{"pageNumber":"118","pageRowStart":"2925","pageSize":"25","recordCount":41032,"records":[{"id":70248861,"text":"70248861 - 2023 - Intramolecular carbon isotope geochemistry of butane isomers from laboratory maturation and Monte-Carlo simulations of kerogen types I, II, and III","interactions":[],"lastModifiedDate":"2023-09-25T11:44:31.032693","indexId":"70248861","displayToPublicDate":"2023-09-18T06:43:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Intramolecular carbon isotope geochemistry of butane isomers from laboratory maturation and Monte-Carlo simulations of kerogen types I, II, and III","docAbstract":"

Position-specific (PS) carbon isotope compositions of light hydrocarbons such as propane and butane isomers (n-butane and i-butane) can provide a wealth of information on the history of natural gases in the subsurface reservoirs and other environments. For PS carbon isotope analysis of butane isomers, we have established a GC-pyrolysis-GC-isotope ratio mass spectrometry method with demonstrated accuracy. With this method, we analyzed PS δ13C values of butane isomers generated from the systematic laboratory pyrolysis experiments of three different kerogen types (I, II, and III) at temperatures of 310–430 °C with corresponding thermal maturity (Easy %Ro) ranging from 0.7 to 3.3. The observed evolution in the abundances of butane isomers can be interpreted and semi-quantitatively modeled based on the abundances of different C\"singleC bonds within the kerogens at low maturity and thermal degradation of butane isomers at high maturity. The δ13C values at the central sites of both nC4 and iC4 were heavier than those at the terminal positions, similar to our previous observations of propane. Their isotopic evolution with the maturity were controlled largely by kinetic isotope effects associated with breaking of different C\"singleC bonds during the generation and degradation of butane isomers. Kinetic Monte Carlo (kMC) simulations of n-butane generated from thermal cracking of model kerogens (I, II, and III) and an oil with a series of reactions (homolytic cleavage, β-scission, radical isomerization, H-abstraction, and termination by radical recombination) provided generally consistent results with the experimental observations, although the difference in PS δ13C values between the central and terminal positions are somewhat overestimated. On the other hand, the kMC simulation with homolytic cleavage and capping reactions alone produced significant deviations from the experimental results. Re-assessment of very limited data of PS δ13C values of natural butanes with our experimental and simulation results show that biodegradation significantly increased δ13C values at the central positions, not only of propane, but also of both butane isomers. This study lays a foundation and demonstrates the potential of PS isotope geochemistry of butane isomers to further improve our understanding of the sources, and geochemical and microbial processes of light hydrocarbons in the subsurface and other natural environments.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2023.09.003","usgsCitation":"Li, X., Xie, H., Birdwell, J.E., McGovern, G., and Horita, J., 2023, Intramolecular carbon isotope geochemistry of butane isomers from laboratory maturation and Monte-Carlo simulations of kerogen types I, II, and III: Geochimica et Cosmochimica Acta, v. 360, p. 57-67, https://doi.org/10.1016/j.gca.2023.09.003.","productDescription":"11 p.","startPage":"57","endPage":"67","ipdsId":"IP-152055","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":442066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2023.09.003","text":"Publisher Index Page"},{"id":421118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"360","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Xiaoqiang","contributorId":298943,"corporation":false,"usgs":false,"family":"Li","given":"Xiaoqiang","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":883972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Hao","contributorId":243585,"corporation":false,"usgs":false,"family":"Xie","given":"Hao","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":883973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":883974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGovern, Gregory","contributorId":330092,"corporation":false,"usgs":false,"family":"McGovern","given":"Gregory","email":"","affiliations":[{"id":78810,"text":"Department of Chemistry and Physics, West Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":883975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horita, Juske","contributorId":300474,"corporation":false,"usgs":false,"family":"Horita","given":"Juske","affiliations":[{"id":32968,"text":"Oak Ridge National Laboratory, Oak Ridge, TN","active":true,"usgs":false}],"preferred":false,"id":883976,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248678,"text":"70248678 - 2023 - Early Pliocene (Zanclean) stratigraphic framework for PRISM5/PlioMIP3 time slices","interactions":[],"lastModifiedDate":"2023-11-07T16:04:50.445619","indexId":"70248678","displayToPublicDate":"2023-09-15T10:13:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Early Pliocene (Zanclean) stratigraphic framework for PRISM5/PlioMIP3 time slices","docAbstract":"

Global reconstructions of Pliocene climate provide important insights into how the climate system operates under elevated temperatures and atmospheric CO2 levels. These reconstructions have been used extensively in paleoclimate modeling experiments for comparison to simulated conditions, and as boundary conditions.Most previous work focused on the Late Pliocene interval known as the mid Piacenzian Warm Period (mPWP), the interval originally identified by the U.S. Geological Survey Pliocene Research, Interpretation and Synoptic Mapping Project (PRISM) as the PRISM interval or Mid Pliocene Warm Period. The term Mid Pliocene Warm Period is a misnomer due to changes to the geological time scale, and its use should be discontinued. The Pliocene Model Intercomparison Project (PlioMIP), now in its third phase, is expanding to include a focus on the Early Pliocene (Zanclean). PlioMIP3 experiments will allow comparison of environmental and climatic conditions before and after closure of the Central American Seaway (CAS). PlioMIP3 used the annual insolation pattern at the top of the atmosphere to determine time slices in the Zanclean that have orbital configurations that are most similar to modern. Two have been selected by PlioMIP and adopted by PRISM for inclusion in future studies: PRISM5.1 (4.474 Ma) and PRISM5.2 (4.870 Ma). Here we establish the stratigraphic framework for these Early Pliocene time slices and furnish information to help locate these intervals in proxy records of paleoenvironmental data using oxygen isotope stratigraphy, paleomagnetic stratigraphy, biostratigraphy, and biochronology (calibrated planktic foraminifer and calcareous nannofossil events).

","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.20.3.02","usgsCitation":"Dowsett, H., Robinson, M., Foley, K.M., Hunter, S., Dolan, A.M., and Tindall, J.C., 2023, Early Pliocene (Zanclean) stratigraphic framework for PRISM5/PlioMIP3 time slices: Stratigraphy, v. 20, no. 3, p. 225-231, https://doi.org/10.29041/strat.20.3.02.","productDescription":"8 p.","startPage":"225","endPage":"231","ipdsId":"IP-153122","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":420893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":883184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":261664,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":883185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":883186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunter, Steve 0000-0002-4593-6238","orcid":"https://orcid.org/0000-0002-4593-6238","contributorId":302870,"corporation":false,"usgs":false,"family":"Hunter","given":"Steve","email":"","affiliations":[{"id":40084,"text":"Leeds Univ.","active":true,"usgs":false}],"preferred":false,"id":883330,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dolan, Aisling M","contributorId":206287,"corporation":false,"usgs":false,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":883331,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tindall, Julia C.","contributorId":147376,"corporation":false,"usgs":false,"family":"Tindall","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":883332,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249845,"text":"70249845 - 2023 - What evidence exists on the ecological and physical effects of built structures in shallow, tropical coral reefs? A systematic map protocol","interactions":[],"lastModifiedDate":"2023-11-02T14:22:32.977468","indexId":"70249845","displayToPublicDate":"2023-09-15T09:19:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5897,"text":"Environmental Evidence","active":true,"publicationSubtype":{"id":10}},"title":"What evidence exists on the ecological and physical effects of built structures in shallow, tropical coral reefs? A systematic map protocol","docAbstract":"

Background

Shallow, tropical coral reefs face compounding threats from habitat degradation due to coastal development and pollution, impacts from storms and sea-level rise, and pulse disturbances like blast fishing, mining, dredging, and ship groundings that reduce coral reefs’ height and variability. One approach toward restoring coral reef structure from these threats is deploying built structures. Built structures range from engineered modules and repurposed materials to underwater sculptures and intentionally placed natural rocks. Restoration practitioners and coastal managers increasingly consider incorporating built structures, including nature-based solutions, into coral reef-related applications. Yet, synthesized evidence on the ecological and physical performance of built structure interventions across a variety of contexts (e.g., restoration, coastal protection, mitigation, tourism) is not readily available to guide decisions. To help inform management decisions, here we aim to document the global evidence base on the ecological and physical performance of built structures in shallow (≤ 30 m) tropical (35° N to 35° S latitude) coral ecosystems. The collated evidence base on use cases and associated ecological and physical outcomes of built structure interventions can help inform future consideration of built structures in reef restoration design, siting, and implementation.

Method

To discover evidence on the performance of built structures in coral reef-related applications, such as restoration, mitigation, and coastal protection, primary literature will be searched across indexing platforms, bibliographic databases, open discovery citation indexes, a web-based search engine, a novel literature discovery tool, and organizational websites. The geographic scope of the search is global, and there is no limitation to temporal scope. Primary literature will be screened first at the level of title and abstract and then at the full text level against defined eligibility criteria for the population, intervention, study type, and outcomes of interest. Metadata will be extracted from studies that pass both screening levels. The resulting data will be analyzed to determine the distribution and abundance of evidence. Results will be made publicly available and reported in a systematic map that includes a narrative description, identifies evidence clusters and gaps, and outlines future research directions on the use of built structures in coral reef-related applications.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-023-00313-2","usgsCitation":"Paxton, A., Swannack, T., Piercy, C., Altman, S., Poussard, L., Puckett, B., Storlazzi, C.D., and Viehman, T., 2023, What evidence exists on the ecological and physical effects of built structures in shallow, tropical coral reefs? A systematic map protocol: Environmental Evidence, v. 12, 19, 17 p., https://doi.org/10.1186/s13750-023-00313-2.","productDescription":"19, 17 p.","ipdsId":"IP-151595","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-023-00313-2","text":"Publisher Index Page"},{"id":422332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2023-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Paxton, Avery 0000-0002-4871-9167","orcid":"https://orcid.org/0000-0002-4871-9167","contributorId":331325,"corporation":false,"usgs":false,"family":"Paxton","given":"Avery","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":887361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swannack, Tom","contributorId":331326,"corporation":false,"usgs":false,"family":"Swannack","given":"Tom","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":887362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piercy, Candice","contributorId":331327,"corporation":false,"usgs":false,"family":"Piercy","given":"Candice","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":887363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Altman, Safra","contributorId":331328,"corporation":false,"usgs":false,"family":"Altman","given":"Safra","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":887364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poussard, Leanne","contributorId":331346,"corporation":false,"usgs":false,"family":"Poussard","given":"Leanne","email":"","affiliations":[],"preferred":false,"id":887365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Puckett, Brandon 0000-0001-9615-6242","orcid":"https://orcid.org/0000-0001-9615-6242","contributorId":331329,"corporation":false,"usgs":false,"family":"Puckett","given":"Brandon","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":887366,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":887367,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Viehman, T. Shay 0000-0001-8505-665X","orcid":"https://orcid.org/0000-0001-8505-665X","contributorId":331330,"corporation":false,"usgs":false,"family":"Viehman","given":"T. Shay","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":887368,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248481,"text":"70248481 - 2023 - Global projections of storm surges using high-resolution CMIP6 climate models","interactions":[],"lastModifiedDate":"2023-09-15T14:16:45.9677","indexId":"70248481","displayToPublicDate":"2023-09-15T09:09:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Global projections of storm surges using high-resolution CMIP6 climate models","docAbstract":"In the coming decades, coastal flooding will become more frequent due to sea-level rise and potential changes in storms. To produce global storm surge projections from 1950 to 2050, we force the Global Tide and Surge Model with a ∼25-km resolution climate model ensemble from the Coupled Model Intercomparison Project Phase 6 High Resolution Model Intercomparison Project (HighResMIP). This is the first time that such a high-resolution ensemble is used to assess changes in future storm surges across the globe. We validate the present epoch (1985–2014) against the ERA5 climate reanalysis, which shows a good overall agreement. However, there is a clear spatial bias with generally a positive bias in coastal areas along semi-enclosed seas and negative bias in equatorial regions. Comparing the future epoch (2021–2050) against the historical epoch (1951–1980), we project ensemble-median changes up to 0.1 (or 20%) in the 1 in 10-year storm surge levels. These changes are not uniform across the globe with decreases along the coast of Mediterranean and northern Africa and southern Australia and increases along the south coast of Australia and Alaska. There are also increases along (parts) of the coasts of northern Caribbean, eastern Africa, China and the Korean peninsula, but with less agreement among the HighResMIP ensemble. Information resulting from this study can be used to inform broad-scale assessment of coastal impacts under future climate change.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023EF003479","usgsCitation":"Muis, S., Aerts, J., Antolinez, J.A., Dullaart, J.C., Duong, T.M., Erikson, L.H., Haarsma, R.J., Irazoqui Apecechea, M., Mengel, M., Le Bars, D., O'Neill, A., Ranasinghe, R., Roberts, M.J., Verlaan, M., Ward, P., and Yan, K., 2023, Global projections of storm surges using high-resolution CMIP6 climate models: Earth's Future, v. 11, no. 9, e2023EF003479, 17 p., https://doi.org/10.1029/2023EF003479.","productDescription":"e2023EF003479, 17 p.","ipdsId":"IP-143098","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":442080,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023ef003479","text":"Publisher Index Page"},{"id":420834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"9","noUsgsAuthors":false,"publicationDate":"2023-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Muis, Sanne 0000-0002-8145-0171","orcid":"https://orcid.org/0000-0002-8145-0171","contributorId":305488,"corporation":false,"usgs":false,"family":"Muis","given":"Sanne","email":"","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":883062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aerts, Jeroen C. J. H.","contributorId":329702,"corporation":false,"usgs":false,"family":"Aerts","given":"Jeroen C. J. H.","affiliations":[{"id":49403,"text":"Deltares, Delft, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Antolinez, Jose A. A.","contributorId":329703,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","middleInitial":"A. A.","affiliations":[{"id":78694,"text":"TU Delft, Delft, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883064,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dullaart, Job C.","contributorId":329704,"corporation":false,"usgs":false,"family":"Dullaart","given":"Job","email":"","middleInitial":"C.","affiliations":[{"id":78695,"text":"Vrije Universiteit Amsterdam, Amsterdam, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duong, Trang Minh","contributorId":247859,"corporation":false,"usgs":false,"family":"Duong","given":"Trang","email":"","middleInitial":"Minh","affiliations":[{"id":39272,"text":"University of Twente","active":true,"usgs":false}],"preferred":false,"id":883066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":883067,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haarsma, Rein J.","contributorId":329707,"corporation":false,"usgs":false,"family":"Haarsma","given":"Rein","email":"","middleInitial":"J.","affiliations":[{"id":78698,"text":"KNMI, De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883068,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Irazoqui Apecechea, Maialen","contributorId":329708,"corporation":false,"usgs":false,"family":"Irazoqui Apecechea","given":"Maialen","email":"","affiliations":[{"id":78699,"text":"Deltares, Delft, The Netherlands; Mercator Ocean, Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":883069,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mengel, Matthias","contributorId":297268,"corporation":false,"usgs":false,"family":"Mengel","given":"Matthias","email":"","affiliations":[{"id":64334,"text":"Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":883071,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Le Bars, Dewi","contributorId":329709,"corporation":false,"usgs":false,"family":"Le Bars","given":"Dewi","email":"","affiliations":[{"id":78698,"text":"KNMI, De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883070,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":883072,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ranasinghe, Roshanka","contributorId":247857,"corporation":false,"usgs":false,"family":"Ranasinghe","given":"Roshanka","email":"","affiliations":[{"id":49677,"text":"IHE Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":883073,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Roberts, Malcolm J.","contributorId":329710,"corporation":false,"usgs":false,"family":"Roberts","given":"Malcolm","email":"","middleInitial":"J.","affiliations":[{"id":64050,"text":"Met Office Hadley Centre, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":883074,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Verlaan, Martin","contributorId":329711,"corporation":false,"usgs":false,"family":"Verlaan","given":"Martin","email":"","affiliations":[{"id":78700,"text":"Deltares, Delft, The Netherlands; TU Delft, Delft, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883075,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ward, Philip J.","contributorId":329712,"corporation":false,"usgs":false,"family":"Ward","given":"Philip J.","affiliations":[{"id":78695,"text":"Vrije Universiteit Amsterdam, Amsterdam, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883076,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yan, Kun","contributorId":329713,"corporation":false,"usgs":false,"family":"Yan","given":"Kun","email":"","affiliations":[{"id":49403,"text":"Deltares, Delft, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":883077,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70248497,"text":"70248497 - 2023 - Karst groundwater vulnerability determined by modeled age and residence time tracers","interactions":[],"lastModifiedDate":"2023-09-15T13:52:37.801283","indexId":"70248497","displayToPublicDate":"2023-09-15T08:44:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Karst groundwater vulnerability determined by modeled age and residence time tracers","docAbstract":"Karst aquifers are a vital groundwater resource globally, but features such as rapid recharge and conduit flow make them highly vulnerable to land-surface contamination. We apply environmental age tracers to the south-central Texas Edwards aquifer, a karst resource in a rapidly urbanizing and drought-prone region, to assess vulnerability to land-surface contamination and risks unique to karst aquifers. We show that vulnerability of Edwards aquifer groundwater follows similar spatial and depth patterns common to porous-media type aquifers, despite complicated karst hydrogeologic features. Shallow and unconfined parts are more vulnerable to land-surface contamination than the deeper and confined parts, although even the oldest groundwater is mixed with some recent recharge. When modeled age-tracer results are coupled with other independent geochemical tracers of water-rock interaction specific to karst settings, they can yield insight into residence time and associated vulnerability.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL102853","usgsCitation":"Musgrove, M., Jurgens, B., and Opsahl, S.P., 2023, Karst groundwater vulnerability determined by modeled age and residence time tracers: Geophysical Research Letters, v. 50, no. 18, e2023GL102853, 10 p., https://doi.org/10.1029/2023GL102853.","productDescription":"e2023GL102853, 10 p.","ipdsId":"IP-145022","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":442084,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl102853","text":"Publisher Index Page"},{"id":435175,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CWM574","text":"USGS data release","linkHelpText":"Data for karst groundwater vulnerability determined by modeled age and residence time tracers"},{"id":420830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.81367338316534,\n 30.54366239128437\n ],\n [\n -100.81367338316534,\n 28.95832731254596\n ],\n [\n -96.77473383220936,\n 28.95832731254596\n ],\n [\n -96.77473383220936,\n 30.54366239128437\n ],\n [\n -100.81367338316534,\n 30.54366239128437\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"18","noUsgsAuthors":false,"publicationDate":"2023-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Musgrove, MaryLynn 0000-0003-1607-3864","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":223710,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Opsahl, Stephen P. 0000-0002-4774-0415 sopsahl@usgs.gov","orcid":"https://orcid.org/0000-0002-4774-0415","contributorId":4713,"corporation":false,"usgs":true,"family":"Opsahl","given":"Stephen","email":"sopsahl@usgs.gov","middleInitial":"P.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883099,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248504,"text":"70248504 - 2023 - Toward probabilistic post-fire debris-flow hazard decision support","interactions":[],"lastModifiedDate":"2023-09-15T13:27:51.647204","indexId":"70248504","displayToPublicDate":"2023-09-15T08:11:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Toward probabilistic post-fire debris-flow hazard decision support","docAbstract":"

Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., ≥24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to “messaging fatigue” if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest ∼10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards.

","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-22-0188.1","usgsCitation":"Oakley, N.S., Liu, T., McGuire, L., Simpson, M., Hatchett, B.J., Tardy, A., Kean, J.W., Castellano, C., Laber, J.L., and Steinhoff, D., 2023, Toward probabilistic post-fire debris-flow hazard decision support: Bulletin of the American Meteorological Society, v. 104, no. 9, p. E1587-E1605, https://doi.org/10.1175/BAMS-D-22-0188.1.","productDescription":"19 p.","startPage":"E1587","endPage":"E1605","ipdsId":"IP-150218","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":442089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-22-0188.1","text":"Publisher Index Page"},{"id":420828,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Montecito","otherGeospatial":"Santa Ynez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.66701649030912,\n 34.452095445537736\n ],\n [\n -119.62687608675861,\n 34.446131312701965\n ],\n [\n -119.57877992754959,\n 34.44672774514967\n ],\n [\n 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S.","contributorId":197885,"corporation":false,"usgs":false,"family":"Oakley","given":"Nina","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":883104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Tao","contributorId":329738,"corporation":false,"usgs":false,"family":"Liu","given":"Tao","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":883151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, Luke","contributorId":197027,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","affiliations":[],"preferred":false,"id":883106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simpson, Matthew","contributorId":329726,"corporation":false,"usgs":false,"family":"Simpson","given":"Matthew","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":883107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatchett, Benjamin J. 0000-0003-1066-3601","orcid":"https://orcid.org/0000-0003-1066-3601","contributorId":214405,"corporation":false,"usgs":false,"family":"Hatchett","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":39033,"text":"Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":883108,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tardy, Alexander","contributorId":329727,"corporation":false,"usgs":false,"family":"Tardy","given":"Alexander","email":"","affiliations":[{"id":12788,"text":"National Weather Service","active":true,"usgs":false}],"preferred":false,"id":883109,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883110,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Castellano, Christopher","contributorId":329728,"corporation":false,"usgs":false,"family":"Castellano","given":"Christopher","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":883111,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Laber, Jayme L.","contributorId":192864,"corporation":false,"usgs":false,"family":"Laber","given":"Jayme","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":883112,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Steinhoff, Daniel","contributorId":329730,"corporation":false,"usgs":false,"family":"Steinhoff","given":"Daniel","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":883113,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70248520,"text":"70248520 - 2023 - Evaluation of replicate sampling using hierarchical spatial modeling of population surveys accounting for imperfect detectability","interactions":[],"lastModifiedDate":"2023-09-15T13:08:12.301905","indexId":"70248520","displayToPublicDate":"2023-09-15T07:46:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of replicate sampling using hierarchical spatial modeling of population surveys accounting for imperfect detectability","docAbstract":"

Effective species management and conservation benefit from knowledge of species distribution and status. Surveys to obtain that information often involve replicate sampling, which increases survey effort and costs. We simultaneously modeled species distribution, abundance and spatial correlation, and compared the uncertainty in replicate abundance estimates of the endangered palila (Loxioides bailleui) using hierarchical generalized additive models with a soap film smoother that incorporated random effects for visit. Based on survey coverage and detections, we selected the 2017 point-transect distance sampling survey on Mauna Kea, Hawai‘i Island, for our modeling. Our modeling approach allowed us to account for imperfect detections, control the effects of boundary features, and generate visit-specific density surface maps. We found that visit-specific smooths were nearly identical, indicating that little information was gained from a subsequent visit, and that most of the estimator uncertainty was derived from within-visit variability. Scaling back the palila survey to a single visit would halve the survey effort and logistical costs and increase efficiencies in data management and processing. Changing the sampling protocol warrants careful consideration and our findings may help management and regulatory agencies by maximizing efficiency and minimizing costs of surveying protocols, while providing guidelines on how to best collect information critical to species' conservation.

","language":"English","publisher":"Wiley","doi":"10.1002/wsb.1471","usgsCitation":"Camp, R.J., Asing, C.K., Banko, P.C., Berry, L., Brinck, K., Farmer, C., and Genz, A., 2023, Evaluation of replicate sampling using hierarchical spatial modeling of population surveys accounting for imperfect detectability: Wildlife Society Bulletin, v. 47, no. 3, e1471, 12 p., https://doi.org/10.1002/wsb.1471.","productDescription":"e1471, 12 p.","ipdsId":"IP-141510","costCenters":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"links":[{"id":442092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1471","text":"Publisher Index Page"},{"id":420826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Hawai'i, Mauna Kea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.46881876229043,\n 19.83012427098143\n ],\n [\n -155.54063798804242,\n 19.820472473849165\n ],\n [\n -155.5293521097099,\n 19.793927010622525\n ],\n [\n -155.50626735857531,\n 19.77075644297679\n ],\n [\n -155.4539419226703,\n 19.76448050106208\n ],\n [\n -155.45445491714,\n 19.80068445784383\n ],\n [\n -155.46881876229043,\n 19.83012427098143\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":883143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":883144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":883145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berry, Lainie","contributorId":272646,"corporation":false,"usgs":false,"family":"Berry","given":"Lainie","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":883146,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":883150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farmer, Chris","contributorId":150179,"corporation":false,"usgs":false,"family":"Farmer","given":"Chris","affiliations":[{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":883148,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Genz, Ayesha 0000-0002-2916-1436","orcid":"https://orcid.org/0000-0002-2916-1436","contributorId":196671,"corporation":false,"usgs":false,"family":"Genz","given":"Ayesha","email":"","affiliations":[],"preferred":false,"id":883149,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248492,"text":"70248492 - 2023 - Satellite-derived plant cover maps vary in performance depending on version and product","interactions":[],"lastModifiedDate":"2023-09-15T12:45:29.027447","indexId":"70248492","displayToPublicDate":"2023-09-15T07:03:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Satellite-derived plant cover maps vary in performance depending on version and product","docAbstract":"

Understanding the accuracy and appropriate application scale of satellite-derived maps of vegetation cover is essential for effective management of the vast, remote rangelands of the world. However, the underlying models are updated frequently and may combine with rapidly changing vegetation conditions to cause variations in accuracy and precision over time. We sought to assess how model performance changed between different versions of satellite-derived cover products (Rangeland Analysis Platform, RAP, and Rangeland Condition Monitoring and Assessment Protocol, RCMAP) and how the performance of LandCart compared to RAP and RCMAP. Additionally, we asked how variability in agreement between LandCart and field-based models varied with scale. We utilized an intensive dataset of grid-point intercept functional group cover data collected between 2016 and 2020 across the ∼113 kHA 2015 Soda Wildfire to 1) evaluate r2 agreement between versions of each satellite-derived product and plot-level field data and 2) assess relative standard error of agreement in cover between LandCart and continuous field-based Empirical Bayesian Kriging (EBK) regression models. Agreement between satellite- compared to field-plot values of cover (r2) increased for RCMAP Version 5.0 compared to Version 2.0, but there were negligible changes between versions of RAP. Despite this, r2 values of RCMAP and LandCart were nearly always less than RAP. Variability in agreement between EBK regression model cover and LandCart-derived cover decreased with the scale of consideration. Variability in agreement between satellite-derived cover products and field-based metrics is lowest at larger scale (mega-fire or regional) and varies from year to year and across versions, which could complicate detection of temporal changes in plant cover.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2023.110950","usgsCitation":"Applestein, C., and Germino, M., 2023, Satellite-derived plant cover maps vary in performance depending on version and product: Ecological Indicators, v. 155, 110950, 8 p., https://doi.org/10.1016/j.ecolind.2023.110950.","productDescription":"110950, 8 p.","ipdsId":"IP-152901","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":442098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2023.110950","text":"Publisher Index Page"},{"id":420825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.56338308098071,\n 43.926296989230224\n ],\n [\n -117.56338308098071,\n 42.354755589015696\n ],\n [\n -115.93309004811645,\n 42.354755589015696\n ],\n [\n -115.93309004811645,\n 43.926296989230224\n ],\n [\n -117.56338308098071,\n 43.926296989230224\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"155","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":883089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":883090,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248464,"text":"sir20105090CC - 2023 - Geology and undiscovered resource assessment of the potash-bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States","interactions":[{"subject":{"id":70248464,"text":"sir20105090CC - 2023 - Geology and undiscovered resource assessment of the potash-bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States","indexId":"sir20105090CC","publicationYear":"2023","noYear":false,"chapter":"CC","displayTitle":"Geology and Undiscovered Resource Assessment of the Potash-Bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States","title":"Geology and undiscovered resource assessment of the potash-bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States"},"predicate":"IS_PART_OF","object":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"id":1}],"isPartOf":{"id":70040436,"text":"sir20105090 - 2010 - Global mineral resource assessment","indexId":"sir20105090","publicationYear":"2010","noYear":false,"title":"Global mineral resource assessment"},"lastModifiedDate":"2026-02-23T18:16:22.356495","indexId":"sir20105090CC","displayToPublicDate":"2023-09-14T10:14:34","publicationYear":"2023","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":"2010-5090","chapter":"CC","displayTitle":"Geology and Undiscovered Resource Assessment of the Potash-Bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States","title":"Geology and undiscovered resource assessment of the potash-bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States","docAbstract":"

The U.S. Geological Survey (USGS) assessed undiscovered potash resources in the Elk Point Basin in Canada and the United States as part of a global mineral resource assessment. The Elk Point Basin is a large, Middle Devonian (Givetian) intracratonic evaporite basin covering approximately 1,200,000 square kilometers (km2) and filled mainly with marine evaporite and minor clastic sedimentary rocks that contain stratabound potash-bearing salt. The potash-bearing salt is concentrated in four stratigraphic members (Patience Lake, Belle Plaine, White Bear, and Esterhazy) in the upper 100 meters (m) of the Prairie Evaporite and are separated by beds of halite (NaCl) that contain lesser—presently non-economic—amounts of sylvite (KCl) and carnallite (KMgCl3·6H2O). The principal ore-bearing salt contains mainly sylvite. Four permissive tracts were defined that permit the presence of undiscovered stratabound potash (both sylvite- and carnallite-bearing salt) using geological criteria.

Permissive tracts are defined by the spatial extent of each stratigraphic member that is at least 1 m thick, are less than 3 kilometers (km) from the surface, contain at least 4 percent equivalent potassium oxide (K2O), and contain the currently known resources. The permissive tracts include known potash deposits and potash occurrences as wells or mines not in production and show where undiscovered potash resources may be present. Well data are used to define the extent, thickness, average K2O equivalent grades, and volumes of each member. Data were supplied by the Saskatchewan Geological Survey or were obtained from published National Instrument (NI) 43-101 technical reports and other published reports, such as annual 10-K reports or news releases.

The Elk Point Basin is the world’s largest source of potash, producing 23.0 million metric tons (Mt) of potassium chloride (KCl) (the equivalent of about 14.4 Mt of K2O) in 2018. In terms of global importance, the Elk Point Basin may contain 40 to greater than 50 percent of the world’s potash resources. Since 1962, potash companies have mined more than 1.5 trillion metric tons of ore containing 605 Mt of KCl (the equivalent of about 380 Mt of K2O). The total value of the ore produced through 2018 is on the order of $70 trillion (CAD). Potash is currently produced from eight conventional and three underground solution mines at depths ranging from 900 m to nearly 1,800 m. Estimates of the amount of potash in the Elk Point Basin vary considerably and the data and methods used in those estimations are not well documented. Known potash resources are approximately 99 billion metric tons (Bt) of ore containing 22 Bt of K2O equivalent.

As a result of new mine openings and increased production capacity at existing mines, the total production capacity of mines in the Elk Point Basin has increased significantly (to about 32.8 Mt of KCl or 22.8 Mt of K2O equivalent per year). Additional production capacity of about 31 Mt of KCl (or 17 Mt of K2O equivalent) per year could be realized over the next decade if several current (as of 2019) exploration and development projects reach production status.

Stratabound potash-bearing salt of the Prairie Evaporite presently underlies a total area of about 188,000 km2 and has a total volume of about 2,690 cubic kilometers (km3). Post-depositional solution processes considerably modified the mineralogy and presence of the potash-bearing salt. These changes had a profound effect on the volume and grade of potash resources that remained in the Prairie Evaporite and are a major consideration of exploration and mining operations as well as in this assessment of undiscovered potash resources.

This USGS assessment includes the locations and possible amounts of undiscovered potash resources in the Prairie Evaporite. Volumes for each stratigraphic member were computed using member thicknesses and areal extent modified by actual, estimated geologic loss owing to salt dissolution and extraction ratios, as well as estimated distribution of carnallite and sylvite. Both sylvite- and carnallite-bearing salts were assessed for potash in this study. The assessment uses modern published grade and tonnage data. The amount of undiscovered potash is estimated by using Monte Carlo simulations to combine volume estimates of the potash-bearing members with probability distributions for average grade and bulk density.

Mean potash grades (expressed as percentage of K2O equivalent) calculated using drill core analyses are 17.76 for the Patience Lake Member, 15.98 for the Belle Plaine Member, 10.66 for the White Bear Member, and 15.30 for the Esterhazy Member. Geologic losses reported as extraction ratios during mining may range from 27.5 to 41.6 percent and are dependent on mining method and local geologic conditions. The assessment determined that mean estimated undiscovered K2O equivalent resources for the Patience Lake, Belle Plaine, White Bear, and Esterhazy Members are 340, 220, 34, and 190 Bt, respectively, and estimated a total mean of 790 Bt for the entire Prairie Evaporite above a depth of 3 km. The total mineralized rock tonnage is estimated to be about 5,000 Bt. Most of the assessed potash is located within Saskatchewan with lesser amounts in Alberta and Manitoba as well as Montana and North Dakota within the United States.

Although carnallite is mined for potash in Europe, it has historically been avoided in mining plans for potash-producing companies in Saskatchewan because of mining, processing, and grade considerations. Carnallite-rich salt is locally present in concentrations and volumes that could be a significant resource of magnesium chloride (MgCl2) obtained as a byproduct of processing the carnallite for potash. Previously estimated reserves (not NI 43-101 compliant) of mineralized material from 1955 to 2019 are 695 Mt at 22.1 percent MgCl2. The total amount of K2O equivalent as carnallite was estimated during this USGS assessment to be about 120 Bt (or 180 Bt KCl). With uncertainties in defining the areal extent of carnallite in each of the potash-bearing members, the amount of MgCl2 as carnallite in the Elk Point Basin could be approximately 180 Bt.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105090CC","usgsCitation":"Cocker, M.D., Orris, G.J., Dunlap, P., Yang, C., and Bliss, J.D., 2023, Geology and undiscovered resource assessment of the potash-bearing, Middle Devonian (Givetian), Prairie Evaporite, Elk Point Basin, Canada and United States: U.S. Geological Survey Scientific Investigations Report 2010–5090–CC, 145 p. and data files, https://doi.org/10.3133/sir20105090cc.","productDescription":"Report: ix, 145 p.; Spatial Data; Table; Readme","numberOfPages":"145","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053948","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":420793,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2010/5090/cc/sir20105090cc_readme.txt","size":"20 KB","linkFileType":{"id":2,"text":"txt"}},{"id":420792,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5090/cc/sir20105090cc.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420791,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5090/cc/covrthb.jpg"},{"id":500441,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115404.htm","linkFileType":{"id":5,"text":"html"}},{"id":420795,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2010/5090/cc/sir20105090cc_gis.zip","text":"GIS data","size":"5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":420794,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2010/5090/cc/sir20105090cc_tableD1.xlsx","text":"Table D1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"Canada, United States","otherGeospatial":"Elk Point basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.62003370031768,\n 47.834501224842995\n ],\n [\n -101.94859207917939,\n 46.78881376731823\n ],\n [\n -99.38036924401605,\n 47.064056171229\n ],\n [\n -98.5222551132618,\n 47.90409118922611\n ],\n [\n -98.07428783462548,\n 49.274981575913074\n ],\n [\n -99.04751209813486,\n 51.323577075700086\n ],\n [\n -100.3990594914377,\n 52.712828172299055\n ],\n [\n -101.88661026215635,\n 53.29557322524738\n ],\n [\n -105.10333042439493,\n 55.3188488619183\n ],\n [\n -109.45509609372056,\n 56.74298922060379\n ],\n [\n -112.20142702562293,\n 60.35496296087831\n ],\n [\n -117.3247083017325,\n 60.31647251233596\n ],\n [\n -118.81440244912545,\n 59.02540113843418\n ],\n [\n -120.91916585824947,\n 57.85206145280617\n ],\n [\n -122.23992348667531,\n 57.6515156864875\n ],\n [\n -121.81553356709298,\n 55.96853063691535\n ],\n [\n -114.91051096218939,\n 55.415073221874025\n ],\n [\n -116.12311653915395,\n 53.68014339688571\n ],\n [\n -114.36068663970394,\n 51.40761693922349\n ],\n [\n -114.73749958809637,\n 49.58765797184398\n ],\n [\n -108.67606009110042,\n 49.31809944743287\n ],\n [\n -105.62003370031768,\n 47.834501224842995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Contact Information
Mineral Resources Program
U.S. Geological Survey 
12201 Sunrise Valley Drive 
913 National Center 
Reston, VA 20192 

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-09-14","noUsgsAuthors":false,"publicationDate":"2023-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Cocker, Mark D. 0000-0001-9435-5862 mcocker@usgs.gov","orcid":"https://orcid.org/0000-0001-9435-5862","contributorId":4297,"corporation":false,"usgs":true,"family":"Cocker","given":"Mark","email":"mcocker@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orris, Greta J. 0000-0002-2340-9955 greta@usgs.gov","orcid":"https://orcid.org/0000-0002-2340-9955","contributorId":3472,"corporation":false,"usgs":true,"family":"Orris","given":"Greta","email":"greta@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":883024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunlap, Pamela pdunlap@usgs.gov","contributorId":5329,"corporation":false,"usgs":true,"family":"Dunlap","given":"Pamela","email":"pdunlap@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Chao","contributorId":119386,"corporation":false,"usgs":true,"family":"Yang","given":"Chao","email":"","affiliations":[],"preferred":false,"id":883026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bliss, James D. jbliss@usgs.gov","contributorId":2790,"corporation":false,"usgs":true,"family":"Bliss","given":"James","email":"jbliss@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883027,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248727,"text":"70248727 - 2023 - PopEquus: a predictive modeling tool to support management decisions for free-roaming horse populations","interactions":[],"lastModifiedDate":"2023-09-18T14:49:18.727694","indexId":"70248727","displayToPublicDate":"2023-09-14T09:19:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"PopEquus: A predictive modeling tool to support management decisions for free-roaming horse populations","title":"PopEquus: a predictive modeling tool to support management decisions for free-roaming horse populations","docAbstract":"

Feral horse (Equus caballus) population management is a challenging problem around the world because populations often exhibit density-independent growth, can exert negative ecological effects on ecosystems, and require great cost to be managed. However, strong value-based connections between people and horses cause contention around management decisions. To help make informed decisions, natural resource managers might benefit from more detailed understanding of how horse management alternatives, including combinations of removals and fertility control methods, could achieve objectives of sustainable, multiple-use ecosystems while minimizing overall horse handling and fiscal costs. Here, we describe a modeling tool that simulates horse management alternatives and estimates trade-offs in predicted metrics related to population size, animal handling, and direct costs of management. The model considers six management actions for populations (removals for adoption or long-term holding; fertility control treatment with three vaccines, intrauterine devices, and mare sterilization), used alone or in combination. We simulated 19 alternative management scenarios at 2-, 3-, and 4-year management return intervals and identified efficiency frontiers among alternatives for trade-offs between predicted population size and six management metrics. Our analysis identified multiple alternatives that could maintain populations within target population size ranges, but some alternatives (e.g., removal and mare sterilization, removal and GonaCon treatment) performed better at minimizing overall animal handling requirements and management costs. Cost savings increased under alternatives with more effective, longer lasting fertility control techniques over longer management intervals compared with alternatives with less-effective, shorter lasting fertility control techniques. We built a user-friendly website application, PopEquus, that decision makers and interested individuals can use to simulate management alternatives and evaluate trade-offs among management and cost metrics. Our results and website application provide quantitative trade-off tools for horse population management decisions and can help support value-based management decisions for wild or feral horse populations and ecosystems at local and regional scales around the world.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4632","usgsCitation":"Folt, B.P., Schoenecker, K., Ekernas, L., Edmunds, D.R., and Hannon, M.T., 2023, PopEquus: a predictive modeling tool to support management decisions for free-roaming horse populations: Ecosphere, v. 14, no. 9, e4632, 20 p., https://doi.org/10.1002/ecs2.4632.","productDescription":"e4632, 20 p.","ipdsId":"IP-141050","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":442104,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4632","text":"Publisher Index Page"},{"id":435178,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HVUA6D","text":"USGS data release","linkHelpText":"Scenario Analysis of Management Alternatives for Free-roaming Horse Populations (Version 1.0.0)"},{"id":435177,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NMRQDG","text":"USGS data release","linkHelpText":"PopEquus: A Predictive Modeling Tool to Support Management Decisions for Free-roaming Horse Populations, Version 1.0.1"},{"id":420891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"9","noUsgsAuthors":false,"publicationDate":"2023-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Folt, Brian Patrick 0000-0003-2278-2018","orcid":"https://orcid.org/0000-0003-2278-2018","contributorId":328937,"corporation":false,"usgs":true,"family":"Folt","given":"Brian","email":"","middleInitial":"Patrick","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":883322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":883323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ekernas, L. Stefan 0000-0002-9205-1985","orcid":"https://orcid.org/0000-0002-9205-1985","contributorId":329791,"corporation":false,"usgs":false,"family":"Ekernas","given":"L. Stefan","affiliations":[{"id":78719,"text":"The Denver Zoo","active":true,"usgs":false}],"preferred":false,"id":883324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":883325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hannon, Mark T. 0000-0003-1050-749X mhannon@usgs.gov","orcid":"https://orcid.org/0000-0003-1050-749X","contributorId":329792,"corporation":false,"usgs":true,"family":"Hannon","given":"Mark","email":"mhannon@usgs.gov","middleInitial":"T.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":883326,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248801,"text":"70248801 - 2023 - Blue carbon in a changing climate and a changing context","interactions":[],"lastModifiedDate":"2023-09-21T13:42:13.711053","indexId":"70248801","displayToPublicDate":"2023-09-14T08:39:29","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Blue carbon in a changing climate and a changing context","docAbstract":"

Blue carbon, a convenient term to encompass the climate mitigation value of coastal carbon dynamics, has received global policy attention and growing datasets to support management actions. Carbon stock assessments in mangroves, seagrass, and tidal marshes document significant carbon storage in soils. Models illustrate significant downward fluxes of carbon dioxide and limited methane emissions, making tidal wetland preservation and restoration notably potent for carbon dioxide removal (CDR). Natural variation in different carbon stocks and fluxes has led to prioritization efforts to characterize coastal lands across physical and biological gradients. However, a larger concern beyond upscaling carbon dynamics is the resilience of these stocks and fluxes with global changes. Data-informed models have greatly improved our assessments of the vulnerability of soil and biomass stocks, greenhouse gas (GHG) balance, and spatial extents. Accelerated sea-level rise is increasingly concerning, but its impacts vary by resilience context, as very few coastal lands are without direct human impact. As the landscape context has changed, blue carbon fluxes have also shifted in terms of importance and distribution. New incentives for tidal ecosystem management are expanding boundaries to include algal carbon and tidal transport of alkalinity, which bring additional co-benefits to coastal waters. Using examples from the conterminous USA on blue carbon stocks, radiative balance, and extent, this chapter explores timelines of physical and biogeochemical stressors and their application to past, current, and future climate mitigation functions of coastal ecosystems.

","largerWorkTitle":"Climate change and estuaries","language":"English","publisher":"CRC Press","usgsCitation":"Windham-Myers, L., 2023, Blue carbon in a changing climate and a changing context, chap. of Climate change and estuaries, p. 203-214.","productDescription":"12 p.","startPage":"203","endPage":"214","ipdsId":"IP-144897","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":421025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421024,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/chapters/edit/10.1201/9781003126096-12/blue-carbon-changing-climate-changing-context-lisamarie-windham-myers?context=ubx&refId=09c63ae2-147c-4d3e-a44e-e123f1046fc4","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Kennish, Michael J.","contributorId":111903,"corporation":false,"usgs":true,"family":"Kennish","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":883743,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Paerl, Hans W.","contributorId":172724,"corporation":false,"usgs":false,"family":"Paerl","given":"Hans","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":883744,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Crosswell, Joseph","contributorId":217003,"corporation":false,"usgs":false,"family":"Crosswell","given":"Joseph","email":"","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":883745,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883711,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70248449,"text":"70248449 - 2023 - Multi-decadal erosion rates from glacierized watersheds on Mount Baker, Washington, USA, reveal topographic, climatic, and lithologic controls on sediment yields","interactions":[],"lastModifiedDate":"2023-09-13T19:15:08.088286","indexId":"70248449","displayToPublicDate":"2023-09-13T13:51:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Multi-decadal erosion rates from glacierized watersheds on Mount Baker, Washington, USA, reveal topographic, climatic, and lithologic controls on sediment yields","docAbstract":"

Understanding land surface change in and sediment export out of proglacial landscapes is critical for understanding geohazard and flood risks over engineering timescales and characterizing landscape evolution over geomorphic timescales. We used automated Structure from Motion software to process historical aerial photographs and, with modern lidar data, generated a high-resolution DEM time series with coverage over 10 glacierized watersheds on Mount Baker, Washington, USA for the time period between 1947 and 2015. We measured basin-wide sediment yields and sediment redistribution on hillslopes and in stream channels. Slopes within most measured erosion sites are above theoretical and observed debris-flow thresholds. We observed significant erosion of hillslopes and limited deposition on hillslopes and in stream channels. Sediment delivery ratios during time periods with net erosion averaged 0.73. We determined, consistent with previous field observations, that debris flows originating from moraines are a primary erosion mechanism in proglacial zones on Mount Baker. Time series measurements indicate that temporal variability in erosion rates is associated with climate oscillations, with higher erosion rates during cooler-wetter periods. Basin-wide sediment yield is positively correlated with lithology (r2 = 0.54), hillslope angle (r2 = 0.52), drainage area (r2 = 0.82), and negatively correlated with stream channel slope (r2 = 0.67). Topographic differences between high and low yielding basins indicate that spatial variability in erosion on Mount Baker is sensitive to Pleistocene and Holocene glacial and volcanic activity. Specific sediment yields in six basins averaged 4600 ton/km2/yr, consistent with global measurements in glacierized catchments. Specific sediment yield decreased with increasing basin area, with total loads in the downstream main stem Nooksack River estimated between 480 and 820 ton/km2/yr. Proglacial sediment yields account for between 18 and 32 % of total sediment load in the main stem Nooksack River and exceed contributions by bluff and terrace erosion, which account for between 8 and 13 % of total load. Our findings indicate that erosion in glacierized basins is sensitive to decadal climate oscillations and that high proglacial sediment yields provide an important contribution to river systems downstream, particularly in catchments where upland topography and lithology is favorable.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2023.108805","usgsCitation":"Schwat, E., Istanbulluoglu, E., Horner-Devine, A., Anderson, S.W., Knuth, F., and Shean, D., 2023, Multi-decadal erosion rates from glacierized watersheds on Mount Baker, Washington, USA, reveal topographic, climatic, and lithologic controls on sediment yields: Geomorphology, v. 438, 108805, 17 p., https://doi.org/10.1016/j.geomorph.2023.108805.","productDescription":"108805, 17 p.","ipdsId":"IP-152751","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":442107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2023.108805","text":"Publisher Index Page"},{"id":420774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Koma Kulshan, Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.85505311805078,\n 48.71738721735028\n ],\n [\n -121.8138912926704,\n 48.69120451475976\n ],\n [\n -121.72859208826776,\n 48.669266019808475\n ],\n [\n -121.67503212271245,\n 48.72229495768988\n ],\n [\n -121.6804873043895,\n 48.74224816877816\n ],\n [\n -121.66610546178667,\n 48.77461439590772\n ],\n [\n -121.7603313271151,\n 48.82361440118996\n ],\n [\n -121.86298792776248,\n 48.840916292756305\n ],\n [\n -121.92101122378057,\n 48.82034922330564\n ],\n [\n -121.94729528095108,\n 48.77134602689094\n ],\n [\n -121.91357233967562,\n 48.74061295759708\n ],\n [\n -121.85505311805078,\n 48.71738721735028\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"438","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwat, Eli","contributorId":299744,"corporation":false,"usgs":false,"family":"Schwat","given":"Eli","email":"","affiliations":[],"preferred":false,"id":882951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Istanbulluoglu, Erkan 0000-0001-9453-4676","orcid":"https://orcid.org/0000-0001-9453-4676","contributorId":295348,"corporation":false,"usgs":false,"family":"Istanbulluoglu","given":"Erkan","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":882952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horner-Devine, Alex 0000-0003-2323-7150","orcid":"https://orcid.org/0000-0003-2323-7150","contributorId":295351,"corporation":false,"usgs":false,"family":"Horner-Devine","given":"Alex","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":882953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knuth, Friedrich","contributorId":299741,"corporation":false,"usgs":false,"family":"Knuth","given":"Friedrich","email":"","affiliations":[],"preferred":false,"id":882955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shean, David","contributorId":299742,"corporation":false,"usgs":false,"family":"Shean","given":"David","affiliations":[],"preferred":false,"id":882956,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248439,"text":"70248439 - 2023 - Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework","interactions":[],"lastModifiedDate":"2023-09-13T18:44:22.909442","indexId":"70248439","displayToPublicDate":"2023-09-13T13:33:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework","title":"Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework","docAbstract":"

Introduction: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined.

Methods: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region.

Results: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km.

Discussion: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.

","language":"English","publisher":"Frontiers Media S.A.","doi":"10.3389/fmicb.2023.1223876","usgsCitation":"Wiesner-Friedman, C., Beattie, R.E., Stewart, J.R., Hristova, K.R., and Serre, M.L., 2023, Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework: Frontiers in Microbiology, v. 14, 1223876, 14 p., https://doi.org/10.3389/fmicb.2023.1223876.","productDescription":"1223876, 14 p.","ipdsId":"IP-149826","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":442109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2023.1223876","text":"Publisher Index Page"},{"id":420771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","otherGeospatial":"Ahnapee River, East Twin River, Kewaunee River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":3073,\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2023-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiesner-Friedman, Corinne","contributorId":329682,"corporation":false,"usgs":false,"family":"Wiesner-Friedman","given":"Corinne","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":882931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beattie, Rachelle Elaine 0000-0002-9648-4948","orcid":"https://orcid.org/0000-0002-9648-4948","contributorId":298312,"corporation":false,"usgs":true,"family":"Beattie","given":"Rachelle","email":"","middleInitial":"Elaine","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":882932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Jill R.","contributorId":329683,"corporation":false,"usgs":false,"family":"Stewart","given":"Jill","email":"","middleInitial":"R.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hristova, Krassimira R.","contributorId":298313,"corporation":false,"usgs":false,"family":"Hristova","given":"Krassimira","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":882934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Serre, Marc L.","contributorId":329684,"corporation":false,"usgs":false,"family":"Serre","given":"Marc","email":"","middleInitial":"L.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882935,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254741,"text":"70254741 - 2023 - Data-limited fishery assessment methods shed light on the exploitation history and population dynamics of Endangered Species Act-listed Yelloweye Rockfish in Puget Sound, Washington","interactions":[],"lastModifiedDate":"2024-06-07T16:51:40.506334","indexId":"70254741","displayToPublicDate":"2023-09-13T11:43:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Data-limited fishery assessment methods shed light on the exploitation history and population dynamics of Endangered Species Act-listed Yelloweye Rockfish in Puget Sound, Washington","docAbstract":"

Objective

The distinct population segment (DPS) of Yelloweye Rockfish Sebastes ruberrimus inhabiting the Puget Sound/Georgia Basin was listed under the Endangered Species Act (ESA) in 2010, and a formal recovery plan for the DPS was published by National Oceanic and Atmospheric Administration Fisheries in 2017. In this recovery plan, the biological criteria for delisting or downlisting were specified as certain levels of spawning potential ratio (SPR), a commonly used metric of equilibrium stock status for commercially exploited fishes. Although this metric can be estimated from length compositions, the combination of length data with a catch history (which was not previously available for this DPS) improves our understanding of population dynamics over time and allows us to estimate a different measure of stock status, relative (to unfished) spawning stock biomass (SSB), rather than only SPR.

Methods

To estimate relative SSB and reconstruct the historical dynamics of this DPS, we reconstructed the catch history from fisheries records, collated length data from historical and contemporary hook-and-line surveys, and fitted a data-limited version of a statistical catch-at-age model.

Result

Despite a high level of uncertainty, we estimated that Yelloweye Rockfish in Puget Sound are above 25% of unfished biomass (a reference point detailed in the recovery criteria) under the assumption of deterministic recruitment, presenting the first direct estimates of Yelloweye Rockfish population status in Puget Sound.

Conclusion

However, as informed by recent genetic studies, the DPS boundaries of ESA-listed Yelloweye Rockfish extend from South Puget Sound to Queen Charlotte Strait in British Columbia. The Canadian portion of this population is managed separately and is currently estimated to be at 32% of unfished biomass (95% quantiles = 15%–68%). Thus, the disjunction between the biological boundaries of the population and the jurisdictional boundaries between Canada and the United States presents an additional source of uncertainty in assessing recovery that must be addressed to achieve DPS-wide recovery goals.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10251","usgsCitation":"Min, M., Cope, J., Lowry, D., Selleck, J., Tonnes, D., Andrews, K., Pacunski, R., Hennings, A., and Scheuerell, M.D., 2023, Data-limited fishery assessment methods shed light on the exploitation history and population dynamics of Endangered Species Act-listed Yelloweye Rockfish in Puget Sound, Washington: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 5, e1051, 16 p., https://doi.org/10.1002/mcf2.10251.","productDescription":"e1051, 16 p.","ipdsId":"IP-146495","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":442111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10251","text":"Publisher Index Page"},{"id":429657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.09326531847128,\n 49.00502906664644\n ],\n [\n -124.94030563468002,\n 49.00502906664644\n ],\n [\n -124.94030563468002,\n 46.793491720907355\n ],\n [\n -122.09326531847128,\n 46.793491720907355\n ],\n [\n -122.09326531847128,\n 49.00502906664644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Min, Markus","contributorId":337385,"corporation":false,"usgs":false,"family":"Min","given":"Markus","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":902398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cope, Jason","contributorId":337386,"corporation":false,"usgs":false,"family":"Cope","given":"Jason","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowry, Dayv","contributorId":337387,"corporation":false,"usgs":false,"family":"Lowry","given":"Dayv","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Selleck, James","contributorId":337388,"corporation":false,"usgs":false,"family":"Selleck","given":"James","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tonnes, Daniel","contributorId":337390,"corporation":false,"usgs":false,"family":"Tonnes","given":"Daniel","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrews, Kelly","contributorId":337392,"corporation":false,"usgs":false,"family":"Andrews","given":"Kelly","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902403,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pacunski, Robert","contributorId":337393,"corporation":false,"usgs":false,"family":"Pacunski","given":"Robert","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902404,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hennings, Andrea","contributorId":337396,"corporation":false,"usgs":false,"family":"Hennings","given":"Andrea","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":902405,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902406,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256516,"text":"70256516 - 2023 - Multi-resolution habitat models of the Puerto Rican Nightjar Antrostromus noctitherus","interactions":[],"lastModifiedDate":"2024-08-20T16:40:42.78689","indexId":"70256516","displayToPublicDate":"2023-09-13T11:35:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1048,"text":"Bird Conservation International","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi-resolution habitat models of the Puerto Rican Nightjar Antrostromus noctitherus","title":"Multi-resolution habitat models of the Puerto Rican Nightjar Antrostromus noctitherus","docAbstract":"

The Puerto Rican Nightjar Antrostomus noctitherus is an endemic Caprimulgid found in dry coastal and lower montane forests of south-western Puerto Rico. Information on the species (e.g. abundance, nesting biology) has been mostly restricted to forest reserves (i.e. Guánica Forest and Susúa Forest) with limited information available from private lands. We collected stand-level vegetation structure and geographical information from forest reserves and private lands to model habitat suitability and distribution for the Nightjar. Results of the stand-level model indicated forest type and midstorey vegetation density best predicted Nightjar habitat. Our spatial model predicted considerably more Nightjar habitat (17,819.64 ha) located outside protected areas than previously reported. Further, the model highlighted several localities of importance for the species across southern Puerto Rico, all located within private lands. We used a patch occupancy approach to assess regions identified by the landscape-level model as suitable for the Nightjar and documented the presence of the species in 32 of 55 sites, located in 12 of 18 municipalities across southern Puerto Rico. The protection and restoration of forest across the southern coast of Puerto Rico would help to ensure the long-term persistence of the Nightjar across a considerable portion of its range. Addressing habitat needs may be the single most effective mechanism to achieve recovery of the species.

","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0959270923000278","usgsCitation":"Vilella, F., and Gonzalez, R., 2023, Multi-resolution habitat models of the Puerto Rican Nightjar Antrostromus noctitherus: Bird Conservation International, v. 33, e74, 10 p., https://doi.org/10.1017/S0959270923000278.","productDescription":"e74, 10 p.","ipdsId":"IP-152543","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":442113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1017/s0959270923000278","text":"Publisher Index Page"},{"id":432949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.30291952675373,\n 18.570803919744066\n ],\n [\n -67.30291952675373,\n 17.90303830780671\n ],\n [\n -65.57254725538235,\n 17.90303830780671\n ],\n [\n -65.57254725538235,\n 18.570803919744066\n ],\n [\n -67.30291952675373,\n 18.570803919744066\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2023-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907774,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez, Rafael","contributorId":340993,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Rafael","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907775,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248277,"text":"sir20235087 - 2023 - Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM)","interactions":[],"lastModifiedDate":"2026-03-12T21:08:34.632448","indexId":"sir20235087","displayToPublicDate":"2023-09-12T19:46:00","publicationYear":"2023","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":"2023-5087","displayTitle":"Approaches for Assessing Flows, Concentrations, and Loads of Highway and Urban Runoff and Receiving-Stream Stormwater in Southern New England With the Stochastic Empirical Loading and Dilution Model (SELDM)","title":"Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM)","docAbstract":"

The Stochastic Empirical Loading and Dilution Model (SELDM) was designed to help quantify the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. SELDM is calibrated using representative hydrological and water-quality input statistics. This report by the U.S. Geological Survey, in cooperation with the Federal Highway Administration and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation, documents approaches for assessing flows, concentrations, and loads of highway- and urban-runoff and receiving-stream stormwater in southern New England with SELDM. In this report, the term “urban runoff” is used to identify stormwater flows from developed areas with impervious fractions ranging from 10 to 100 percent without regard to the U.S. Census Bureau designation for any given location. There are more than 48,000 delineated road-stream crossings in southern New England, but because there are relatively few precipitation, streamflow, and water-quality monitoring sites in this area, methods were needed to simulate conditions at unmonitored sites. This report documents simulation methods, methods for interpreting stochastic model results, sensitivity analyses to identify the most critical variables of concern, and examples demonstrating how simulation results can be used to inform scientific decision-making processes. Results of 7,511 SELDM simulations were used to do the sensitivity analyses and provide information decisionmakers can use to address runoff-quality issues in southern New England and other areas of the Nation.

The sensitivity analyses indicate the relatively strong effect of input variables on variations in output results. These analyses indicate that highway and urban runoff quality and upstream water-quality statistics that vary considerably from site to site have the greatest effect on simulated results. Further data are needed to improve available water-quality statistics, and because the number of monitored sites will never approach the number of sites of interest for water-quality management, research is needed to identify methods to select statistics for unmonitored sites and quantify the uncertainties in the selection process. Hydrologically, prestorm streamflows with and without zero flows are the most sensitive and therefore the most important hydrologic variables to quantify. Results of analyses also are sensitive to statistics used for simulating structural best management practices.

Although the focus of the report is on data, statistics, simulation methods, and methods to interpret stochastic simulations, the examples in this report provide results that can be used to inform scientific decision-making processes. The results of 441 simulations that provide regional and site-specific highway and urban runoff yields across southern New England can be used for total maximum daily load analyses. The example stormwater load analysis done for 16 tributaries of the Narragansett Bay demonstrates that highway nitrogen loads are a small fraction of stormwater loads (about 3.6 percent), and a much smaller fraction of all nitrogen loads to the bay, primarily because highways have a small footprint on the land. Examples evaluating the potential effectiveness of end-of-pipe treatment indicate that offsite treatment is warranted in developed areas, and land conservation may be an effective mitigation strategy. The results of these analyses are consistent with conclusions from other simulation and monitoring studies.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235087","collaboration":"Prepared in cooperation with the Federal Highway Administration and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation","usgsCitation":"Granato, G.E., Spaetzel, A.B., and Jeznach, L.C., 2023, Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5087, 152 p., https://doi.org/10.3133/sir20235087.","productDescription":"Report: xii, 152 p.; Software Release; 4 Data Releases","numberOfPages":"152","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133112","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501050,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115402.htm","linkFileType":{"id":5,"text":"html"}},{"id":420555,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K0Y7XR","text":"USGS data release","linkHelpText":"Model archive for analysis of the effects of impervious cover on receiving-water quality with the Stochastic Empirical Loading Dilution Model (SELDM)"},{"id":420556,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZNIH5","text":"USGS data release","linkHelpText":"Model archive for analysis of flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":420554,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B02EUZ","text":"USGS data release","linkHelpText":"Model archive for analysis of long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading Dilution Model (SELDM)"},{"id":420557,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VK1MCG","text":"USGS data release","linkHelpText":"Basin characteristics and point locations of road crossings in Connecticut, Massachusetts, and Rhode Island for highway-runoff mitigation analyses using the Stochastic Empirical Loading and Dilution Model"},{"id":420548,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5087/coverthb.jpg"},{"id":420549,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5087/sir20235087.pdf","text":"Report","size":"8.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5087"},{"id":420552,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5087/images/"},{"id":420887,"rank":11,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9PYG7T5","text":"USGS software release","linkHelpText":"- Stochastic Empirical Loading and Dilution Model (SELDM) software archive"},{"id":420551,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5087/sir20235087.XML"},{"id":420553,"rank":10,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9XBPIOB","text":"USGS software release","linkHelpText":"- Best management practices statistical estimator (BMPSE) version 1.2.0"},{"id":420550,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235087/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5087"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.28249194123347,\n 42.97558594616143\n ],\n [\n -73.81823969745555,\n 41.00628384122689\n ],\n [\n -69.72117184187273,\n 40.9949482400483\n ],\n [\n -69.72117184187273,\n 42.97558594616143\n ],\n [\n -73.28249194123347,\n 42.97558594616143\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2023-09-12","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":203250,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spaetzel, Alana B. 0000-0002-9871-812X","orcid":"https://orcid.org/0000-0002-9871-812X","contributorId":240935,"corporation":false,"usgs":true,"family":"Spaetzel","given":"Alana","email":"","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeznach, Lillian C. 0000-0002-5476-9232","orcid":"https://orcid.org/0000-0002-5476-9232","contributorId":297153,"corporation":false,"usgs":true,"family":"Jeznach","given":"Lillian","email":"","middleInitial":"C.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882227,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248425,"text":"tm8D2 - 2023 - Design and utility of automatous, floating bait delivery platform for applying fish management baits","interactions":[],"lastModifiedDate":"2023-09-13T13:48:28.142468","indexId":"tm8D2","displayToPublicDate":"2023-09-12T15:12:56","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"8-D2","displayTitle":"Design and Utility of Automatous, Floating Bait Delivery Platform for Applying Fish Management Baits","title":"Design and utility of automatous, floating bait delivery platform for applying fish management baits","docAbstract":"

Using manufactured baits to attract fish to passive gear is common practice in fisheries management. The most common method is using hoop nets baited with soybean cakes or waste cheese to increase captures of multiple catfish species; however, these techniques are limited to how often bait is added, the type of bait, gear compatibility, and oversaturation of bait during soak time. The U.S. Geological Survey developed a technique to deliver various types of manufactured, pelleted baits over multiple scenarios and traditional passive gears. A floating platform designed with a dispenser can be constructed easily and allows for the automatic application of varying quantities and sizes of bait. Bait platforms can be modified for use in lakes and rivers where water fluctuations are common. Unlike traditional baiting techniques, these platforms can be positioned over or near any type of gear and release bait as many as nine times daily. Programmed release of bait multiple time a day can be useful to target fish activity during specific hours and can allow for sustained application without bait oversaturation or deterioration from long soak times. This report describes the design of a bait delivery platform developed for deployment in the Sandusky River in Ohio for the removal of Ctenopharyngodon idella (Valenciennes, 1844; grass carp) during 2021 and 2022.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm8D2","usgsCitation":"Wamboldt, J.J., 2023, Design and utility of automatous, floating bait delivery platform for applying fish management baits: U.S. Geological Survey Techniques and Methods, book 8, chap. D2, 8 p., https://doi.org/10.3133/tm8D2.","productDescription":"vi, 8 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153329","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":420736,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm8D2/full","linkFileType":{"id":5,"text":"html"}},{"id":420715,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/08/d02/images/"},{"id":420714,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/08/d02/tm8d2.XML","linkFileType":{"id":8,"text":"xml"}},{"id":420713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/08/d02/tm8d2.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 8–D2"},{"id":420712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/08/d02/coverthb.jpg"}],"contact":"

Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-09-12","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wamboldt, James J. 0000-0003-3043-5198","orcid":"https://orcid.org/0000-0003-3043-5198","contributorId":219060,"corporation":false,"usgs":true,"family":"Wamboldt","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":882873,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250212,"text":"70250212 - 2023 - Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska","interactions":[],"lastModifiedDate":"2023-11-28T17:24:31.851807","indexId":"70250212","displayToPublicDate":"2023-09-12T11:18:25","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska","docAbstract":"The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different hydrogeomorphic conditions. The large volume of surface water features and HR remote sensing data make manual annotation of the water features over the entire nation infeasible. Furthermore, annual and seasonal variations of surface waters warrant some level of periodic updates to hydrography. Advances in deep learning technologies provide an opportunity to automate hydrography extraction and scale up the process to a nationwide level. One major challenge, however, is the effect of spatial heterogeneity due to the wide variety of hydrogeomorphic conditions in the United States. In other words, it is unclear how a deep learning model pre-trained in one set of hydrogeomorphic conditions can be effectively applied to other conditions for hydrographic feature extraction. This paper aims to provide some clarity in this regard by testing automated deep learning and its transferability to the extraction of hydrography from digital elevation model (DEM) data spanning a range of hydrogeomorphic conditions in Alaska. In transfer learning, the knowledge (e.g., neural network weights) from one domain is transferred to other domains and thereby decrease training requirements in the target domain.","conferenceTitle":"GIScience 2023 Workshop on CartoAI: AI for cartography","conferenceDate":"September 12-15, 2023","conferenceLocation":"Leeds, United Kingdom","language":"English","publisher":"ICA Commission on Multiscale Cartography","usgsCitation":"Stanislawski, L.V., Jaroenchai, N., Wang, S., Shavers, E.J., Duffy, A., Thiem, P.T., Jiang, Z., and Camerer, A., 2023, Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska, GIScience 2023 Workshop on CartoAI: AI for cartography, Leeds, United Kingdom, September 12-15, 2023, 3 p.","productDescription":"3 p.","ipdsId":"IP-156657","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":423015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":423004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cartoai.github.io/giscience2023/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.78252936612796,\n 71.06703147535433\n ],\n [\n -160.1834860755645,\n 68.98584856236955\n ],\n [\n -158.7680908656647,\n 63.65325869193444\n ],\n [\n -142.69648224587866,\n 62.32431074447908\n ],\n [\n -142.64727236190666,\n 70.42721281931796\n ],\n [\n -154.78252936612796,\n 71.06703147535433\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaroenchai, Nattapon","contributorId":267318,"corporation":false,"usgs":false,"family":"Jaroenchai","given":"Nattapon","email":"","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":888922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":888923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duffy, Alexander 0000-0001-6036-0583","orcid":"https://orcid.org/0000-0001-6036-0583","contributorId":299070,"corporation":false,"usgs":false,"family":"Duffy","given":"Alexander","email":"","affiliations":[{"id":64752,"text":"University of Missouri Science & Technology","active":true,"usgs":false}],"preferred":false,"id":888925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":888926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":888927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Camerer, Adam","contributorId":331850,"corporation":false,"usgs":false,"family":"Camerer","given":"Adam","email":"","affiliations":[{"id":26996,"text":"Missouri University of Science & Technology","active":true,"usgs":false}],"preferred":false,"id":888928,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248283,"text":"cir1510 - 2023 - Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project progress toward an information management and technology plan","interactions":[],"lastModifiedDate":"2023-09-12T18:55:36.46567","indexId":"cir1510","displayToPublicDate":"2023-09-12T10:25:00","publicationYear":"2023","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":"1510","displayTitle":"Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) Pilot Project Progress Toward an Information Management and Technology Plan","title":"Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project progress toward an information management and technology plan","docAbstract":"

Executive Summary

The U.S. Geological Survey carries out a wide variety of multidisciplinary science projects through the Bureau’s regions, mission areas, programs, and science centers. However, this structure can limit interactions among individual scientists, segregate data holdings, and make it difficult to apply holistic, interdisciplinary science. In addition, technological advances in sensors, data storage and analysis, computing power, and networking have resulted in an exponential growth in the volume, variety, and complexity of data. To address some of these challenges, the U.S. Geological Survey initiated the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project to facilitate interdisciplinary science in the drought-stricken basin and apply information management and technology (IMT) resources that can be used to deliver actionable science efficiently and effectively.

In fiscal year 2021, the Data Management and Advanced Technology subgroup of the ASIST pilot project worked toward developing an IMT plan that includes several advanced IMT solutions that are being implemented Bureau-wide by the Office of the Associate Chief Information Officer. This plan identifies applications, opportunities, and steps to leverage new and existing technologies, data, models, and knowledge to support integrated science projects across the Colorado River Basin. The subgroup also created an inventory of available IMT resources and their locations. The Colorado River Basin ASIST pilot project also developed a multiyear approach to build capacity for supporting integrated science projects in the Colorado River Basin, which provides an advanced IMT framework for expediting the production of interdisciplinary science related to the basin.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1510","usgsCitation":"Anderson, E.D., Erxleben, J.R., Qi, S.L., Monroe, A.P., and Dahm, K.G., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project progress toward an information management and technology plan: U.S. Geological Survey Circular 1510, 11 p., https://doi.org/10.3133/cir1510.","productDescription":"viii, 10 p.","onlineOnly":"Y","ipdsId":"IP-131022","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":420721,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1510/images"},{"id":420626,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221040","text":"USGS Open-File Report 2022-1040","linkHelpText":"Presented Abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020)"},{"id":420625,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223051","text":"USGS Fact Sheet 2022-3051","linkHelpText":"U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan"},{"id":420590,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223016","text":"USGS Fact Sheet 2022-3016","linkHelpText":"Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)"},{"id":420589,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223010","text":"USGS Fact Sheet 2022-3010","linkHelpText":"Addressing Stakeholder Science Needs for Integrated Drought Science in the Colorado River Basin"},{"id":420624,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/cir1502","text":"USGS Circular 1502","linkHelpText":"Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science Strategy"},{"id":420627,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/cir1483","text":"USGS Circular 1483","linkHelpText":"Rocky Mountain Region Science Exchange 2020—EarthMAP and the Colorado River Basin"},{"id":420588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1510/cir1510.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1510"},{"id":420587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1510/coverthb.jpg"},{"id":420735,"rank":11,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1510/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1510"},{"id":420722,"rank":10,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1510/cir1510.xml"}],"country":"United States","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.50163992210821,\n 41.074060046257415\n ],\n [\n -117.48373547769216,\n 41.074060046257415\n ],\n [\n -117.48373547769216,\n 30.63237394457815\n ],\n [\n -107.50163992210821,\n 30.63237394457815\n ],\n [\n -107.50163992210821,\n 41.074060046257415\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225

","tableOfContents":"","publishedDate":"2023-09-12","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":202072,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric D.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":882263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erxleben, Jennifer R. 0000-0002-4060-0241","orcid":"https://orcid.org/0000-0002-4060-0241","contributorId":299423,"corporation":false,"usgs":true,"family":"Erxleben","given":"Jennifer","email":"","middleInitial":"R.","affiliations":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"preferred":true,"id":882264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahm, Katharine G. 0000-0002-4024-8110","orcid":"https://orcid.org/0000-0002-4024-8110","contributorId":299422,"corporation":false,"usgs":true,"family":"Dahm","given":"Katharine","email":"","middleInitial":"G.","affiliations":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"preferred":true,"id":882267,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248453,"text":"70248453 - 2023 - Comment on “A new decade in seismoacoustics (2010–2022)” by Fransiska Dannemann Dugick, Clinton Koch, Elizabeth Berg, Stephen Arrowsmith, and Sarah Albert","interactions":[],"lastModifiedDate":"2023-12-04T17:15:29.773582","indexId":"70248453","displayToPublicDate":"2023-09-12T08:40:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Comment on “A new decade in seismoacoustics (2010–2022)” by Fransiska Dannemann Dugick, Clinton Koch, Elizabeth Berg, Stephen Arrowsmith, and Sarah Albert","docAbstract":"

An increase in seismic stations also having microbarographs has led to increased interest in the field of seismoacoustics. A review of the recent advances in this field can be found in Dannemann Dugick et al. (2023). The goal of this note is to draw the attention of the readers of Dannemann Dugick et al. (2023) to several additional interactions between the solid Earth and atmosphere that have not been classically considered in the field of seismoacoustics. The 15 January 2022 Hunga Tonga–Hunga Ha‘api eruption produced acoustic gravity waves that were recorded globally. For example, the Lamb wave from this eruption produced early‐arriving and long‐lasting tsunami waves. This eruption also provided globally recorded coupling of atmospheric modes with solid Earth modes, providing another example of the complex interactions that can occur at the boundary between the atmosphere and the solid Earth. Even in the absence of large atmospheric signals, collocated pressure sensors at seismic stations can be a useful tool for estimating the local substructure, such at VS30⁠, the average shear velocity of the upper 30 m. Finally, at low frequencies, it is possible to use pressure records to correct out atmospheric disturbances recorded on seismometers. We briefly review the aforementioned, nontraditional seismoacoustic topics that we feel are important to consider as part of the full suite of interactions occurring between the solid Earth and atmosphere.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230111","usgsCitation":"Ringler, A.T., Anthony, R.E., Shiro, B., Tanimoto, T., and Wilson, D.C., 2023, Comment on “A new decade in seismoacoustics (2010–2022)” by Fransiska Dannemann Dugick, Clinton Koch, Elizabeth Berg, Stephen Arrowsmith, and Sarah Albert: Bulletin of the Seismological Society of America, v. 113, no. 6, p. 2746-2752, https://doi.org/10.1785/0120230111.","startPage":"2746","endPage":"2752","ipdsId":"IP-154575","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":420788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":882970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":882971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shiro, Brian 0000-0001-8756-288X","orcid":"https://orcid.org/0000-0001-8756-288X","contributorId":204040,"corporation":false,"usgs":true,"family":"Shiro","given":"Brian","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":882972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tanimoto, Toshiro","contributorId":303974,"corporation":false,"usgs":false,"family":"Tanimoto","given":"Toshiro","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":882973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":882974,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248757,"text":"70248757 - 2023 - Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay","interactions":[],"lastModifiedDate":"2023-09-20T15:08:03.924703","indexId":"70248757","displayToPublicDate":"2023-09-12T07:03:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay","title":"Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay","docAbstract":"

Objective

This paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area-based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature-derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward.

Methods

A CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery-independent indices of abundance.

Result

The CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region.

Conclusion

The CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10250","usgsCitation":"Anstead, K.A., Sweka, J., Barry, L., Hallerman, E., Smith, D.R., Ameral, N., Schmidtke, M., and Wong, R.A., 2023, Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 5, e10250, 16 p., https://doi.org/10.1002/mcf2.10250.","productDescription":"e10250, 16 p.","ipdsId":"IP-154235","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10250","text":"Publisher Index Page"},{"id":420973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.0255903899312,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 40.963933240903344\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Anstead, Kristen A.","contributorId":329847,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweka, John A.","contributorId":288581,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":883460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barry, Linda","contributorId":329848,"corporation":false,"usgs":false,"family":"Barry","given":"Linda","email":"","affiliations":[],"preferred":false,"id":883461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hallerman, Eric M.","contributorId":279474,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":883462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, David R. 0000-0001-9560-5210 dvsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-9560-5210","contributorId":329849,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dvsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":883463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ameral, Natalie","contributorId":329850,"corporation":false,"usgs":false,"family":"Ameral","given":"Natalie","email":"","affiliations":[],"preferred":false,"id":883464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidtke, Michael","contributorId":329851,"corporation":false,"usgs":false,"family":"Schmidtke","given":"Michael","email":"","affiliations":[],"preferred":false,"id":883465,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wong, Richard A.","contributorId":329852,"corporation":false,"usgs":false,"family":"Wong","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883466,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248365,"text":"ofr20231065 - 2023 - Status, trend, and monitoring effectiveness of Marbled Murrelet (Brachyramphus marmoratus) at sea abundance and reproductive output off central California, 1999–2021","interactions":[],"lastModifiedDate":"2023-09-12T13:52:06.86719","indexId":"ofr20231065","displayToPublicDate":"2023-09-11T15:19:44","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1065","displayTitle":"Status, Trend, and Monitoring Effectiveness of Marbled Murrelet (Brachyramphus marmoratus) at Sea Abundance and Reproductive Output off Central California, 1999–2021","title":"Status, trend, and monitoring effectiveness of Marbled Murrelet (Brachyramphus marmoratus) at sea abundance and reproductive output off central California, 1999–2021","docAbstract":"

Marbled Murrelets (Brachyramphus marmoratus) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding murrelet abundance, distribution, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, the meeting of Federal- and State-mandated recovery efforts. From 1999 to present, line-transect surveys have been performed to estimate at-sea abundance and reproductive output of Marbled Murrelets in the marine environment in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using this long-term annual time series, we developed a new and comprehensive analytical framework to estimate annual murrelet abundance and trend at sea, evaluated the effectiveness of spatial and temporal components of the monitoring study design, assessed two measures of annual murrelet reproductive output, and developed new spatial models to map murrelet at-sea density and estimate model-based annual at-sea abundances. The long-term average, design-based after-hatch-year (AHY) abundance estimate for the study area was 376 murrelets (range: 163–586 annually), and we did not detect any significant trend during the 23 years of monitoring. Spatial-model-based AHY abundance estimates were similar to design-based estimates but with smaller estimated variance. The AHY murrelets were most abundant nearshore, with little annual variation; alongshore, distribution was more annually variable, and some long-term hotspots occurred, particularly around Point Año Nuevo. The AHY murrelet densities were greatest in July and least in June and August. The long-term average hatch-year (HY) abundance estimate was 13 murrelets (range: 0–31 annually), and the long-term average HY:AHY ratio was 0.052; both metrics indicated similar interannual patterns. Evidence of a significant trend in either metric of reproductive output was not detected; although large overlap among interannual abundance and ratio estimates at the 95-percent confidence interval level made it difficult to evaluate interannual differences. Despite the apparent long-term stability in murrelet abundance in this region from 1999 to 2021, future long-term annual monitoring at sea will be critical to determine if the large-scale August 2020 CZU Santa Cruz Mountain wildfire that occurred adjacent to our study area affects local murrelet at-sea abundance and distribution. We also evaluated potential changes to survey and analytical design that could benefit this monitoring program in the future. Results indicated that eliminating the offshore stratum, focusing more effort on the nearshore stratum, and doing fewer surveys focused on a narrower timeframe could maintain or improve AHY trend estimates while preserving the ability to compare them to past years.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231065","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Felis, J., Adams, J., and Becker, B., 2023, Status, trend, and monitoring effectiveness of Marbled Murrelet (Brachyramphus marmoratus) at sea abundance and reproductive output off central California, 1999–2021: U.S. Geological Survey Open-File Report 2023–1065, 47 p., https://doi.org/10.3133/ofr20231065.","productDescription":"Report: viii, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-147273","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":420681,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","text":"USGS Data Release","description":"Felis, J.J., Adams, J., Peery, M.Z., Henry, R.W., Henkel, L.A., Becker, B.H., and Halbert, P., 2022, Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999–2021 (ver. 4.0, May 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F75B01RW.","linkHelpText":"Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999–2021 (ver. 4.0, May 2022)"},{"id":420675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1065/covrthb.jpg"},{"id":420676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1065/ofr20231065.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420677,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1065/ofr20231065.xml"},{"id":420678,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1065/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.02806862776823,\n 36.92229477756594\n ],\n [\n -121.92468779073133,\n 36.98296517484485\n ],\n [\n -122.46905251075435,\n 37.54475259884556\n ],\n [\n -122.79696360323157,\n 37.35110782152849\n ],\n [\n -122.23483030184242,\n 36.782698937994695\n ],\n [\n -122.02806862776823,\n 36.92229477756594\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2023-09-11","noUsgsAuthors":false,"publicationDate":"2023-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Felis, Jonathan 0000-0003-3056-925X jfelis@usgs.gov","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":174518,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":882705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Josh 0000-0003-3056-925X josh_adams@usgs.gov","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":2422,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","email":"josh_adams@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":882706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":882707,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256430,"text":"70256430 - 2023 - Prioritization of species status assessments for decision support","interactions":[],"lastModifiedDate":"2024-08-01T16:21:36.448455","indexId":"70256430","displayToPublicDate":"2023-09-11T11:17:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14243,"text":"Decision Analysis","active":true,"publicationSubtype":{"id":10}},"title":"Prioritization of species status assessments for decision support","docAbstract":"

Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions.

","language":"English","publisher":"Informs","doi":"10.1287/deca.2023.0026","usgsCitation":"Goode, A.B., Rivenbark, E., Gilbert, J.A., and McGowan, C., 2023, Prioritization of species status assessments for decision support: Decision Analysis, v. 20, no. 4, p. 311-325, https://doi.org/10.1287/deca.2023.0026.","productDescription":"15 p.","startPage":"311","endPage":"325","ipdsId":"IP-151407","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goode, Ashley B.C.","contributorId":332463,"corporation":false,"usgs":false,"family":"Goode","given":"Ashley","middleInitial":"B.C.","affiliations":[{"id":33268,"text":"USDA-ARS Aquatic Weed Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":907349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivenbark, Erin","contributorId":340546,"corporation":false,"usgs":false,"family":"Rivenbark","given":"Erin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilbert, Jessica A.","contributorId":340547,"corporation":false,"usgs":false,"family":"Gilbert","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":907352,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248680,"text":"70248680 - 2023 - A synergistic future for AI and ecology","interactions":[],"lastModifiedDate":"2023-09-18T14:17:54.235709","indexId":"70248680","displayToPublicDate":"2023-09-11T09:13:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A synergistic future for AI and ecology","docAbstract":"

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence—they are critical for both persisting and thriving in an uncertain future.

","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2220283120","usgsCitation":"Han, B.A., Varshney, K.R., LaDeau, S.L., Subramaniam, A., Weathers, K.C., and Zwart, J.A., 2023, A synergistic future for AI and ecology: Proceedings of the National Academy of Sciences, v. 120, no. 38, 2220283120, 7 p., https://doi.org/10.1073/pnas.2220283120.","productDescription":"2220283120, 7 p.","ipdsId":"IP-151656","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":442131,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2220283120","text":"Publisher Index Page"},{"id":420890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"38","noUsgsAuthors":false,"publicationDate":"2023-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Han, Barbara A. 0000-0002-9948-3078","orcid":"https://orcid.org/0000-0002-9948-3078","contributorId":329744,"corporation":false,"usgs":false,"family":"Han","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":883187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varshney, Kush R.","contributorId":329746,"corporation":false,"usgs":false,"family":"Varshney","given":"Kush","email":"","middleInitial":"R.","affiliations":[{"id":78709,"text":"IBM Research - T. J. Watson Research Center","active":true,"usgs":false}],"preferred":false,"id":883188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":883189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Subramaniam, Ajit 0000-0003-1316-5827","orcid":"https://orcid.org/0000-0003-1316-5827","contributorId":329748,"corporation":false,"usgs":false,"family":"Subramaniam","given":"Ajit","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":883190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weathers, Kathleen C.","contributorId":202417,"corporation":false,"usgs":false,"family":"Weathers","given":"Kathleen","email":"","middleInitial":"C.","affiliations":[{"id":36424,"text":"Cary Institute of Ecosystems Studies","active":true,"usgs":false}],"preferred":false,"id":883191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883192,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262059,"text":"70262059 - 2023 - Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal","interactions":[],"lastModifiedDate":"2025-01-10T18:31:23.287244","indexId":"70262059","displayToPublicDate":"2023-09-10T11:42:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5936,"text":"People and Nature","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal","title":"Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal","docAbstract":"
  1. The negative impact of large carnivore presence in human-dominated landscapes manifests as livestock depredation and in extreme cases as attacks on humans. In the case of conflict with leopards in Nepal, attacks resulting in human fatality have become more frequent over time, thus creating an urgent socio-ecological and management issue.
  2. We estimated the occurrence of leopard attacks in Nepal from human-leopard conflict cases reported in the media. We used occupancy models to analyse data collected from online news reports on incidents of leopard attacks on humans to explore drivers of leopard attacks on a landscape scale. Our results suggest that the probability of occurrence of leopard attack is associated with human population density, terrain ruggedness and livestock density.
  3. The human population density effect may be indicative of a density-dependent relationship, where attacks are more likely in areas where an increased abundance of humans increases encounter rates with leopards. The positive effect of livestock density suggests that livestock may be drawing leopards into human settlements, and consequently increasing the likelihood of attacks on humans. Terrain ruggedness might be offering ideal conditions to facilitate attacks on humans, for example remoteness and high amounts of cover to launch ambush attacks.
  4. We provide inference and insights into key determinants of leopard attacks on humans on a landscape scale. These insights can be used to guide future research, inform mitigation measures to reduce leopard attacks and foster a better understanding of the interaction between people and leopards.
  5. This study demonstrates the applicability and novelty of using a hierarchical modelling framework applied to freely and publicly available media reports to inform the applied management of human-wildlife conflict at a national scale.
","language":"English","publisher":"British Ecological Society","doi":"10.1002/pan3.10536","usgsCitation":"Poudel, S., Twining, J., Stedman, R., Ghimire, S., and Fuller, A.K., 2023, Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal: People and Nature, v. 5, no. 6, p. 177-188, https://doi.org/10.1002/pan3.10536.","productDescription":"12 p.","startPage":"177","endPage":"188","ipdsId":"IP-130714","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467093,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/pan3.10536","text":"Publisher Index Page"},{"id":466016,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 79.92780335815291,\n 28.803692392786957\n ],\n [\n 82.71145675855735,\n 27.422532855103327\n ],\n [\n 85.79994047161551,\n 26.519692535546156\n ],\n [\n 88.21732200599081,\n 26.28592528048857\n ],\n [\n 88.23034164615058,\n 28.015630040007878\n ],\n [\n 86.47325630501331,\n 28.11386705824934\n ],\n [\n 82.25297495008891,\n 30.33864883355747\n ],\n [\n 81.61483198209362,\n 30.552482869451012\n ],\n [\n 80.31771509103879,\n 29.885911968905162\n ],\n [\n 79.92780335815291,\n 28.803692392786957\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Poudel, Shashank","contributorId":348087,"corporation":false,"usgs":false,"family":"Poudel","given":"Shashank","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Twining, Joshua P.","contributorId":342747,"corporation":false,"usgs":false,"family":"Twining","given":"Joshua P.","affiliations":[{"id":81920,"text":"Cornell Universtity","active":true,"usgs":false}],"preferred":false,"id":922997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stedman, Richard C.","contributorId":348088,"corporation":false,"usgs":false,"family":"Stedman","given":"Richard C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ghimire, Shravan Kumar","contributorId":348089,"corporation":false,"usgs":false,"family":"Ghimire","given":"Shravan Kumar","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":922930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922927,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}