230309","usgsCitation":"Hartzell, S.H., Leeds, A.L., Ramirez-Guzman, L., Langenheim, V., and Schmitt, R.G., 2024, Site response in the Walnut Creek–Concord region of the San Francisco Bay, California: Ground motion amplification in a fault-bounded basin: Bulletin of the Seismological Society of America, v. 114, no. 5, p. 2668-2686, https://doi.org/10.1785/0120230309.","productDescription":"18 p.","startPage":"2668","endPage":"2686","ipdsId":"IP-156595","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":433493,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Walnut Creek–Concord region of San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.15,\n 38.1\n ],\n [\n -122.15,\n 37.85\n ],\n [\n -121.85,\n 37.85\n ],\n [\n -121.85,\n 38.1\n ],\n [\n -122.15,\n 38.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":912261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leeds, Alena L. 0000-0002-8756-3687 aleeds@usgs.gov","orcid":"https://orcid.org/0000-0002-8756-3687","contributorId":4077,"corporation":false,"usgs":true,"family":"Leeds","given":"Alena","email":"aleeds@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":912262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramirez-Guzman, Leonardo","contributorId":175444,"corporation":false,"usgs":false,"family":"Ramirez-Guzman","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":912263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206990,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[],"preferred":true,"id":912264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmitt, Robert G. 0000-0001-8060-1954 rschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-1954","contributorId":5611,"corporation":false,"usgs":true,"family":"Schmitt","given":"Robert","email":"rschmitt@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":912265,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}