{"pageNumber":"146","pageRowStart":"3625","pageSize":"25","recordCount":40783,"records":[{"id":70257018,"text":"70257018 - 2022 - How shall we meet? Embracing the opportunities of virtual conferencing","interactions":[],"lastModifiedDate":"2024-09-04T16:10:09.863824","indexId":"70257018","displayToPublicDate":"2024-04-05T11:14:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"How shall we meet? Embracing the opportunities of virtual conferencing","docAbstract":"

The SARS-CoV-2 (COVID-19) pandemic triggered dramatic shifts in the way that ecologists teach, research, and interact (e.g., Cooke et al. 2021). As the world now adjusts to a “new normal” era, there is notable and open discussion about the merits or desire to return to practices used prior to the pandemic (e.g., Roulson 2021). A dominant aspect of these discussions is when and how researchers can return to the practice of large, centralized, in-person conferences that have been the primary mode of professional interaction for decades. While questions of safety are naturally paramount and will guide decision making for some time, there remains the broader question of whether and how to implement virtual and hybrid formats in the future.

Discussions about the return to in-person meetings and expressed resentment about the use of virtual formats (Stevens and Murphy 2021) that we routinely see circulated by professional organizations and on social media assume that the latter is a lesser-quality version of the former. However, we put forward that these sentiments neglect the diversity of opinions among scientists and evidence of prevalent, positive attitudes about the use of virtual (and possibly, as yet undeveloped) modes of conferences. For example, 74% of more than 900 researchers surveyed by the journal Nature during the initial phase of the pandemic expressed a desire for virtual conferences to remain in practice even when travel restrictions were eased (Remmel 2021). Other surveys have shown that scientists are highly interested in alternative conference formats due to concerns about climate change (Nursey-Bray et al. 2019; Haage 2020) and access (Niner and Wassermann 2021). These data indicate most researchers have personal circumstances or perspectives that recognize the value of a broader discussion about how conferences and interactions among researchers can be shaped.

","language":"English","publisher":"Wiley","doi":"10.1002/fsh.10765","usgsCitation":"Rolls, R., Rogosch, J.S., and Kuehne, L.M., 2022, How shall we meet? Embracing the opportunities of virtual conferencing: Fisheries Magazine, v. 47, no. 7, p. 304-306, https://doi.org/10.1002/fsh.10765.","productDescription":"3 p.","startPage":"304","endPage":"306","ipdsId":"IP-136783","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":493295,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10072/419537","text":"External Repository"},{"id":432942,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rolls, Robert J.","contributorId":341926,"corporation":false,"usgs":false,"family":"Rolls","given":"Robert J.","affiliations":[{"id":38381,"text":"University of New England","active":true,"usgs":false}],"preferred":false,"id":909172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogosch, Jane S. 0000-0002-1748-4991","orcid":"https://orcid.org/0000-0002-1748-4991","contributorId":317717,"corporation":false,"usgs":true,"family":"Rogosch","given":"Jane","middleInitial":"S.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":909173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuehne, Lauren M.","contributorId":341927,"corporation":false,"usgs":false,"family":"Kuehne","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":81805,"text":"Omfishient Consulting","active":true,"usgs":false}],"preferred":false,"id":909174,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243153,"text":"70243153 - 2022 - Effect of wave skewness and asymmetry on the evolution of Fire Island, New York","interactions":[],"lastModifiedDate":"2024-02-26T17:55:10.691101","indexId":"70243153","displayToPublicDate":"2023-09-01T11:50:35","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Effect of wave skewness and asymmetry on the evolution of Fire Island, New York","docAbstract":"

Bedload transport of sediment by waves and currents is one of the key physical processes that affect the evolution of coasts, nearshore areas, and the engineering practices there. Wave skewness and asymmetry, both of which increase as waves shoal, result in a net bedload sediment flux over a wave cycle. The impacts of this mechanism on large-scale coastal and shoreline change are investigated in this study, using field observations and Coupled Ocean Atmosphere Wave Sediment Transport (COAWST), a hydrodynamic process-based numerical modeling system (Warner et al., 2010). The study site is Fire Island, New York, located at the Atlantic Coast of the USA, with a focus on the persistent shoreline shape, at the western half of this 50-km-long barrier island, that has been hypothesized to be linked to the sand deposits at the shoreface.

","conferenceTitle":"37th International Conference on Coastal Engineering,","conferenceDate":"July 2-8, 2022","conferenceLocation":"New South Wales, Australia","language":"English","publisher":"Coastal engineering proceedings","doi":"10.9753/icce.v37.sediment.17","usgsCitation":"Parlak, M., Ayhan, B., Warner, J.C., Kalra, T., and Safak, I., 2022, Effect of wave skewness and asymmetry on the evolution of Fire Island, New York, 37th International Conference on Coastal Engineering,, v. 37, New South Wales, Australia, July 2-8, 2022, 1 p., https://doi.org/10.9753/icce.v37.sediment.17.","productDescription":"1 p.","ipdsId":"IP-140577","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":445579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.9753/icce.v37.sediment.17","text":"Publisher Index Page"},{"id":425988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","noUsgsAuthors":false,"publicationDate":"2023-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Parlak, Muhammed","contributorId":304662,"corporation":false,"usgs":false,"family":"Parlak","given":"Muhammed","email":"","affiliations":[{"id":66144,"text":"İstanbul Bilgi University","active":true,"usgs":false}],"preferred":false,"id":871289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ayhan, Bilal","contributorId":304663,"corporation":false,"usgs":false,"family":"Ayhan","given":"Bilal","email":"","affiliations":[{"id":66144,"text":"İstanbul Bilgi University","active":true,"usgs":false}],"preferred":false,"id":871290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":871292,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Safak, Ilgar","contributorId":304429,"corporation":false,"usgs":false,"family":"Safak","given":"Ilgar","affiliations":[{"id":66065,"text":"Dept. Civil Engineering, Istanbul Bilgi University, Istanbul, Türkiye","active":true,"usgs":false}],"preferred":false,"id":871293,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259664,"text":"70259664 - 2022 - Paleoseismic study of the XEOLXELEK–Elk Lake fault: A newly identified Holocene fault in thenorthern Cascadia forearc near Victoria, British Columbia, Canada","interactions":[],"lastModifiedDate":"2024-10-21T11:58:02.331962","indexId":"70259664","displayToPublicDate":"2023-03-15T06:54:51","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Paleoseismic study of the XEOLXELEK–Elk Lake fault: A newly identified Holocene fault in thenorthern Cascadia forearc near Victoria, British Columbia, Canada","docAbstract":"High-resolution topographic data show a tectonic scarp formed in Quaternary sediments near the city of Victoria in the northern Cascadia forearc on Vancouver Island, British Columbia, Canada. A paleoseismic trench excavation across the structure, the XEOLXELEK–Elk Lake fault, shows evidence for a Holocene (after 12.2 cal ka BP) surface-rupturing reverse-slip\nearthquake that produced a fault-propagation fold and resulted in the formation of a ∼1.4 to 3.5 m-high scarp. Fault-propagation fold modelling indicates ∼3.2 m of reverse slip on a 50°-dipping fault plane reproduces the observed deformation, and fault-scaling relations suggest a single earthquake rupture with this surface displacement could occur during a ∼Mw 6.1–\n7.6 earthquake. Given the fault’s location within the metropolitan area of Victoria, an earthquake near this magnitude would result in significant damage to local infrastructure and this fault is worth considering in future seismic hazard assessments.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 11th International INQUA Workshop on Paleoseismology, Active Tectonics and Archaeoseismology","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"11th International INQUA Meeting on Paleoseismology, Active Tectonics and Archeoseismology","conferenceDate":"September 25-30, 2022","conferenceLocation":"France","language":"English","publisher":"Zenodo","usgsCitation":"Harrichhausen, N., Finley, T., Morell, K.D., Regalla, C., Bennett, S.E., Leonard, L.J., Nissen, E., McLeod, E., Lynch, E.M., Salomon, G., and Sethanant, I., 2022, Paleoseismic study of the XEOLXELEK–Elk Lake fault: A newly identified Holocene fault in thenorthern Cascadia forearc near Victoria, British Columbia, Canada, in Proceedings of the 11th International INQUA Workshop on Paleoseismology, Active Tectonics and Archaeoseismology, France, September 25-30, 2022, p. 90-93.","productDescription":"4 p.","startPage":"90","endPage":"93","ipdsId":"IP-144716","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":462986,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://zenodo.org/records/7736477#.ZCXHr3ZBw2w"},{"id":463059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrichhausen, Nicolas 0000-0001-8953-4292","orcid":"https://orcid.org/0000-0001-8953-4292","contributorId":254359,"corporation":false,"usgs":false,"family":"Harrichhausen","given":"Nicolas","email":"","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":916175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finley, Theron 0000-0001-7359-5613","orcid":"https://orcid.org/0000-0001-7359-5613","contributorId":345278,"corporation":false,"usgs":false,"family":"Finley","given":"Theron","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":916176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morell, Kristin D. 0000-0001-8464-3553","orcid":"https://orcid.org/0000-0001-8464-3553","contributorId":254360,"corporation":false,"usgs":false,"family":"Morell","given":"Kristin","email":"","middleInitial":"D.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":916177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regalla, Christine 0000-0003-2975-8336","orcid":"https://orcid.org/0000-0003-2975-8336","contributorId":254361,"corporation":false,"usgs":false,"family":"Regalla","given":"Christine","email":"","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":916178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leonard, Lucinda J. 0000-0002-6492-7660","orcid":"https://orcid.org/0000-0002-6492-7660","contributorId":254362,"corporation":false,"usgs":false,"family":"Leonard","given":"Lucinda","email":"","middleInitial":"J.","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":916180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nissen, Edwin 0000-0002-0406-2706","orcid":"https://orcid.org/0000-0002-0406-2706","contributorId":244221,"corporation":false,"usgs":false,"family":"Nissen","given":"Edwin","email":"","affiliations":[{"id":48865,"text":"University of Victoria; Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":916181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McLeod, Eleanor","contributorId":345279,"corporation":false,"usgs":false,"family":"McLeod","given":"Eleanor","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":916182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lynch, Emerson M. 0000-0003-1419-1373","orcid":"https://orcid.org/0000-0003-1419-1373","contributorId":254363,"corporation":false,"usgs":false,"family":"Lynch","given":"Emerson","email":"","middleInitial":"M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":916183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Salomon, Guy 0000-0002-9239-6449","orcid":"https://orcid.org/0000-0002-9239-6449","contributorId":345280,"corporation":false,"usgs":false,"family":"Salomon","given":"Guy","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":916184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sethanant, Israporn 0000-0003-0962-8999","orcid":"https://orcid.org/0000-0003-0962-8999","contributorId":345281,"corporation":false,"usgs":false,"family":"Sethanant","given":"Israporn","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":916185,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236821,"text":"70236821 - 2022 - Extending body condition scoring beyond measurable rump fat to estimate full range of nutritional condition for moose","interactions":[],"lastModifiedDate":"2024-03-28T13:40:21.977752","indexId":"70236821","displayToPublicDate":"2023-02-18T08:28:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":693,"text":"Alces","active":true,"publicationSubtype":{"id":10}},"title":"Extending body condition scoring beyond measurable rump fat to estimate full range of nutritional condition for moose","docAbstract":"

Moose (Alces alces) populations along the southern extent of their range are largely declining, and there is growing evidence that nutritional condition — which influences several vital rates – is a contributing factor. Moose body condition can presently be estimated only when there is measurable subcutaneous rump fat, which equates to animals with >6% ingesta-free body fat (IFBFat). There is need for a technique to allow body fat estimation of animals in poorer body condition (i.e., <6% body fat). We advance current methods for moose, following those used and validated with other ungulate species, by establishing a moose-specific body condition score (BCS) that can be used to estimate IFBFat in the lower range of condition. Our modified BCS was related strongly (r2 = 0.89) to IFBFat estimates based on measurable rump fat. By extending the predicted relationship to individuals without measurable fat, the BCS equated severe emaciation with 0.67% IFBFat, supporting the accuracy of the method. The lower end of nutritional condition is important for identifying relationships involving life-history characteristics because most state-dependent changes occur at lower levels of condition. Therefore, until the BCS can be validated with moose carcasses, we believe our method to estimate body fat across the full range of condition should yield better understanding of the drivers underlying declining moose populations.

","language":"English","publisher":"Lakehead University","usgsCitation":"Levine, R.L., Smiley, R.A., Jesmer, B.R., Oates, B.A., Goheen, J.R., Stephenson, T.R., Kauffman, M., Fralick, G., and Monteith, K., 2022, Extending body condition scoring beyond measurable rump fat to estimate full range of nutritional condition for moose: Alces, v. 58, p. 91-99.","productDescription":"9 p.","startPage":"91","endPage":"99","ipdsId":"IP-141310","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":427211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427210,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://alcesjournal.org/index.php/alces/article/view/1883","linkFileType":{"id":5,"text":"html"}}],"volume":"58","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Rebecca L.","contributorId":296705,"corporation":false,"usgs":false,"family":"Levine","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":852269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smiley, Rachel A.","contributorId":296706,"corporation":false,"usgs":false,"family":"Smiley","given":"Rachel","email":"","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":852270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jesmer, Brett R.","contributorId":296707,"corporation":false,"usgs":false,"family":"Jesmer","given":"Brett","email":"","middleInitial":"R.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":852271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oates, Brendan A.","contributorId":296708,"corporation":false,"usgs":false,"family":"Oates","given":"Brendan","email":"","middleInitial":"A.","affiliations":[{"id":64152,"text":"4Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":852272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goheen, Jacob R.","contributorId":296709,"corporation":false,"usgs":false,"family":"Goheen","given":"Jacob","email":"","middleInitial":"R.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":852273,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Thomas R.","contributorId":296710,"corporation":false,"usgs":false,"family":"Stephenson","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":64153,"text":"Sierra Nevada Bighorn Sheep Recovery Program","active":true,"usgs":false}],"preferred":false,"id":852274,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":852275,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fralick, Gary L.","contributorId":296711,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary L.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":852276,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Monteith, Kevin L.","contributorId":296712,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":852277,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243295,"text":"70243295 - 2022 - VIMTS: Variational-based Imputation for Multi-modal Time Series","interactions":[],"lastModifiedDate":"2023-05-08T12:00:53.534414","indexId":"70243295","displayToPublicDate":"2023-01-26T06:58:56","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"VIMTS: Variational-based Imputation for Multi-modal Time Series","docAbstract":"
Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels, it is important to appropriately impute the high-dimensional modal missing values. However, most existing imputation methods focus on multivariate time series, fail to simultaneously consider temporal dependencies within each series and the correlations across the series, and also lack a probabilistic interpretation. In this paper, we propose a novel method, which uses a new structured variational approximation technique for the imputation of missing values in multi-modal time series. Instead of directly imputing high-dimensional modal missing values, we use the variational approximation technique to impute intermediate lower-dimensional feature representations of high-dimensional modal missing values from simple modalities related to high-dimensional modality and then feed them into a dynamical model. The dynamical model captures the temporal dependencies of the feature representations and finally predicts the target labels. In order to address the optimization difficulties caused by the lack of ground truth values of lower-dimensional feature representations, we also propose a two-stage isolated optimization strategy for better convergence. We evaluate our method on a real-world stream monitoring dataset. Our extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in both data imputation and prediction performance.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IEEE International Conference on Big Data Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Conference on Big Data","conferenceDate":"December 17-20, 2022","conferenceLocation":"Osaka, Japan","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/BigData55660.2022.10020834","usgsCitation":"Xiaowei Jia, Fair, J.H., and Letcher, B., 2022, VIMTS: Variational-based Imputation for Multi-modal Time Series, in IEEE International Conference on Big Data Proceedings, Osaka, Japan, December 17-20, 2022, p. 349-358, https://doi.org/10.1109/BigData55660.2022.10020834.","productDescription":"10 p.","startPage":"349","endPage":"358","ipdsId":"IP-144527","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":416802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xiaowei Jia","contributorId":304930,"corporation":false,"usgs":false,"family":"Xiaowei Jia","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":871938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":871940,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241141,"text":"70241141 - 2022 - New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States","interactions":[],"lastModifiedDate":"2023-03-13T11:32:48.875374","indexId":"70241141","displayToPublicDate":"2023-01-26T06:29:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States","docAbstract":"

Ecosystem transformations to altered or novel ecological states are accelerating across the globe. Indicators of ecological resilience to disturbance and resistance to invasion can aid in assessing risks and prioritizing areas for conservation and restoration. The sagebrush biome encompasses parts of 11 western states and is experiencing rapid transformations due to human population growth, invasive species, altered disturbance regimes, and climate change. We built on prior use of static soil moisture and temperature regimes to develop new, ecologically relevant and climate responsive indicators of both resilience and resistance. Our new indicators were based on climate and soil water availability variables derived from process-based ecohydrological models that allow predictions of future conditions. We asked: (1) Which variables best indicate resilience and resistance? (2) What are the relationships among the indicator variables and resilience and resistance categories? (3) How do patterns of resilience and resistance vary across the area? We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories. Our models had relatively high multiclass accuracy (80% for resilience; 75% for resistance). Top indicator variables for both resilience and resistance included mean temperature, coldest month temperature, climatic water deficit, and summer and driest month precipitation. Variable relationships and patterns differed among ecoregions but reflected environmental gradients; low resilience and resistance were indicated by warm and dry conditions with high climatic water deficits, and moderately high to high resilience and resistance were characterized by cooler and moister conditions with low climatic water deficits. The new, ecologically-relevant indicators provide information on the vulnerability of resources and likely success of management actions, and can be used to develop new approaches and tools for prioritizing areas for conservation and restoration actions.

","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2022.1009268","usgsCitation":"Chambers, J., Brown, J.L., Bradford, J., Board, D.I., Campbell, S.B., Clause, K.J., Hanberry, B., Schlaepfer, D.R., and Urza, A.K., 2022, New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States: Frontiers in Ecology and Evolution, v. 10, 1009268, 17 p., https://doi.org/10.3389/fevo.2022.1009268.","productDescription":"1009268, 17 p.","ipdsId":"IP-146862","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445594,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.1009268","text":"Publisher Index Page"},{"id":414004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":866252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jessi L.","contributorId":44817,"corporation":false,"usgs":false,"family":"Brown","given":"Jessi","email":"","middleInitial":"L.","affiliations":[{"id":13184,"text":"Program in Ecology, Evolution and Conservation Biology, University of Nevada","active":true,"usgs":false}],"preferred":false,"id":866253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":866254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Board, David I.","contributorId":261260,"corporation":false,"usgs":false,"family":"Board","given":"David","email":"","middleInitial":"I.","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":866255,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Steven B.","contributorId":219259,"corporation":false,"usgs":false,"family":"Campbell","given":"Steven","email":"","middleInitial":"B.","affiliations":[{"id":39979,"text":"USDA Natural Resources Conservation Service, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":866256,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clause, Karen J.","contributorId":177564,"corporation":false,"usgs":false,"family":"Clause","given":"Karen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":866257,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanberry, Brice","contributorId":219278,"corporation":false,"usgs":false,"family":"Hanberry","given":"Brice","affiliations":[{"id":39985,"text":"USDA Forest Service, Rapid City, SD","active":true,"usgs":false}],"preferred":false,"id":866258,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":866259,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Urza, Alexandra K. 0000-0001-9795-6735","orcid":"https://orcid.org/0000-0001-9795-6735","contributorId":261259,"corporation":false,"usgs":false,"family":"Urza","given":"Alexandra","email":"","middleInitial":"K.","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":866260,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239734,"text":"70239734 - 2022 - Hydrogen isotope behavior during rhyolite glass hydration under hydrothermal conditions","interactions":[],"lastModifiedDate":"2023-01-16T19:54:24.541164","indexId":"70239734","displayToPublicDate":"2023-01-16T13:51:28","publicationYear":"2022","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":"Hydrogen isotope behavior during rhyolite glass hydration under hydrothermal conditions","docAbstract":"

The diffusion of molecular water (H2Om) from the environment into volcanic glass can hydrate the glass up to several wt% at low temperature over long timescales. During this process, the water imprints its hydrogen isotope composition (δDH2O) to the glass (δDgl) offset by a glass-H2O fractionation factor (ΔDgl-H2O = δDgl – δDH2O) which is approximately −33‰ at Earth surface temperatures. Glasses hydrate much more rapidly at higher, sub-magmatic temperatures as they interact with H2O during eruption, transport, and emplacement. To aid in the interpretation of δDgl in natural samples, we present hydrogen isotope results from vapor hydration experiments conducted at 175–375 °C for durations of hours to months using natural volcanic glasses. The results can be divided into two thermal regimes: above 250 °C and below 250 °C. Lower temperature experiments yield raw ΔDgl-H2O values in the range of −33 ± 11‰. Experiments at 225 °C using both positive and negative initial ΔDgl-H2O values converge on this range of values, suggesting this range represents the approximate equilibrium fractionation for H isotopes between glass and H2O vapor (103lnαgl-H2O) below 250 °C. Variation in ΔDgl-H2O (−33 ± 11‰) between different experiments and glasses may arise from incomplete hydration, analytical uncertainty, differences in glass chemistry, and/or subordinate kinetic isotope effects. Experiments above 250 °C yield unexpectedly low δDgl values with ΔDgl-H2O values of ≤–85‰. While alteration alone is incapable of explaining the data, these run products have more extensive surface alteration and are not interpreted to reflect equilibrium fractionation between glass and H2O vapor. Fourier transform infrared spectroscopy (FTIR) shows that glass can hydrate with as much as 5.9 wt% H2Om and 1.0 wt% hydroxl (OH−) in the highest P-T experiment at 375 °C and 21.1 MPa. Therefore, we employ a 1D isotope diffusion–reaction model of glass hydration to evaluate the roles of equilibrium fractionation, isotope diffusion, water speciation reactions internal to the glass, and changing boundary conditions (e.g. alteration and dissolution). At lower temperatures, the best fitting model results to experimental data for low silica rhyolite (LSR) glasses require only an equilibrium fractionation factor and yield 103lnαgl-H2O values of −33‰ ± 5‰ and −25‰ ± 5‰ at 175 °C and 225 °C, respectively. At higher temperatures, ΔDgl-H2O is dominated by boundary layer effects during glass hydration and glass surface alteration. The modeled bulk δDgl value is highly responsive to changes in the δDgl boundary condition regardless of the magnitude of other kinetic effects. Observed glass dissolution and surficial secondary mineral formation are likely to impose a disequilibrium boundary layer that drives extreme δDgl fractionation with progressive glass hydration. These results indicate that the observed ΔDgl-H2O of ∼−33 ± 11‰ can be cautiously applied as an equilibrium 103lnαgl-H2O value to natural silicic glasses hydrated below 250 °C to identify hydration sources. This approximate ΔDgl-H2O may be applicable to even higher temperature glasses hydrated on short timescales (of seconds to minutes) in phreatomagmatic or submarine eruptions before H2O in the glass is primarily affected by boundary layer effects associated with alteration on the glass surface.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2022.09.032","usgsCitation":"Hudak, M.R., Bindeman, I.N., Watkins, J.M., and Lowenstern, J.B., 2022, Hydrogen isotope behavior during rhyolite glass hydration under hydrothermal conditions: Geochimica et Cosmochimica Acta, v. 337, p. 33-48, https://doi.org/10.1016/j.gca.2022.09.032.","productDescription":"16 p.","startPage":"33","endPage":"48","ipdsId":"IP-125992","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":445596,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2022.09.032","text":"Publisher Index Page"},{"id":411968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"337","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hudak, Michael R. 0000-0002-0583-5424","orcid":"https://orcid.org/0000-0002-0583-5424","contributorId":287589,"corporation":false,"usgs":false,"family":"Hudak","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":861684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":861685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":861686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":861687,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238786,"text":"70238786 - 2022 - The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview","interactions":[],"lastModifiedDate":"2022-12-12T14:28:56.322416","indexId":"70238786","displayToPublicDate":"2023-01-09T08:21:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview","docAbstract":"

The Yellowstone Plateau Volcanic Field (YPVF) contains >10,000 thermal features including hot springs, pools, geysers, mud pots, and fumaroles with diverse chemical compositions. Arsenic (As) concentrations in YPVF thermal waters typically range from 0.005 to 4 mg/L, but an As concentration of 17 mg/L has been reported. Arsenic data from thermal springs, outflow drainages, rivers, and from volcanic rocks and silica sinter were used to identify the sources, characterize geochemical and microbial processes affecting As, and quantify As fluvial transport. Arsenic in YPVF thermal waters is mainly derived from high temperature leaching of rhyolites. Arsenic concentrations in thermal waters primarily depend on water type, which is controlled by boiling, evaporation, mixing, and mineral precipitation and dissolution. Springs with low As concentrations include acid-SO4 (0.1 ± 0.1 mg/L), NH4-SO4 rich (0.003 ± 0.007 mg/L), and dilute thermal waters (0.1 ± 0.1 mg/L); travertine-forming waters have moderate As concentrations (0.4 ± 0.2 mg/L); and neutral- Cl waters (1.2 ± 0.8 mg/L) common in the western portion of the Yellowstone Caldera and Cl-rich waters (1.9 ± 1.2 mg/L) primarily from Basins near the Caldera boundary have elevated As concentrations. Reduced As species (arsenite and thiolated-As species) are most prevalent near the orifice of hot springs, and then As rapidly oxidizes to arsenate along drainages. Previously published cultivation-based studies and metagenomic data from microbial communities inhabiting a variety of hot springs indicate a widespread distribution of arsenite oxidation and arsenate reduction capabilities among the hot springs. Widespread use and transformation of As by thermophilic microorganisms promotes more soluble and toxic forms. Most of the water discharged from thermal springs eventually ends up in a nearby river where As remains soluble and exhibits little attenuation during downstream transport. Since 2010, 183 ± 10 metric tons/year of As were transported from Yellowstone National Park (YNP) via rivers. The discharge from YPVF thermal features impairs river water quality whereby As concentrations exceed 10 μg/L for many rivers reaches within and downstream from YNP.

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Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":858703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":858704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colman, Daniel R. 0000-0002-3253-6833","orcid":"https://orcid.org/0000-0002-3253-6833","contributorId":299802,"corporation":false,"usgs":false,"family":"Colman","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":2340,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":858706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Madeline Oxner 0000-0001-7661-9748","orcid":"https://orcid.org/0000-0001-7661-9748","contributorId":299803,"corporation":false,"usgs":true,"family":"Johnson","given":"Madeline","email":"","middleInitial":"Oxner","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":858707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyd, Eric S. 0000-0003-4436-5856","orcid":"https://orcid.org/0000-0003-4436-5856","contributorId":299804,"corporation":false,"usgs":false,"family":"Boyd","given":"Eric S.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239344,"text":"70239344 - 2022 - Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","interactions":[],"lastModifiedDate":"2023-01-10T13:02:51.552487","indexId":"70239344","displayToPublicDate":"2023-01-06T07:00:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","docAbstract":"

In the western United States, water allocation decisions often incorporate the needs of endangered fish. In the Klamath River basin, an understanding of temporal variation in annual survival rates of Shortnose Suckers Chasmistes brevirostris and Lost River Suckers Deltistes luxatus and their relation to environmental drivers is critical to water management and sucker recovery. Extinction risk is high for these fish because most individuals in the populations are approaching their maximum life span and recruitment of new fish into the adult populations has never exceeded mortality losses in the past 22 years. We used a time series of mark–recapture data from the years 1999–2021 to analyze the relationship between lake level, water quality covariates, and survival of adult Shortnose Suckers and two spawning populations of Lost River Suckers in Upper Klamath Lake, Oregon. We compared competing model hypotheses in a maximum likelihood framework using Akaike's information criterion and then ran the top environmental covariates in a Bayesian framework to estimate how much of the variation in survival was explained by these covariates as compared to random variation. The complementary analyses found almost unequivocal support for our base model without environmental covariates. Estimated adult sucker survival was high across the time series and consistent with sucker life history (mean annual survival = 0.82–0.91). This suggests that adult suckers were generally robust to interannual variation in lake levels as well as consistently poor water quality within the years of our data set. Recovery time is limited, as a declining survival trend for adult suckers in recent years may be due to the onset of senescence. The successful recovery of suckers in Upper Klamath Lake may rely on shifting research from the causes of adult mortality and its relationship with lake surface elevation to the causes of poor recruitment into adult populations.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10850","usgsCitation":"Krause, J.R., Janney, E.C., Burdick, S.M., Harris, A., and Hayes, B., 2022, Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?: North American Journal of Fisheries Management, v. 42, no. 6, p. 1414-1432, https://doi.org/10.1002/nafm.10850.","productDescription":"19 p.","startPage":"1414","endPage":"1432","ipdsId":"IP-135552","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":498870,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10850","text":"Publisher Index Page"},{"id":435588,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM8DPG","text":"USGS data release","linkHelpText":"Data from 2022 Mark-Recapture Analysis on Water and Endangered Fish in the Klamath River Basin: Do Upper Klamath Surface Elevation and Water Quality Affect Adult Lost River and Shortnose Sucker survival?"},{"id":411620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5704575061356,\n 43.029513801797265\n ],\n [\n -123.5704575061356,\n 40.423789760994765\n ],\n [\n -120.34184816411982,\n 40.423789760994765\n ],\n [\n -120.34184816411982,\n 43.029513801797265\n ],\n [\n -123.5704575061356,\n 43.029513801797265\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Krause, Jacob Richard 0000-0002-9804-2481","orcid":"https://orcid.org/0000-0002-9804-2481","contributorId":300701,"corporation":false,"usgs":true,"family":"Krause","given":"Jacob","email":"","middleInitial":"Richard","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":861201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janney, Eric C. 0000-0002-0228-2174","orcid":"https://orcid.org/0000-0002-0228-2174","contributorId":83629,"corporation":false,"usgs":true,"family":"Janney","given":"Eric","email":"","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":861202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":861203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Alta C. 0000-0002-2123-3028 aharris@usgs.gov","orcid":"https://orcid.org/0000-0002-2123-3028","contributorId":3490,"corporation":false,"usgs":true,"family":"Harris","given":"Alta C.","email":"aharris@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":861204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Brian S. 0000-0001-8229-4070","orcid":"https://orcid.org/0000-0001-8229-4070","contributorId":37022,"corporation":false,"usgs":true,"family":"Hayes","given":"Brian S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":861205,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239180,"text":"70239180 - 2022 - Machine learning for understanding inland water quantity, quality, and ecology","interactions":[],"lastModifiedDate":"2023-01-02T19:31:11.232358","indexId":"70239180","displayToPublicDate":"2023-01-02T13:27:55","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Machine learning for understanding inland water quantity, quality, and ecology","docAbstract":"

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; estimating physically or biologically meaningful parameters for use in further modeling; and revealing patterns in complex, multidimensional data or model outputs. An important research frontier is the injection of limnological knowledge into machine-learning models, which has shown great promise for increasing such models’ accuracy, trustworthiness, and interpretability. Here we describe a few of the most powerful machine learning tools, describe best practices for employing these tools and injecting knowledge guidance, and give examples of their applications to advance understanding of inland waters.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00121-3","usgsCitation":"Appling, A.P., Oliver, S.K., Read, J., Sadler, J.M., and Zwart, J.A., 2022, Machine learning for understanding inland water quantity, quality, and ecology, chap. of Encyclopedia of inland waters, v. 4, p. 585-606, https://doi.org/10.1016/B978-0-12-819166-8.00121-3.","productDescription":"22 p.","startPage":"585","endPage":"606","ipdsId":"IP-122850","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":445607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.31223/x5964s","text":"External Repository"},{"id":411277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","edition":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Mehner, Thomas","contributorId":272917,"corporation":false,"usgs":false,"family":"Mehner","given":"Thomas","email":"","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":860710,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Tockner, Klement","contributorId":224174,"corporation":false,"usgs":false,"family":"Tockner","given":"Klement","email":"","affiliations":[{"id":40838,"text":"FWF Austrian Science Fund","active":true,"usgs":false}],"preferred":false,"id":860711,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":860694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239182,"text":"70239182 - 2022 - Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","interactions":[],"lastModifiedDate":"2023-01-02T19:15:38.169222","indexId":"70239182","displayToPublicDate":"2023-01-02T13:08:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","docAbstract":"This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments. In this paper, we first build a state-aware graph model to represent the interactions amongst streams and reservoirs, and then propose a parallel learning structure to extract the reservoir release information and use it to improve the prediction. In particular, for reservoirs with no available release information, we mimic the water managers' release decision process through a pseudo-prospective learning method, which infers the release information from anticipated water temperature dynamics. For reservoirs with the release information, we leverage a physics-based model to simulate the water release temperature and transfer such information to guide the learning process for other reservoirs. The evaluation for the Delaware River Basin shows that the proposed method brings over 10% accuracy improvement over existing data-driven models for stream temperature prediction when the release data is not available for any reservoirs. The performance is further improved after we incorporate the release data and physical simulations for a subset of reservoirs.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 SIAM International Conference on Data Mining (SDM)","conferenceDate":"April 28-30, 2022","conferenceLocation":"Alexandria, Virginia, United States","language":"English","publisher":"Society for Industrial and Applied Mathematics","doi":"10.1137/1.9781611977172.11","usgsCitation":"Jia, X., Chen, S., Xie, Y., Yang, H., Appling, A.P., Oliver, S.K., and Jiang, Z., 2022, Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature, in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), Alexandria, Virginia, United States, April 28-30, 2022, p. 91-99, https://doi.org/10.1137/1.9781611977172.11.","productDescription":"9 p.","startPage":"91","endPage":"99","ipdsId":"IP-134356","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":445610,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/2202.05714","text":"External Repository"},{"id":411275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-04-20","publicationStatus":"PW","contributors":{"editors":[{"text":"Banerjee, Arindam","contributorId":300535,"corporation":false,"usgs":false,"family":"Banerjee","given":"Arindam","email":"","affiliations":[],"preferred":false,"id":860702,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Zhou, Zhi-Hua","contributorId":300536,"corporation":false,"usgs":false,"family":"Zhou","given":"Zhi-Hua","email":"","affiliations":[],"preferred":false,"id":860703,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Papalexakis, Evangelos E.","contributorId":300537,"corporation":false,"usgs":false,"family":"Papalexakis","given":"Evangelos","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":860704,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Riondato, Matteo","contributorId":300538,"corporation":false,"usgs":false,"family":"Riondato","given":"Matteo","email":"","affiliations":[],"preferred":false,"id":860705,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":860696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":860697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Haoyu","contributorId":298611,"corporation":false,"usgs":false,"family":"Yang","given":"Haoyu","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860700,"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":860701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230419,"text":"70230419 - 2022 - Ground motion selection for nonlinear response history analyses of concrete dams","interactions":[],"lastModifiedDate":"2023-05-16T18:48:59.636551","indexId":"70230419","displayToPublicDate":"2022-12-31T13:45:28","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Ground motion selection for nonlinear response history analyses of concrete dams","docAbstract":"

Evaluating the seismic performance of a 3D concrete dam using nonlinear response history analysis (NLRHA) requires three orthogonal components of ground acceleration histories, or ground motions (GMs) for brevity. Although much progress has been made for the topic of ground motion selection and modification (GMSM) in the context of multistory buildings, NLRHA of dams requires at least two additional considerations: (i) accounting for multiple modes of vibration and (ii) including three orthogonal components of GMs. To convey the key ideas in developing an ensemble of multicomponent GMs for this context, the fundamentals of GMSM are first briefly reviewed using a case study. Then, special considerations for concrete dams are highlighted. Finally, a practical method for developing target spectra and selecting multicomponent GMs is presented.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2022 USSD annual conference & exhibition","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"United States Society on Dams (USSD)","usgsCitation":"Kwong, N.S., 2022, Ground motion selection for nonlinear response history analyses of concrete dams, in 2022 USSD annual conference & exhibition, 15 p.","productDescription":"15 p.","ipdsId":"IP-135268","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":417105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398526,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ussd.conferencespot.org/2022/bio/bmt3b25ndXNnc2dvdg%3D%3D","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kwong, N. 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The 11 locations sampled were long-term monitoring sites that had been annually sampled since 1974. In 2020, the number of species collected at each site ranged from 0 to 13, with a mean of 6.3 and median of six. All comparisons to 2020 results were limited to past collections from Management Unit WI-2. Mean total biomass was 10.5 kg/ha which was similar to the average observed over the past 10 years (10.3 kg/ha), less than averages over the past 20 and 30-years, 15.3 and 19.8 kg/ha respectively, and higher than the average observed from 1974-84 (4.7 kg/ha). Average biomass in 2020 was highest for Bloater (6.2 kg/ha), Lake Whitefish (2.3 kg/ha), and Cisco (0.9 kg/ha). Rainbow Smelt biomass averaged 0.3 kg/ha. Year-class strength, as measured by age-1 densities, was well below the 5, 10, and 25-year averages for Bloater, Cisco, Lake Whitefish and Rainbow Smelt. Bloater averaged 1 age-1 fish/ha, Cisco, 0.2 age-1 fish/ha, Lake Whitefish, 15 age-1 fish/ha, and Rainbow Smelt 6 age-1 fish/ha. Cisco survival to age-1 has been near non-existent since the 2014- and 2015-year classes and the last moderate sized year class was in 2009. 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mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evrard, Lori M. 0000-0001-8582-5818 levrard@usgs.gov","orcid":"https://orcid.org/0000-0001-8582-5818","contributorId":2720,"corporation":false,"usgs":true,"family":"Evrard","given":"Lori","email":"levrard@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Owen 0000-0003-0451-110X","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":216889,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240292,"text":"70240292 - 2022 - A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","interactions":[],"lastModifiedDate":"2023-02-03T16:39:51.748313","indexId":"70240292","displayToPublicDate":"2022-12-31T10:22:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2785,"text":"Monographs of the Western North American Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","title":"A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs","docAbstract":"

Las Vegas bearpoppy (Arctomecon californica) occurrences have fluctuated during the past several decades, in part due to interannual variability in rainfall that influences recruitment and mortality events; yet, development in the Las Vegas Valley continues to threaten habitat supporting this species. Arctomecon californica was petitioned for listing under the Endangered Species Act in 2019 and is currently under review to determine whether listing is warranted (USFWS 2020). This review updates species information for A. californica and includes recent insights into the species' seed ecology, habitat requirements and suitability models, propagation and reintroduction, and pollinator biology. We include information from the past 20 years in these areas that supplement conservation and restoration actions for the species. We also identify topics with scarce information and highlight areas for future study, including the following: preservation of genetic diversity through germplasm collections, identification of mechanisms driving the species' soil endemism, maintenance of A. californica–pollinator relationships through understanding pollinator habitat, determination of the viable seed fraction and its longevity in the soil seed reserves, and prediction of population response to regional climate change based on demographic modeling.

","language":"English","publisher":"Monte L. Bean Life Science Museum, Brigham Young University","doi":"10.3398/042.014.0101","usgsCitation":"Stosich, A., DeFalco, L., and Scoles-Sciulla, S.J., 2022, A review of Arctomecon californica (Papaveraceae) with a focus on the species’ potential for propagation and reintroduction and conservation needs: Monographs of the Western North American Naturalist, v. 14, no. 1, p. 1-22, https://doi.org/10.3398/042.014.0101.","productDescription":"22 p.","startPage":"1","endPage":"22","ipdsId":"IP-140238","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3398/042.014.0101","text":"Publisher Index Page"},{"id":412688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","county":"Clark County, Mohave 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Alexander 0000-0002-4403-1090","orcid":"https://orcid.org/0000-0002-4403-1090","contributorId":301994,"corporation":false,"usgs":true,"family":"Stosich","given":"Alexander","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":208658,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scoles-Sciulla, Sara J. 0000-0003-1693-5030 sscoles@usgs.gov","orcid":"https://orcid.org/0000-0003-1693-5030","contributorId":2614,"corporation":false,"usgs":true,"family":"Scoles-Sciulla","given":"Sara","email":"sscoles@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863262,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239159,"text":"dr1165 - 2022 - Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","interactions":[],"lastModifiedDate":"2023-01-03T11:51:43.361028","indexId":"dr1165","displayToPublicDate":"2022-12-30T09:43:53","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1165","displayTitle":"Range-wide Population Trend Analysis for Greater Sage-Grouse (Centrocercus urophasianus)—Updated 1960–2021","title":"Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021","docAbstract":"

Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021) and includes the addition of three analytical updates: (1) identification of population nadirs (lowest points within cycles) at the lek (breeding ground) and neighborhood cluster (group of leks) spatial scales, (2) truncation of prior distributions on rate of change in apparent abundance values to more realistic boundaries for leks with missing data, and (3) addition of 2 years of population lek count data (2020 and 2021) to the current dataset (1953–2021). Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.6). Cumulative declines were 42.5, 65.6, and 80.1 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that range-wide populations continued to decline during 2020 and 2021, although two climate clusters (eastern area and Bi-State area) have shown growth in population abundance in recent years, indicating they have surpassed a recent population abundance nadir.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1165","issn":"2771-9448","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021: Data Report 1165, 16 p., https://doi.org/10.3133/dr1165.","productDescription":"Report: viii, 16 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-144163","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":411225,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"U.S. Geological Survey data release","linkHelpText":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (1960–2021)"},{"id":411222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1165/dr1165.pdf","text":"Report","size":"8.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1165"},{"id":411223,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1165/images"},{"id":411221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1165/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.19101705712868,\n 48.89284566335482\n ],\n [\n -122.13131256755497,\n 48.37855671670232\n ],\n [\n -121.63700211579436,\n 47.85380781194411\n ],\n [\n -121.86067071873805,\n 47.51242609246373\n ],\n [\n -121.87668371107034,\n 47.11115078292244\n ],\n [\n -122.4532512741522,\n 46.70567448488106\n ],\n [\n -122.57397075207928,\n 46.22739431053469\n ],\n [\n -122.36606239056442,\n 45.90712886523926\n ],\n [\n -121.91588031540189,\n 45.358384504867246\n ],\n [\n -122.45322729336237,\n 44.953343420279765\n ],\n [\n -122.52983521148596,\n 44.52144457126775\n ],\n [\n -122.54530349941209,\n 44.259263341793\n ],\n [\n -122.85735468588939,\n 43.86827650155766\n ],\n [\n -123.01704364538307,\n 43.46272668287682\n ],\n [\n -123.01026119844099,\n 43.21452795313556\n ],\n [\n -123.10511846238558,\n 42.807780288627384\n ],\n [\n -122.77301525682884,\n 42.07884845828369\n ],\n [\n -122.62454526743153,\n 41.5133850817748\n ],\n [\n -122.14925905063672,\n 40.83036532174481\n ],\n [\n -121.68837858072334,\n 40.49667518946566\n ],\n [\n -121.57750448671058,\n 39.80789795232664\n ],\n [\n -121.16256808423407,\n 39.215880289775015\n ],\n [\n -120.64471039578123,\n 38.57511631320122\n ],\n [\n -119.84212022937817,\n 37.43985090599355\n ],\n [\n -118.83488568782542,\n 36.55782111752346\n ],\n [\n -118.59349323888452,\n 35.83229168807691\n ],\n [\n -117.40450808029976,\n 35.69535862053445\n ],\n [\n -116.43105077565428,\n 35.459239916109155\n ],\n [\n -114.6229823251931,\n 35.413666627683824\n ],\n [\n -96.18756245429773,\n 35.03931170375198\n ],\n [\n -94.57905716272413,\n 37.1225590274522\n ],\n [\n -94.53555587839759,\n 39.07884303955356\n ],\n [\n -95.83917624651616,\n 40.61806593890233\n ],\n [\n -96.37123578579417,\n 42.39205565405334\n ],\n [\n -96.46625839716233,\n 45.31077773932529\n ],\n [\n -97.30155203348903,\n 48.980368873698694\n ],\n [\n -122.19101705712868,\n 48.89284566335482\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
https://www.usgs.gov/centers/werc

Contact Pubs Warehouse
https://pubs.er.usgs.gov/contact

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2022-12-30","noUsgsAuthors":false,"publicationDate":"2022-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":860636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":860637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":860638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":860640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":860641,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanser, Steve E. 0000-0002-4430-2073 shanser@usgs.gov","orcid":"https://orcid.org/0000-0002-4430-2073","contributorId":152523,"corporation":false,"usgs":true,"family":"Hanser","given":"Steve","email":"shanser@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":860642,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wiechman, Lief A. 0000-0002-3804-4426","orcid":"https://orcid.org/0000-0002-3804-4426","contributorId":184047,"corporation":false,"usgs":true,"family":"Wiechman","given":"Lief","email":"","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860643,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":860644,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70245790,"text":"70245790 - 2022 - Perspectives on premetamorphic stratabound tourmalinites","interactions":[],"lastModifiedDate":"2023-06-27T12:10:06.160041","indexId":"70245790","displayToPublicDate":"2022-12-30T07:09:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":15684,"text":"Journal of Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Perspectives on premetamorphic stratabound tourmalinites","docAbstract":"

Stratabound tourmalinites are metallogenically important rocks that locally show a close spatial association with diverse types of mineralization, especially volcanogenic massive sulfides (VMS) and clastic-dominated (CD) Zn-Pb deposits. These tourmalinite occurrences pan the geologic record from Eoarchean to Jurassic. Host lithologies are dominated by clastic metasedimentary rocks but in some areas include metavolcanic rocks, marble, or metaevaporites. Stratabound and stratiform (conformable) tourmalinites commonly display sedimentary structures such as graded beds, cross-beds, and rip-up clasts. In most cases, field and microtextural relationships are consistent with a synsedimentary to the early diagenetic introduction of boron as a precursor to tourmaline formation.


Whole-rock geochemical data for major, trace, and rare earth elements (REE) provide valuable insights into tourmalinite origins. Al-normalized values relative to those for least-altered host metasedimentary rocks suggest that tourmalinites in proximal settings at or near hydrothermal vent sites characterized by high fluid/rock regimes (e.g., Sullivan Pb-Zn-Ag deposit, Canada) have very different signatures than those in low fluid/rock, distal settings (e.g., Broken Hill Pb-Zn-Ag deposit, Australia). The high fluid/rock regimes at Sullivan show large mass changes of +60 % for Mg and +180 % for Mn, as well as large variations in abundances of light and middle REE. In contrast, tourmalinite formation in low fluid/rock regimes yields minimal Al-normalized changes in major elements, trace elements, and REE. Boron isotope values of tourmalinite-hosted tourmaline vary widely from -26.1 to +27.5 ‰, and are attributed mainly to boron sources (e.g., sediments, evaporites) with generally minor influence from processes such as formational temperature, fluid/rock ratio, and secular variation in seawater δ11B values.


Laterally extensive stratiform tourmalinites formed mainly by syngenetic or early diagenetic processes on or beneath the seafloor. The syngenetic process is attributed to the interaction of vented B-rich brines with aluminous minerals in sediments, whereas the diagenetic process involves the selective replacement of aluminous sediments by B-rich fluids. Modern examples of tourmalinites, as yet undiscovered, may exist in metalliferous sediments of the Red Sea and the eastern Pacific Ocean, in altered volcaniclastic sediments within active seafloor-hydrothermal systems of the South Pacific, and in hydrothermal mounds and vents associated with mafic sill complexes in extensional basins as in the North Sea and South China Sea. Stratabound tourmalinites that contain base-metal sulfides, high Mn concentrations (>1 wt. % MnO), or positive Eu anomalies can be valuable exploration guides for base-metal sulfide deposits in sedimentary and volcanic terranes.

","language":"English","publisher":"Czech Geological Society","doi":"10.3190/jgeosci.349","usgsCitation":"Slack, J.F., 2022, Perspectives on premetamorphic stratabound tourmalinites: Journal of Geosciences, v. 67, no. 2, p. 73-102, https://doi.org/10.3190/jgeosci.349.","productDescription":"30 p.","startPage":"73","endPage":"102","ipdsId":"IP-136843","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":445617,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3190/jgeosci.349","text":"Publisher Index Page"},{"id":418502,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":876333,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70241988,"text":"70241988 - 2022 - Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022","interactions":[],"lastModifiedDate":"2023-04-03T12:00:47.057179","indexId":"70241988","displayToPublicDate":"2022-12-30T06:59:14","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022","docAbstract":"Red Knots (Calidris canutus rufa) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). In the late 1990s and early 2000s, the number of Red Knots found at Delaware Bay declined from ~50,000 to ~13,000. Horseshoe crabs have been harvested for use as bait in eel (Anguilla rostrata) and whelk (Busycon) fisheries since at least 1990, and some avian conservation biologists hypothesized that horseshoe crab harvest levels in the 1990s prevented sufficient refueling for successful migration to the breeding grounds, nesting, and survival for the remainder of the annual cycle. Since 2013, the harvest of horseshoe crabs in the Delaware Bay region has been managed using an Adaptive Resource Management (ARM) framework. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat for Red Knots and other migrating shorebirds. For annual harvest recommendations, the ARM framework requires annual estimates of horseshoe crab population size and the Red Knot stopover population size. We conducted a mark-recapture-resight investigation to estimate the passage population of Red Knots at Delaware Bay in 2022. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The 2022 Red Knot mark-resight dataset","language":"English","publisher":"Delaware Division of Fish and Wildlife","usgsCitation":"Lyons, J.E., 2022, Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022, 23 p.","productDescription":"23 p.","ipdsId":"IP-150372","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":415051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415047,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.alpha.delaware.gov/fish-wildlife/conservation/shorebirds/research/"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89400562504255,\n 39.91434007716924\n ],\n [\n -75.89400562504255,\n 38.41619673661057\n ],\n [\n -74.49548202885819,\n 38.41619673661057\n ],\n [\n -74.49548202885819,\n 39.91434007716924\n ],\n [\n -75.89400562504255,\n 39.91434007716924\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":868432,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254865,"text":"70254865 - 2022 - Environmental drivers of demography and potential factors limiting the recovery of an endangered marine top predator","interactions":[],"lastModifiedDate":"2024-06-12T00:38:08.360231","indexId":"70254865","displayToPublicDate":"2022-12-28T19:35:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Environmental drivers of demography and potential factors limiting the recovery of an endangered marine top predator","docAbstract":"

Understanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age-specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions (Eumetopias jubatus) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local- and basin-scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population-level responses to future climate variability.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4325","usgsCitation":"Warlick, A.J., Johnson, D.S., Gelatt, T., and Converse, S.J., 2022, Environmental drivers of demography and potential factors limiting the recovery of an endangered marine top predator: Ecosphere, v. 13, no. 12, e4325, 22 p., https://doi.org/10.1002/ecs2.4325.","productDescription":"e4325, 22 p.","ipdsId":"IP-139276","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":445619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4325","text":"Publisher Index Page"},{"id":429937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.46357232138772,\n 49.37139878865602\n ],\n [\n -147.11982232138757,\n 49.37139878865602\n ],\n [\n -147.11982232138757,\n 61.76514999401567\n ],\n [\n -179.46357232138772,\n 61.76514999401567\n ],\n [\n -179.46357232138772,\n 49.37139878865602\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Warlick, Amanda J.","contributorId":299750,"corporation":false,"usgs":false,"family":"Warlick","given":"Amanda","email":"","middleInitial":"J.","affiliations":[{"id":13190,"text":"School of Aquatic and Fishery Sciences, University of Washington","active":true,"usgs":false}],"preferred":false,"id":902732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Devin S.","contributorId":167773,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin","email":"","middleInitial":"S.","affiliations":[{"id":24829,"text":"National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, Washington","active":true,"usgs":false}],"preferred":false,"id":902733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gelatt, Tom S.","contributorId":337852,"corporation":false,"usgs":false,"family":"Gelatt","given":"Tom S.","affiliations":[{"id":35876,"text":"Alaska Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":902734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902731,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255116,"text":"70255116 - 2022 - Hidden in plain sight: Integrated population models to resolve partially observable latent population structure","interactions":[],"lastModifiedDate":"2024-06-14T16:30:02.406565","indexId":"70255116","displayToPublicDate":"2022-12-28T11:25:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Hidden in plain sight: Integrated population models to resolve partially observable latent population structure","docAbstract":"

Population models often require detailed information on sex-, age-, or size-specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage- or size-structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class-specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high-resolution output of latent population structure that is partially observed during the monitoring process.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4321","usgsCitation":"Lawson, A.J., Jodice, P.G., Rainwater, T., Dunham, K.D., Hart, M., Butfiloski, J.W., Wilkinson, P., and Moore, C., 2022, Hidden in plain sight: Integrated population models to resolve partially observable latent population structure: Ecosphere, v. 13, e4321, 22 p., https://doi.org/10.1002/ecs2.4321.","productDescription":"e4321, 22 p.","ipdsId":"IP-137983","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4321","text":"Publisher Index Page"},{"id":430217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University","active":true,"usgs":false}],"preferred":false,"id":903451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Kylee Denise 0000-0002-9249-0590","orcid":"https://orcid.org/0000-0002-9249-0590","contributorId":296991,"corporation":false,"usgs":true,"family":"Dunham","given":"Kylee","email":"","middleInitial":"Denise","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Morgan","contributorId":338673,"corporation":false,"usgs":false,"family":"Hart","given":"Morgan","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butfiloski, Joseph W.","contributorId":338675,"corporation":false,"usgs":false,"family":"Butfiloski","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilkinson, Philip M.","contributorId":338676,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Philip M.","affiliations":[{"id":54598,"text":"Tom Yawkey Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":903455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Clinton 0000-0001-7782-3994 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0001-7782-3994","contributorId":338679,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903456,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239082,"text":"70239082 - 2022 - Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods","interactions":[],"lastModifiedDate":"2022-12-26T18:10:26.43659","indexId":"70239082","displayToPublicDate":"2022-12-26T11:24:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods","docAbstract":"

  1. Water is a critical and limited resource, particularly in the arid West, but water availability is projected to decline even while demand increases due to growing human populations and increases in duration and severity of drought. Mesic areas provide important water resources for numerous wildlife species, including the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), an indicator for the health of sagebrush ecosystems. Understanding how wildlife use these crucial areas is necessary to inform management and conservation of sensitive species. Specifically, the influence of anthropogenic water subsidies such as irrigated pastures is not well-studied.

  2. We evaluated brood-rearing habitat selection and brood survival of sage-grouse in Long Valley, California, an area where the water rights are primarily owned by the city of Los Angeles and water is used locally to irrigate for livestock. This area thus represents a unique balance between the needs of wildlife and people that could increasingly define future water management.

  3. In this study, sage-grouse broods moved closer to the edge of mesic areas and used more interior areas during the late brood-rearing period, selecting for greener areas after 1 July. Mesic areas were particularly important during dry years, with broods using areas farther interior than in wet years. Brood survival was also positively influenced by the availability and condition of mesic resources, as indicated by variation in values of normalized difference vegetation index (NDVI), with survival peaking at moderate values of NDVI and just outside the edge but decreasing inside the mesic areas.

  4. Our results highlight the importance of quality edge habitat of large mesic areas for sage-grouse to balance habitat selection and survival, particularly during drier years and during the late brood-rearing period, which is a critical period because chick survival has been shown to influence population growth.

  5. This study highlights the implications of large-scale anthropogenic water manipulation, and the balance between local irrigation and water distribution to benefit other regions, from the context of a species of high conservation concern in North American sagebrush ecosystems.

","language":"English","publisher":"British Ecological Society","doi":"10.1002/2688-8319.12194","usgsCitation":"Severson, J.P., Coates, P.S., Milligan, M.C., O’Neil, S.T., Ricca, M.A., Abele, S., Boone, J., and Casazza, M.L., 2022, Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods: Ecological Solutions and Evidence, v. 3, no. 4, e12194, 14 p., https://doi.org/10.1002/2688-8319.12194.","productDescription":"e12194, 14 p.","ipdsId":"IP-133694","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445624,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2688-8319.12194","text":"Publisher Index Page"},{"id":435591,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P958IEOS","text":"USGS data release","linkHelpText":"Selection and Survival of Greater Sage-Grouse Broods in Mesic Areas of Long Valley, California (2003 - 2018)"},{"id":411052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Convict Creek, Hot Creek, Laurel Creek, Long Valley, Owens River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.1350693897337,\n 37.73064685702448\n ],\n [\n -119.11309673348379,\n 37.63718071169116\n ],\n [\n -118.887877006921,\n 37.56101670388047\n ],\n [\n -118.69561626473362,\n 37.493492064720016\n ],\n [\n -118.65167095223377,\n 37.36699156235693\n ],\n [\n -118.62420513192129,\n 37.19653339628323\n ],\n [\n -118.49786235848364,\n 37.268697427166515\n ],\n [\n 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]\n}","volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859986,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859987,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milligan, Megan C. 0000-0001-8466-7803","orcid":"https://orcid.org/0000-0001-8466-7803","contributorId":296042,"corporation":false,"usgs":true,"family":"Milligan","given":"Megan","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859989,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859990,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abele, Steve C.","contributorId":300333,"corporation":false,"usgs":false,"family":"Abele","given":"Steve C.","affiliations":[{"id":65086,"text":"U.S. Fish and Wildlife Service, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":859991,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boone, John D.","contributorId":300334,"corporation":false,"usgs":false,"family":"Boone","given":"John D.","affiliations":[{"id":65087,"text":"Great Basin Bird Observatory, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":859992,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":859993,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70257013,"text":"70257013 - 2022 - Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","interactions":[],"lastModifiedDate":"2024-09-04T15:45:23.281028","indexId":"70257013","displayToPublicDate":"2022-12-22T08:39:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5278,"text":"Mammal Research","active":true,"publicationSubtype":{"id":10}},"title":"Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","docAbstract":"

Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears (Tremarctos ornatus) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species’ range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear.

","language":"English","publisher":"Springer Link","doi":"10.1007/s13364-022-00664-0","usgsCitation":"Falconi, N., Finn, J.T., Fuller, T., and Organ, J.F., 2022, Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru: Mammal Research, v. 68, p. 143-150, https://doi.org/10.1007/s13364-022-00664-0.","productDescription":"8 p.","startPage":"143","endPage":"150","ipdsId":"IP-119469","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-69.59042,-17.58001],[-69.85844,-18.09269],[-70.37257,-18.34798],[-71.37525,-17.7738],[-71.46204,-17.36349],[-73.44453,-16.35936],[-75.23788,-15.26568],[-76.00921,-14.64929],[-76.42347,-13.82319],[-76.25924,-13.53504],[-77.10619,-12.22272],[-78.09215,-10.37771],[-79.03695,-8.38657],[-79.44592,-7.93083],[-79.76058,-7.19434],[-80.53748,-6.54167],[-81.25,-6.13683],[-80.92635,-5.69056],[-81.41094,-4.73676],[-81.09967,-4.03639],[-80.30256,-3.40486],[-80.18401,-3.82116],[-80.46929,-4.05929],[-80.44224,-4.42572],[-80.02891,-4.34609],[-79.62498,-4.4542],[-79.20529,-4.95913],[-78.6399,-4.54778],[-78.45068,-3.8731],[-77.8379,-3.00302],[-76.63539,-2.60868],[-75.545,-1.56161],[-75.23372,-0.91142],[-75.37322,-0.15203],[-75.10662,-0.05721],[-74.4416,-0.53082],[-74.1224,-1.00283],[-73.6595,-1.26049],[-73.07039,-2.30895],[-72.32579,-2.43422],[-71.77476,-2.16979],[-71.41365,-2.3428],[-70.81348,-2.25686],[-70.04771,-2.72516],[-70.69268,-3.74287],[-70.39404,-3.76659],[-69.89364,-4.29819],[-70.79477,-4.25126],[-70.92884,-4.40159],[-71.74841,-4.59398],[-72.89193,-5.27456],[-72.96451,-5.74125],[-73.21971,-6.08919],[-73.12003,-6.62993],[-73.72449,-6.9186],[-73.7234,-7.341],[-73.98724,-7.52383],[-73.57106,-8.42445],[-73.01538,-9.03283],[-73.22671,-9.46221],[-72.56303,-9.52019],[-72.18489,-10.0536],[-71.30241,-10.07944],[-70.48189,-9.49012],[-70.54869,-11.00915],[-70.09375,-11.12397],[-69.52968,-10.95173],[-68.66508,-12.5613],[-68.88008,-12.89973],[-68.92922,-13.60268],[-68.94889,-14.45364],[-69.33953,-14.9532],[-69.16035,-15.32397],[-69.38976,-15.66013],[-68.95964,-16.5007],[-69.59042,-17.58001]]]},\"properties\":{\"name\":\"Peru\"}}]}","volume":"68","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Falconi, Nereyda","contributorId":272944,"corporation":false,"usgs":false,"family":"Falconi","given":"Nereyda","email":"","affiliations":[],"preferred":false,"id":909147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, John T.","contributorId":43398,"corporation":false,"usgs":false,"family":"Finn","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":16720,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003-9485, USA","active":true,"usgs":false}],"preferred":false,"id":909148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Todd K.","contributorId":270781,"corporation":false,"usgs":false,"family":"Fuller","given":"Todd K.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":909150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239113,"text":"70239113 - 2022 - Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","interactions":[],"lastModifiedDate":"2022-12-28T14:04:34.673006","indexId":"70239113","displayToPublicDate":"2022-12-22T08:00:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","docAbstract":"

Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence-background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target-group points from Salix (willow) occurrences to represent available riparian habitat. Random-background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix-background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem-specific target-group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad-scale climatic requirements.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4305","usgsCitation":"Perry, L.G., Jarnevich, C.S., and Shafroth, P., 2022, Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions: Ecosphere, v. 13, no. 12, e4305, 22 p., https://doi.org/10.1002/ecs2.4305.","productDescription":"e4305, 22 p.","ipdsId":"IP-133461","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445636,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4305","text":"Publisher Index Page"},{"id":435593,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIB2TF","text":"USGS data release","linkHelpText":"Occurrence data and models for woody riparian native and invasive plant species in the conterminous western USA"},{"id":411118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 28\n ],\n [\n -100,\n 28\n ],\n [\n -100,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G","contributorId":177873,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":860091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860093,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239342,"text":"70239342 - 2022 - Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","interactions":[],"lastModifiedDate":"2023-01-10T13:25:00.980147","indexId":"70239342","displayToPublicDate":"2022-12-22T07:22:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp","docAbstract":"

Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4331","usgsCitation":"Schoolmaster, D.R., Coulter, A.A., Kallis, J.L., Glover, D., Dettmers, J.M., and Erickson, R.A., 2022, Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp: Ecosphere, v. 13, no. 12, e4331, 14 p., https://doi.org/10.1002/ecs2.4331.","productDescription":"e4331, 14 p.","ipdsId":"IP-133899","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445639,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4331","text":"Publisher Index Page"},{"id":411623,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.8161968210176,\n 42.527099685626524\n ],\n [\n -91.59388938446455,\n 42.527099685626524\n ],\n [\n -91.59388938446455,\n 38.52233430466708\n ],\n [\n -87.8161968210176,\n 38.52233430466708\n ],\n [\n -87.8161968210176,\n 42.527099685626524\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolmaster, Donald R. Jr. 0000-0003-0910-4458","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":221551,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","suffix":"Jr.","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coulter, Alison A.","contributorId":90992,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[{"id":26877,"text":"Southern Illinois University, Carbondale, IL","active":true,"usgs":false},{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":861194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kallis, Jahn L.","contributorId":205603,"corporation":false,"usgs":false,"family":"Kallis","given":"Jahn","email":"","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":861195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glover, David C.","contributorId":274925,"corporation":false,"usgs":false,"family":"Glover","given":"David C.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":861196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dettmers, John M.","contributorId":191256,"corporation":false,"usgs":false,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":861197,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":861198,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238931,"text":"ofr20221108 - 2022 - Using seismic noise correlation to determine the shallow velocity structure of the Seattle basin, Washington","interactions":[],"lastModifiedDate":"2026-03-30T20:54:17.77567","indexId":"ofr20221108","displayToPublicDate":"2022-12-21T09:18:30","publicationYear":"2022","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":"2022-1108","displayTitle":"Using Seismic Noise Correlation to Determine the Shallow Velocity Structure of the Seattle Basin, Washington","title":"Using seismic noise correlation to determine the shallow velocity structure of the Seattle basin, Washington","docAbstract":"

Cross-correlation waveforms of seismic noise in the Seattle basin, Washington, were analyzed to determine the group velocities of surface waves and constrain the shear-wave velocity (VS) for depths less than about 2 kilometers (km). Twenty broadband seismometers were deployed for about 3 weeks in three dense arrays separated by about 5 km, with minimum intra-array station spacing of about 0.5 km. Cross correlations of only 9 days of noise recordings produced Green’s functions at periods of 2 to 6 seconds (s) for sites about 5 km apart. Usable noise correlations for shorter periods of 0.5 to 1.0 s were found for sites within the arrays separated by 1 to 2 km. We bandpass filtered the inter- and intra-array cross-correlation waveforms to determine Love-wave group velocities at periods of 0.5 to 6 s for paths within the Seattle basin and at 3 to 5 s for paths crossing the southern edge of the basin. We developed a non-linear inversion program to determine VS profiles that fit the observed group velocities for paths in the basin. We found that these group velocities are well fit by a variety of VS profiles, each with a distinct jump in VS at depths ranging from 0.9 to 1.3 km. This jump in VS is inferred to represent the top of bedrock. The observed group velocities are not matched by models with the top of bedrock at 0.7-km depth or shallower. The group velocities are also fit by a model with no large jumps in VS in depths less than 2.4 km. The VS profile for the middle of the basin from Stephenson and others (2017), with a depth to bedrock of 0.9 km, also adequately fits the group velocity observations, if a velocity gradient is added from 0.05- to 0.1-km depth. The results indicate that short (3-week) deployments of seismometers to record seismic noise may provide useful constraints on the VS of sedimentary basins.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221108","collaboration":"Prepared in cooperation with the University of Washington","usgsCitation":"Frankel, A., and Bodin, P., 2022, Using seismic noise correlation to determine the shallow velocity structure of the Seattle basin, Washington: U.S. Geological Survey Open-File Report 2022–1108, 13 p., https://doi.org/10.3133/ofr20221108.","productDescription":"vi, 12 p.","onlineOnly":"Y","ipdsId":"IP-140830","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501842,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114001.htm","linkFileType":{"id":5,"text":"html"}},{"id":410660,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.XML"},{"id":410656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1108/coverthb.jpg"},{"id":410657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1108/ofr20221108.pdf","text":"Report","description":"OFR 2022-1108"},{"id":410658,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221108/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1108"},{"id":410659,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1108/images"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Seattle Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45036951581977,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.51906296781365\n ],\n [\n -122.22524539503297,\n 47.693059199440825\n ],\n [\n -122.45036951581977,\n 47.693059199440825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025

","tableOfContents":"","publishedDate":"2022-12-21","noUsgsAuthors":false,"publicationDate":"2022-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":859229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodin, Paul","contributorId":104142,"corporation":false,"usgs":true,"family":"Bodin","given":"Paul","affiliations":[],"preferred":false,"id":859230,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238971,"text":"sir20225108 - 2022 - Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21","interactions":[],"lastModifiedDate":"2022-12-20T12:03:56.601438","indexId":"sir20225108","displayToPublicDate":"2022-12-19T12:06:14","publicationYear":"2022","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":"2022-5108","displayTitle":"Hydrogeologic Characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from Water-Level and Water-Chemistry Data, 2015–21","title":"Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21","docAbstract":"

Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.

Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.

Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225108/full","text":"Report"}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-12-19","noUsgsAuthors":false,"publicationDate":"2022-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859461,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}