{"pageNumber":"154","pageRowStart":"3825","pageSize":"25","recordCount":40783,"records":[{"id":70237290,"text":"70237290 - 2022 - Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","interactions":[],"lastModifiedDate":"2022-10-06T14:25:24.776873","indexId":"70237290","displayToPublicDate":"2022-10-06T09:19:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin","docAbstract":"Reliable quantitative information on sediment sources to rivers is critical to mitigate contamination and target conservation and restoration actions. However, the determination of the relative importance of sediment sources is complicated at the scale of large river basins by immense variability in erosional processes and sediment sources over space and time, heterogeneity in sediment transport and deposition, and a paucity of sediment monitoring data. Sediment source fingerprinting is an increasingly adopted field-based technique that identifies the nature and relative source contribution of sediment transported in waterways. Notably, sediment source fingerprinting provides information that is independent of other field, modeling, or remotely sensed techniques. However, the diversity in sediment fingerprinting sampling, analytical, and interpretive methods has been recognized as a problem in terms of developing standardized procedures for its application at the scale of large river basins. Accordingly, this review focuses on established sediment source fingerprinting studies conducted within the Mississippi River Basin (MRB), summarizes unique information provided by sediment source fingerprinting that is distinct from traditional monitoring techniques, evaluates consistency and reliability of methodological approaches among MRB studies, and provides prospects for the use of the sediment source fingerprinting technique as an aid to large-scale landscape conservation and restoration under current management frameworks. Most established MRB studies got creditable fingerprinting results and considered near-channel sources as the dominant sediment sources in most cases, while the comparability of their results suffers from a lack of standardization in procedural steps. Findings from MRB studies demonstrate that sediment source fingerprinting is a highly valuable and reliable sediment source assessment approach to assist land and water resource management under current management frameworks, but efforts are still needed to make this technique ready to be used in a more predominant way in large-scale landscape conservation and restoration efforts. We summarized research needs and suggested the best fingerprinting practices for management purposes with the aim of ensuring that this technique is as robust and reliable as it moves forward.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.116260","usgsCitation":"Xu, Z., Belmont, P., Brahney, J., and Gellis, A.C., 2022, Sediment source fingerprinting as an aid to large-scale landscape conservation and restoration: A review for the Mississippi River Basin: Journal of Environmental Management, v. 324, 116260, 20 p., https://doi.org/10.1016/j.jenvman.2022.116260.","productDescription":"116260, 20 p.","ipdsId":"IP-141762","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":446204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2022.116260","text":"Publisher Index Page"},{"id":408033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -94.0869140625,\n              29.57345707301757\n            ],\n            [\n              -89.7802734375,\n              28.729130483430154\n            ],\n            [\n              -89.20898437499999,\n              29.34387539941801\n            ],\n            [\n              -89.5166015625,\n              30.107117887092357\n            ],\n            [\n              -89.384765625,\n              33.65120829920497\n            ],\n            [\n              -82.8369140625,\n              34.77771580360469\n            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University","active":true,"usgs":false}],"preferred":false,"id":853997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belmont, Patrick","contributorId":275033,"corporation":false,"usgs":false,"family":"Belmont","given":"Patrick","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":853998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":853999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237272,"text":"70237272 - 2022 - Simple statistical models can be sufficient for testing hypotheses with population time series data","interactions":[],"lastModifiedDate":"2022-10-06T14:10:39.16442","indexId":"70237272","displayToPublicDate":"2022-10-06T08:52:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Simple statistical models can be sufficient for testing hypotheses with population time series data","docAbstract":"<p><span>Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9339","usgsCitation":"Wenger, S., Stowe, E.S., Gido, K.B., Freeman, M., Kanno, Y., Franssen, N.R., Olden, J., Poff, N.L., Walters, A.W., Bumpers, P.M., Mims, M.C., Hooten, M.B., and Lu, X., 2022, Simple statistical models can be sufficient for testing hypotheses with population time series data: Ecology and Evolution, v. 12, no. 9, e9339, 13 p., https://doi.org/10.1002/ece3.9339.","productDescription":"e9339, 13 p.","ipdsId":"IP-133439","costCenters":[{"id":683,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446206,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.9339","text":"External Repository"},{"id":408029,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":853925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stowe, Edward S.","contributorId":273256,"corporation":false,"usgs":false,"family":"Stowe","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gido, Keith B.","contributorId":198487,"corporation":false,"usgs":false,"family":"Gido","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":853927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Franssen, Nathan R.","contributorId":273252,"corporation":false,"usgs":false,"family":"Franssen","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olden, Julian 0000-0003-2143-1187","orcid":"https://orcid.org/0000-0003-2143-1187","contributorId":296007,"corporation":false,"usgs":false,"family":"Olden","given":"Julian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":853931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poff, N. LeRoy","contributorId":261271,"corporation":false,"usgs":false,"family":"Poff","given":"N.","email":"","middleInitial":"LeRoy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":853932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":853933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bumpers, Phillip M.","contributorId":203871,"corporation":false,"usgs":false,"family":"Bumpers","given":"Phillip","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":853934,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mims, Meryl C. 0000-0003-0570-988X","orcid":"https://orcid.org/0000-0003-0570-988X","contributorId":209951,"corporation":false,"usgs":false,"family":"Mims","given":"Meryl","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":853935,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hooten, Mevin B. 0000-0002-1614-723X","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":292295,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853936,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lu, Xinyi","contributorId":279368,"corporation":false,"usgs":false,"family":"Lu","given":"Xinyi","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":853937,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70237276,"text":"70237276 - 2022 - Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris)","interactions":[],"lastModifiedDate":"2022-10-06T13:47:45.013432","indexId":"70237276","displayToPublicDate":"2022-10-06T08:35:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Range-wide population projections for Northern Red-Bellied Cooters (<i>Pseudemys rubriventris</i>)","title":"Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris)","docAbstract":"<p>Northern Red-Bellied Cooters (<i>Pseudemys rubriventris</i>) have a disjunct distribution with a relictual population in southeastern Massachusetts and a larger range across the mid-Atlantic United States. The relictual population is currently listed with protections under the U.S. Endangered Species Act but the status of the population in the remainder of the species' range has not been assessed, and there is concern that it may be at risk of extinction without protection. The U.S. Fish and Wildlife Service requires scientific information of the species' status to inform conservation decisions. There is little empirical information available from<span>&nbsp;</span><i>P. rubriventris</i><span>&nbsp;</span>populations and, furthermore, the majority of what exists comes from the disjunct northern subpopulation. To fill data gaps in the species' life history and reduce geographic bias, we supplement available data from<span>&nbsp;</span><i>P. rubriventris</i><span>&nbsp;</span>with demographic rate estimates from other<span>&nbsp;</span><i>Pseudemys</i><span>&nbsp;</span>species to parameterize an age-structured population projection model. Our estimate of mean population growth rate was 0.987 (0.92–1.04), indicating that<span>&nbsp;</span><i>P. rubriventris</i><span>&nbsp;</span>populations may be in decline. However, there was considerable uncertainty in our results, with 35% of projections resulting in stable or increasing populations. Additional uncertainty about parameter values, geographic variation, and current threats limit the assessment. We discuss the merits and limitations of our population projection modeling (PPM) approach where other analytical methods are precluded by lack of available data.</p>","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/21-065","usgsCitation":"Fleming, J.E., Moore, J.F., Waddle, H., Martin, J., and Campbell Grant, E.H., 2022, Range-wide population projections for Northern Red-Bellied Cooters (Pseudemys rubriventris): Journal of Herpetology, v. 56, no. 3, p. 362-369, https://doi.org/10.1670/21-065.","productDescription":"8 p.","startPage":"362","endPage":"369","ipdsId":"IP-130062","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":408028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Massachusetts, New Jersey, North Carolina, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.332763671875,\n              34.551811369170494\n            ],\n            [\n              -76.607666015625,\n              34.6241677899049\n            ],\n            [\n              -76.201171875,\n              34.831841149828655\n            ],\n            [\n              -75.399169921875,\n              35.23664622093195\n            ],\n            [\n              -75.322265625,\n              35.47856499535729\n            ],\n            [\n              -75.377197265625,\n              35.84453450421662\n            ],\n            [\n              -75.83862304687499,\n              37.046408899699564\n            ],\n            [\n       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0000-0003-2570-914X","orcid":"https://orcid.org/0000-0003-2570-914X","contributorId":238931,"corporation":false,"usgs":true,"family":"Fleming","given":"Jillian","email":"","middleInitial":"Elizabeth","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":853941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waddle, Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":206866,"corporation":false,"usgs":true,"family":"Waddle","given":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":216734,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":853944,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70238817,"text":"70238817 - 2022 - Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","interactions":[],"lastModifiedDate":"2022-12-13T13:40:55.160716","indexId":"70238817","displayToPublicDate":"2022-10-06T07:10:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5538,"text":"AoB PLANTS","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation","docAbstract":"<p><span>Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m</span><sup>−2</sup><span>&nbsp;adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m</span><sup>2</sup><span>&nbsp;that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/aobpla/plac045","usgsCitation":"Applestein, C., Caughlin, T., and Germino, M., 2022, Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation: AoB PLANTS, v. 14, no. 6, plac045, 13 p., https://doi.org/10.1093/aobpla/plac045.","productDescription":"plac045, 13 p.","ipdsId":"IP-127630","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aobpla/plac045","text":"Publisher Index Page"},{"id":410359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.2526670227266,\n              45.4\n            ],\n            [\n              -117.2526670227266,\n              43.21761290801206\n            ],\n            [\n              -113.74569616023115,\n              43.21761290801206\n            ],\n            [\n              -113.74569616023115,\n              45.4\n            ],\n            [\n              -117.2526670227266,\n              45.4\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":858781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858782,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70240117,"text":"70240117 - 2022 - Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","interactions":[],"lastModifiedDate":"2023-01-27T13:09:43.480884","indexId":"70240117","displayToPublicDate":"2022-10-06T07:02:49","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":"Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb0\">Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2022.1007010","usgsCitation":"Wood, D.J., Stoy, P.C., Powell, S., and Beever, E.A., 2022, Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments: Frontiers in Ecology and Evolution, v. 10, 1007010, 16 p., https://doi.org/10.3389/fevo.2022.1007010.","productDescription":"1007010, 16 p.","ipdsId":"IP-143541","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446214,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.1007010","text":"Publisher Index Page"},{"id":435664,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z47EWL","text":"USGS data release","linkHelpText":"Model performance and output variables for phenological events across land cover types in the Northwestern Plains, 1989-2014"},{"id":412401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Nebraska, North Dakota, South Dakota, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -116.36446840224696,\n              49.041849451282246\n            ],\n            [\n              -116.36446840224696,\n              42.4957242202581\n            ],\n            [\n              -99.3648518667362,\n              42.4957242202581\n            ],\n            [\n              -99.3648518667362,\n              49.041849451282246\n            ],\n            [\n              -116.36446840224696,\n              49.041849451282246\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, David J. A. 0000-0003-4315-5160 dwood@usgs.gov","orcid":"https://orcid.org/0000-0003-4315-5160","contributorId":177588,"corporation":false,"usgs":true,"family":"Wood","given":"David","email":"dwood@usgs.gov","middleInitial":"J. A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoy, Paul C.","contributorId":204157,"corporation":false,"usgs":false,"family":"Stoy","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":862634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Scott","contributorId":192347,"corporation":false,"usgs":false,"family":"Powell","given":"Scott","affiliations":[],"preferred":false,"id":862635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":862636,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237313,"text":"70237313 - 2022 - Nonlinear multidecadal trends in organic matter dynamics in Midwest reservoirs are a function of variable hydroclimate","interactions":[],"lastModifiedDate":"2022-11-16T17:11:50.804997","indexId":"70237313","displayToPublicDate":"2022-10-06T06:38:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Nonlinear multidecadal trends in organic matter dynamics in Midwest reservoirs are a function of variable hydroclimate","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Dissolved organic matter (DOM) and particulate organic matter (POM) can influence biogeochemical processes in aquatic systems. An understanding, however, of the source, composition, and processes driving inland reservoir organic matter (OM) cycling at a regional scale over the long term is currently unexplored. Here, we quantify decadal patterns (&gt; 20 yr) of DOM quantity and composition and POM in 40 reservoirs in the midcontinent United States. We built 184 Random Forest models to identify how the relative influence of watershed characteristics and limnological parameters on OM dynamics may vary over time and in synchrony with hydroclimatic anomalies. The reservoir OM quantity and composition varied nonmonotonically through time and in contrast to lake browning observed in the northern hemisphere. Reservoir DOM composition switched from humic and aromatic during wet summers to aliphatic, potentially autochthonous DOM during particularly prolonged dry summers in the mid-2000s. The shift in reservoir DOM quantity and composition could be attributed to the change in time-varying control of watershed and limnological factors mediated by the hydroclimatic conditions. Watershed control (e.g., percent crops) was predominant during wet summers, while the effect of reservoir morphology (e.g., maximum depth) and water quality parameters (e.g., Secchi depth, chlorophyll<span>&nbsp;</span><i>a</i>) were evident during dry summers. Thus, future predictions of drier conditions may promote “greening” with negative implications for reservoir water quality and treated drinking water. Considering the nonlinear nature of reservoir OM dynamics and its controls will help to better mitigate water quality issues in these constructed systems increasingly impacted by global changes.</p></div></div>","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.12220","usgsCitation":"Bhattacharya, R., Jones, J.R., Graham, J.L., Obrecht, D., Thorpe, A., Harlan, J.D., and North, R., 2022, Nonlinear multidecadal trends in organic matter dynamics in Midwest reservoirs are a function of variable hydroclimate: Limnology and Oceanography, v. 67, no. 11, p. 2531-2546, https://doi.org/10.1002/lno.12220.","productDescription":"16 p.","startPage":"2531","endPage":"2546","ipdsId":"IP-107792","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":467158,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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,{"id":70237558,"text":"70237558 - 2022 - Multispecies approaches to status assessments in support of endangered species classifications","interactions":[],"lastModifiedDate":"2022-11-16T17:13:33.687452","indexId":"70237558","displayToPublicDate":"2022-10-05T11:53:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Multispecies approaches to status assessments in support of endangered species classifications","docAbstract":"<p><span>Multispecies risk assessments have developed within many international conservation programs, reflecting a widespread need for efficiency. Under the United States Endangered Species Act (ESA), multispecies assessments ultimately lead to species-level listing decisions. Although this approach provides opportunities for improved efficiency, it also risks overwhelming or biasing the assessment process and would benefit from clear guidance for practitioners. We reviewed multispecies assessments conducted between 1993 and 2019 for ESA listing decisions to identify the ecological basis for combining species, the assessment approach used, and the policy factors influencing their efficacy. We identified 42 cases covering 359 species. Most assessments (81%) included two to five species, although the maximum was 82. A common theme involved grouping narrow endemics or habitat specialists based on taxonomic relatedness, similar distributions, and common threats to persistence. All assessments included a combined threats analysis, but few employed a common species' response model or expert elicitation process. Although ESA risk assessments are distinct from policy decisions, most assessments (50%) supported decisions that all species warranted endangered status. Available guidance has generally emphasized ecological similarity as the key attribute leading to successful multispecies assessments. The challenge with consistently selecting species based on qualitative proxies such as common distributions or threats to persistence is that ecological patterns and processes are scale dependent. Focusing instead on the assessment methods and their potential for bias and increased efficiency may provide a stronger basis for developing consistent and transparent guidance.</span></p>","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12825","usgsCitation":"Fitzgerald, D.B., Freeman, M., Maloney, K.O., Young, J.A., Rosenberger, A.E., Kazyak, D., and Smith, D.R., 2022, Multispecies approaches to status assessments in support of endangered species classifications: Conservation Science and Practice, v. 4, no. 11, e12825, 11 p., https://doi.org/10.1111/csp2.12825.","productDescription":"e12825, 11 p.","ipdsId":"IP-127956","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446217,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12825","text":"Publisher Index Page"},{"id":408261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzgerald, Daniel Bruce 0000-0002-3254-7428","orcid":"https://orcid.org/0000-0002-3254-7428","contributorId":245718,"corporation":false,"usgs":true,"family":"Fitzgerald","given":"Daniel","email":"","middleInitial":"Bruce","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854460,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70263564,"text":"70263564 - 2022 - Creep rate models for the 2023 US National Seismic Hazard Model: Physically constrained inversions for the distribution of creep on California faults","interactions":[],"lastModifiedDate":"2025-02-13T17:17:51.912547","indexId":"70263564","displayToPublicDate":"2022-10-05T11:16:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Creep rate models for the 2023 US National Seismic Hazard Model: Physically constrained inversions for the distribution of creep on California faults","docAbstract":"<p><span>Widespread surface creep is observed across a number of active faults included in the United States (US) National Seismic Hazard Model (NSHM). In northern California, creep occurs on the central section of the San Andreas fault, along the Hayward and Calaveras faults through the San Francisco Bay Area, and to the north coast region along the Maacama and Bartlett Springs faults. In southern California, creep is observed across the Coachella segment of the San Andreas fault, through the Brawley Seismic Zone, and along the Imperial and Superstition Hills faults. Seismic hazard assessments for California have accounted for creep using various data and methods, including the most recent Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3) in 2013. The purpose of this study is to expand and update the UCERF3 creep rate data set for the 2023 release of the US NSHM and to invert geodetic data and the surface creep rate data for the spatial distribution of interseismic fault creep on California faults using an elastic model with physical creep constraints. The updated surface creep rate compilation consists of a variety of data types including alignment arrays, offset cultural markers, creepmeters, Interferometric Synthetic Aperture Radar, and Global Positioning System data. We compile a total of 497 surface creep rate measurements, 400 of which are new and 97 of which appear in the UCERF3 compilation. We compute creep rate distributions for each of the five 2023 NSHM geodetic‐based and geologic‐based deformation models. Computed creep rates are used to reduce the total fault moment rate available for earthquake sequences in the NSHM model. We find that, despite relatively large variability in model long‐term slip rates across all five deformation models, the variability in depth‐averaged creep rate across all models is relatively small, typically 5–10&nbsp;mm/yr along the creeping San Andreas fault section and only 2–4&nbsp;mm/yr along the Maacama and Rodgers Creek‐Hayward faults.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/ 0220220186","usgsCitation":"Johnson, K., Murray, J.R., and Wespestad, C., 2022, Creep rate models for the 2023 US National Seismic Hazard Model: Physically constrained inversions for the distribution of creep on California faults: Seismological Research Letters, v. 93, no. 6, p. 3151-3169, https://doi.org/10.1785/ 0220220186.","productDescription":"19 p.","startPage":"3151","endPage":"3169","ipdsId":"IP-142160","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, K. M.","contributorId":350935,"corporation":false,"usgs":false,"family":"Johnson","given":"K. M.","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":927345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wespestad, Crystal","contributorId":296055,"corporation":false,"usgs":false,"family":"Wespestad","given":"Crystal","email":"","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":927347,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70237730,"text":"70237730 - 2022 - Exposure to 17α-ethinylestradiol results in differential susceptibility of largemouth bass (Micropterus salmoides) to bacterial infection","interactions":[],"lastModifiedDate":"2022-10-21T14:37:13.503204","indexId":"70237730","displayToPublicDate":"2022-10-05T09:34:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Exposure to 17α-ethinylestradiol results in differential susceptibility of largemouth bass (Micropterus salmoides) to bacterial infection","docAbstract":"<p><span>Disease outbreaks, skin lesions, mortality events, and reproductive abnormalities have been observed in wild populations of centrarchids. The presence of estrogenic endocrine disrupting compounds (EEDCs) has been implicated as a potential causal factor for these effects. The effects of prior EEDC exposure on immune response were examined in juvenile largemouth bass (</span><i>Micropterus salmoides</i><span>) exposed to a potent synthetic estrogen (17α-ethinylestradiol, EE2) at a low (EE2</span><sub>Low</sub><span>, 0.87 ng/L) or high (EE2</span><sub>High</sub><span>, 9.08 ng/L) dose for 4 weeks, followed by transfer to clean water and injection with an LD</span><sub>40</sub><span>&nbsp;dose of the Gram-negative bacteria&nbsp;</span><i>Edwardsiella piscicida</i><span>. Unexpectedly, this prior exposure to EE2</span><sub>High</sub><span>&nbsp;significantly increased survivorship at 10 d post-infection compared to solvent control or EE2</span><sub>Low</sub><span>-exposed, infected fish. Both prior exposure and infection with&nbsp;</span><i>E. piscicida</i><span>&nbsp;led to significantly reduced hepatic glycogen levels, indicating a stress response resulting in depletion of energy stores. Additionally, pathway analysis for liver and spleen indicated differentially expressed genes associated with immunometabolic processes in the mock-injected EE2</span><sub>High</sub><span>&nbsp;treatment that could underlie the observed protective effect and metabolic shift in EE2</span><sub>High</sub><span>-infected fish. Our results demonstrate that exposure to a model EEDC alters metabolism and immune function in a fish species that is ecologically and economically important in North America.</span></p>","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.2c02250","usgsCitation":"Leet, J.K., Greer, J., Richter, C.A., Iwanowicz, L., Spinard, E., McDonald, J., Conway, C.M., Gale, R.W., Tillitt, D.E., and Hansen, J.D., 2022, Exposure to 17α-ethinylestradiol results in differential susceptibility of largemouth bass (Micropterus salmoides) to bacterial infection: Environmental Science and Technology, v. 56, no. 20, p. 14375-14386, https://doi.org/10.1021/acs.est.2c02250.","productDescription":"12 p.","startPage":"14375","endPage":"14386","ipdsId":"IP-139357","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":446225,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.2c02250","text":"Publisher Index Page"},{"id":435665,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93OHUUS","text":"USGS data release","linkHelpText":"Physiological and molecular endpoints observed in juvenile largemouth bass in response to an estrogen (17&amp;amp;amp;amp;alpha;-ethinylestradiol) and subsequently a bacterial challenge (Edwardsiella piscicida) exposure under laboratory conditions."},{"id":408607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"20","noUsgsAuthors":false,"publicationDate":"2022-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Leet, Jessica Kristin 0000-0001-8142-6043","orcid":"https://orcid.org/0000-0001-8142-6043","contributorId":225505,"corporation":false,"usgs":true,"family":"Leet","given":"Jessica","email":"","middleInitial":"Kristin","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greer, Justin","contributorId":298316,"corporation":false,"usgs":false,"family":"Greer","given":"Justin","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":855379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":855381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spinard, Edward","contributorId":298319,"corporation":false,"usgs":false,"family":"Spinard","given":"Edward","email":"","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":855382,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Jacquelyn","contributorId":298321,"corporation":false,"usgs":false,"family":"McDonald","given":"Jacquelyn","email":"","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":855383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conway, Carla M. 0000-0002-3851-3616 cmconway@usgs.gov","orcid":"https://orcid.org/0000-0002-3851-3616","contributorId":2946,"corporation":false,"usgs":true,"family":"Conway","given":"Carla","email":"cmconway@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":855384,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gale, Robert W. 0000-0002-8533-141X rgale@usgs.gov","orcid":"https://orcid.org/0000-0002-8533-141X","contributorId":2808,"corporation":false,"usgs":true,"family":"Gale","given":"Robert","email":"rgale@usgs.gov","middleInitial":"W.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855385,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":855386,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hansen, John D. 0000-0002-3006-2734","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":220725,"corporation":false,"usgs":true,"family":"Hansen","given":"John","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":855387,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70237281,"text":"70237281 - 2022 - Evolutionary dynamics inform management interventions of a hanging garden obligate, Carex specuicola","interactions":[],"lastModifiedDate":"2022-10-06T14:37:55.932771","indexId":"70237281","displayToPublicDate":"2022-10-05T09:31:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evolutionary dynamics inform management interventions of a hanging garden obligate, <i>Carex specuicola</i>","title":"Evolutionary dynamics inform management interventions of a hanging garden obligate, Carex specuicola","docAbstract":"<p><span>Uncovering the historical and contemporary processes shaping rare species with complex distributions is of growing importance due to threats such as habitat destruction and climate change. Species restricted to specialized, patchy habitat may persist by virtue of life history characteristics facilitating ongoing gene flow and dispersal, but they could also reflect the remnants of formerly widespread, suitable habitat that existed during past climate regimes. If formerly widespread species did not rely upon traits facilitating high dispersibility to persist, contemporary populations could be at high risk of extirpation or extinction. Fortunately, genomic investigations provide an opportunity to illuminate such alternative scenarios while simultaneously offering guidance for future management interventions. Herein, we test the role of these mechanisms in shaping patterns of genomic diversity and differentiation across a highly restricted and rare ecosystem: desert hanging gardens. We focus on&nbsp;</span><i>Carex specuicola</i><span>&nbsp;(Cyperaceae), a hanging garden obligate narrowly distributed in the Four Corners region of the southwestern United States that is listed as Threatened under the United States Endangered Species Act. Population structure and diversity analyses reveal that hanging garden populations are shaped by strong genetic drift, but that individuals in gardens are occasionally more closely related to individuals at other gardens than to individuals within the same garden. Similarly, gardens separated by long geographic distances may contain individuals that are more closely related compared to individuals in gardens separated by short geographic distances. Demographic modeling supports historical gene flow between some contemporary garden pairs, which is corroborated by low estimates of inbreeding coefficients and recent divergence times. As such, multiple lines of evidence support dispersal and gene flow across&nbsp;</span><i>C. specuicola</i><span>&nbsp;populations at both small and large spatial scales, indicating that even if&nbsp;</span><i>C. specuicola</i><span>&nbsp;was formerly more widespread, it may be well suited to persist in hanging gardens so long as suitable habitat remains available. Analyses like those demonstrated herein may be broadly applicable for understanding the short- and long-term evolutionary processes influencing rare species, and especially those having complex distributions across heterogeneous landscapes.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2022.941002","usgsCitation":"Chapin, K.J., Jones, M.R., Winkler, D.E., Rink, G., and Massatti, R., 2022, Evolutionary dynamics inform management interventions of a hanging garden obligate, Carex specuicola: Frontiers in Conservation Science, v. 3, 941002, 15 p., https://doi.org/10.3389/fcosc.2022.941002.","productDescription":"941002, 15 p.","ipdsId":"IP-141134","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446227,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2022.941002","text":"Publisher Index Page"},{"id":435666,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLZ1XD","text":"USGS data release","linkHelpText":"Carex specuicola genomic data for the southern Colorado Plateau Desert"},{"id":408036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"southern Colorado Plateau Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.478515625,\n              35.88905007936091\n            ],\n            [\n              -109.072265625,\n              35.88905007936091\n            ],\n            [\n              -109.072265625,\n              37.75334401310656\n            ],\n            [\n              -110.478515625,\n              37.75334401310656\n            ],\n            [\n              -110.478515625,\n              35.88905007936091\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2022-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Chapin, Kenneth James 0000-0002-8382-4050","orcid":"https://orcid.org/0000-0002-8382-4050","contributorId":297377,"corporation":false,"usgs":true,"family":"Chapin","given":"Kenneth","email":"","middleInitial":"James","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Matthew R","contributorId":297378,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":64389,"text":"formerly: USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rink, Glenn","contributorId":297379,"corporation":false,"usgs":false,"family":"Rink","given":"Glenn","affiliations":[{"id":64390,"text":"Deaver Herbarium, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853973,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70237282,"text":"70237282 - 2022 - A fault‐based crustal deformation model with deep driven dislocation sources for the 2023 update to the U.S. National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2022-10-31T14:50:23.413345","indexId":"70237282","displayToPublicDate":"2022-10-05T09:12:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"A fault‐based crustal deformation model with deep driven dislocation sources for the 2023 update to the U.S. National Seismic Hazard Model","docAbstract":"<p><span>A fault‐based crustal deformation model with deep driven dislocation sources is applied to estimate long‐term on‐fault slip rates and off‐fault moment rate distribution in the western United States (WUS) for the 2023 update to the National Seismic Hazard Model (NSHM). This model uses the method of&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf37\">Zeng and Shen (2017)</a><span>&nbsp;to invert for slip rate and strain‐rate parameters based on inputs from Global Positioning System (GPS) velocities and geologic slip‐rate constraints. The model connects adjacent major fault segments in California and the Cascadia subduction zone to form blocks that extend to the boundaries of the study area. Faults within the blocks are obtained from the NSHM geologic fault section database. The model slip rates are determined using a least‐squares inversion with a normalized chi‐square of 6.6. I also apply a time‐dependent correction called “ghost transient” effect to account for the viscoelastic responses from large historic earthquakes along the San Andreas fault and Cascadia subduction zone. Major discrepancies between model slip rates and geologic slip rates along the San Andreas fault, for example, from the Cholame to the Mojave and San Bernardino segments of the San Andreas, are well reduced after the ghost transient correction is applied to GPS velocities. The off‐fault moment rate distribution is consistent with regional tectonics and seismicity patterns with a total rate of&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mn xmlns=&quot;&quot;>1.6</mn><mo xmlns=&quot;&quot;>&amp;#xD7;</mo><msup xmlns=&quot;&quot;><mn>10</mn><mn>19</mn></msup><mtext xmlns=&quot;&quot;>&amp;#x2009;&amp;#x2009;</mtext><mi xmlns=&quot;&quot; mathvariant=&quot;normal&quot;>N</mi><mo xmlns=&quot;&quot;>&amp;#xB7;</mo><mi xmlns=&quot;&quot; mathvariant=&quot;normal&quot;>m</mi><mo xmlns=&quot;&quot;>/</mo><mi xmlns=&quot;&quot;>yr</mi></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mn\">1.6</span><span id=\"MathJax-Span-4\" class=\"mo\">×</span><span id=\"MathJax-Span-5\" class=\"msup\"><span id=\"MathJax-Span-6\" class=\"mn\">10</span><sup><span id=\"MathJax-Span-7\" class=\"mn\">19</span></sup></span><span id=\"MathJax-Span-8\" class=\"mtext\"><sup> </sup> </span><span id=\"MathJax-Span-9\" class=\"mi\">N</span><span id=\"MathJax-Span-10\" class=\"mo\">⋅</span><span id=\"MathJax-Span-11\" class=\"mi\">m</span><span id=\"MathJax-Span-12\" class=\"mo\">/</span><span id=\"MathJax-Span-13\" class=\"mi\">yr</span></span></span></span></span></span><span>&nbsp;for the WUS.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220209","usgsCitation":"Zeng, Y., 2022, A fault‐based crustal deformation model with deep driven dislocation sources for the 2023 update to the U.S. National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3170-3185, https://doi.org/10.1785/0220220209.","productDescription":"16 p.","startPage":"3170","endPage":"3185","ipdsId":"IP-142327","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":408032,"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        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -126.7822265625,\n              31.80289258670676\n            ],\n            [\n              -105,\n              31.80289258670676\n            ],\n            [\n              -105,\n              48.922499263758255\n            ],\n            [\n              -126.7822265625,\n              48.922499263758255\n            ],\n            [\n              -126.7822265625,\n              31.80289258670676\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":853974,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70238634,"text":"70238634 - 2022 - Regional models do not outperform continental models for invasive species","interactions":[],"lastModifiedDate":"2022-12-02T13:01:29.078063","indexId":"70238634","displayToPublicDate":"2022-10-04T07:00:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Regional models do not outperform continental models for invasive species","docAbstract":"<p data-obkms-id=\"3937B3B8-2189-42EC-BC04-BAD8BB131901\"><strong>Aim</strong>: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.</p><p data-obkms-id=\"31E9AFA9-0FFF-478C-BFCD-6E4F6737E347\"><strong>Location</strong>: Continental United States.</p><p data-obkms-id=\"162A30EF-445B-4BF1-A640-95383BD90C51\"><strong>Methods</strong>: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.</p><p data-obkms-id=\"14DC1F50-A2B4-42AB-B496-6708B6458947\"><strong>Results</strong>: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.</p><p data-obkms-id=\"D2827041-F6B2-4DE9-B722-9639396FE56D\"><strong>Main conclusions</strong>: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.</p>","language":"English","publisher":"NeoBiota","doi":"10.3897/neobiota.77.86364","usgsCitation":"Jarnevich, C.S., Sofaer, H., Engelstad, P., and Belamaric, P., 2022, Regional models do not outperform continental models for invasive species: NeoBiota, v. 77, https://doi.org/10.3897/neobiota.77.86364.","productDescription":"22 p.","startPage":"1-22","ipdsId":"IP-137001","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446233,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.77.86364","text":"Publisher Index Page"},{"id":435667,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AL0PN","text":"USGS data release","linkHelpText":"Data to create and evaluate distribution models for invasive species for different geographic extents"},{"id":409981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","noUsgsAuthors":false,"publicationDate":"2022-10-04","publicationStatus":"PW","contributors":{"authors":[{"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":858156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":858157,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":858158,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belamaric, Pairsa 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":299593,"corporation":false,"usgs":false,"family":"Belamaric","given":"Pairsa","affiliations":[{"id":64897,"text":"Student Contractor to the USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":858159,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237881,"text":"70237881 - 2022 - Hurdles to developing quantitative decision support for Endangered Species Act resource allocation","interactions":[],"lastModifiedDate":"2022-10-31T12:01:40.279728","indexId":"70237881","displayToPublicDate":"2022-10-04T06:58:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"title":"Hurdles to developing quantitative decision support for Endangered Species Act resource allocation","docAbstract":"<div class=\"JournalAbstract\"><p>The U.S. Fish and Wildlife Service oversees the recovery of many species protected by the U.S. Endangered Species Act (ESA). Recent research suggests that a structured approach to allocating conservation resources could increase recovery outcomes for ESA listed species. Quantitative approaches to decision support can efficiently allocate limited financial resources and maximize desired outcomes. Yet, developing quantitative decision support under real-world constraints is challenging. Approaches that pair research teams and end-users are generally the most effective. However, co-development requires overcoming “hurdles” that can arise because of differences in the mental models of the co-development team. These include perceptions that: (1) scarce funds should be spent on action, not decision support; (2) quantitative approaches are only useful for simple decisions; (3) quantitative tools are inflexible and prescriptive black boxes; (4) available data are not good enough to support decisions; and (5) prioritization means admitting defeat. Here, we describe how we addressed these misperceptions during the development of a prototype resource allocation decision support tool for understanding trade-offs in U.S. endangered species recovery. We describe how acknowledging these hurdles and identifying solutions enabled us to progress with development. We believe that our experience can assist other applications of developing quantitative decision support for resource allocation.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fcosc.2022.1002804","usgsCitation":"Iacona, G.D., Avery-Gomm, S., Maloney, R.F., Brazill-Boast, J., Crouse, D.T., Drew, C., Epanchin-Niell, R.S., Hall, S.B., Maguire, L.A., Male, T., Newman, J., Possingham, H.P., Rumpff, L., Runge, M.C., Weiss, K.C., Wilson, R.S., Zablan, M.A., and Gerber, L.R., 2022, Hurdles to developing quantitative decision support for Endangered Species Act resource allocation: Frontiers in Conservation Science, v. 3, 1002804, 9 p., https://doi.org/10.3389/fcosc.2022.1002804.","productDescription":"1002804, 9 p.","ipdsId":"IP-114585","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":446236,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2022.1002804","text":"Publisher Index Page"},{"id":408877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2022-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Iacona, Gwenllian D.","contributorId":213094,"corporation":false,"usgs":false,"family":"Iacona","given":"Gwenllian","email":"","middleInitial":"D.","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":856069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avery-Gomm, Stephanie","contributorId":213093,"corporation":false,"usgs":false,"family":"Avery-Gomm","given":"Stephanie","email":"","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":856070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Richard F.","contributorId":213091,"corporation":false,"usgs":false,"family":"Maloney","given":"Richard","email":"","middleInitial":"F.","affiliations":[{"id":38703,"text":"New Zealand Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":856071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brazill-Boast, James","contributorId":213095,"corporation":false,"usgs":false,"family":"Brazill-Boast","given":"James","email":"","affiliations":[{"id":38705,"text":"New South Wales Office of Environment and Heritage","active":true,"usgs":false}],"preferred":false,"id":856072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crouse, Deborah T.","contributorId":173709,"corporation":false,"usgs":false,"family":"Crouse","given":"Deborah","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":856073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drew, C Ashton","contributorId":298628,"corporation":false,"usgs":false,"family":"Drew","given":"C Ashton","affiliations":[{"id":64631,"text":"KDV Decision Analysis LLC","active":true,"usgs":false}],"preferred":false,"id":856074,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Epanchin-Niell, Rebecca S.","contributorId":175364,"corporation":false,"usgs":false,"family":"Epanchin-Niell","given":"Rebecca","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":856075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Sarah B.","contributorId":213157,"corporation":false,"usgs":false,"family":"Hall","given":"Sarah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":856076,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Maguire, Lynn A.","contributorId":213097,"corporation":false,"usgs":false,"family":"Maguire","given":"Lynn","email":"","middleInitial":"A.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":856077,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Male, Tim","contributorId":213158,"corporation":false,"usgs":false,"family":"Male","given":"Tim","email":"","affiliations":[],"preferred":false,"id":856078,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Newman, Jeff","contributorId":213099,"corporation":false,"usgs":false,"family":"Newman","given":"Jeff","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":856079,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Possingham, Hugh P.","contributorId":20882,"corporation":false,"usgs":false,"family":"Possingham","given":"Hugh","email":"","middleInitial":"P.","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":856080,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rumpff, Libby","contributorId":197117,"corporation":false,"usgs":false,"family":"Rumpff","given":"Libby","email":"","affiliations":[],"preferred":false,"id":856081,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":856082,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Weiss, Katherine C B","contributorId":298629,"corporation":false,"usgs":false,"family":"Weiss","given":"Katherine","email":"","middleInitial":"C B","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":856083,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wilson, Robyn S.","contributorId":175362,"corporation":false,"usgs":false,"family":"Wilson","given":"Robyn","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":856084,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zablan, Marilet A.","contributorId":175046,"corporation":false,"usgs":false,"family":"Zablan","given":"Marilet","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":856085,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Gerber, Leah R.","contributorId":147236,"corporation":false,"usgs":false,"family":"Gerber","given":"Leah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":856086,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70237184,"text":"70237184 - 2022 - Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","interactions":[],"lastModifiedDate":"2022-10-04T11:59:18.208422","indexId":"70237184","displayToPublicDate":"2022-10-03T06:55:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","docAbstract":"<p id=\"abspara0010\">We combined data from geomorphologic surveys, glacial modelling, and<span>&nbsp;</span><sup>10</sup><span>Be exposure ages of boulders on&nbsp;moraines, to investigate the&nbsp;Last Glacial Maximum&nbsp;(LGM) and the early retreat glacial phases in the Stura Valley of the Maritime Alps. We used the exposure ages to reconstruct the timing of standstills or readvances which interrupted the post-LGM withdrawal, initiated ∼24 ka. We mapped and dated the frontal moraines of a first glacial standstill/readvance at a short distance (∼7&nbsp;km) from the maximum external limit of the LGM, which occurred at ∼22 ka, and a second one at ∼19 ka (Bühl stadial). This morpho-chronologic succession is congruent with that obtained in the adjacent Gesso Valley and, combined with the similarity of Equilibrium Line Altitude values, demonstrates a consistent glacial response in the Maritime Alps to&nbsp;climatic forcing.</span></p><p id=\"abspara0015\">Our data are chronologically consistent with those of the southern flank of the European Alps, stressing not only a general synchroneity of the LGM across the various sectors, but also that of a LGM recessional standstill or readvance at ∼22 ka. The short distance between the LGM moraines and the recessionary phase moraines, and minimal difference in ELA indicate a modest variation in the mass balance of the Maritime Alps glaciers during this time interval. A similar modest variation between LGM and the first recessional phase<span>&nbsp;</span>glacier mass balance<span>&nbsp;</span>is also found throughout the western sector of the Southern Alps but is considerably more pronounced for the glaciers of the central-eastern sectors. This behaviour can be explained by the interplay between the moisture supplied by southern currents sourced in the Western Mediterranean and that advected by the westerlies sourced in the North Atlantic, which affected the various sectors of the Southern Alps differently.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107770","usgsCitation":"Ribolini, A., Spagnolo, M., Cyr, A.J., and Federici, P.R., 2022, Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps: Quaternary Science Reviews, v. 295, 107770, 17 p., https://doi.org/10.1016/j.quascirev.2022.107770.","productDescription":"107770, 17 p.","ipdsId":"IP-140334","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446241,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107770","text":"Publisher Index Page"},{"id":435668,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HCE4EC","text":"USGS data release","linkHelpText":"Data release for cosmogenic beryllium-10 exposure ages of moraine boulders in the Stura Valley, Maritime Alps, northwestern Italy"},{"id":407853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy, France","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              7.305908203125,\n              43.97700467496408\n            ],\n            [\n              7.80029296875,\n              43.97700467496408\n            ],\n            [\n              7.80029296875,\n              44.32384807250689\n            ],\n            [\n              7.305908203125,\n              44.32384807250689\n            ],\n            [\n              7.305908203125,\n              43.97700467496408\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ribolini, Adriano 0000-0001-5851-8775","orcid":"https://orcid.org/0000-0001-5851-8775","contributorId":291770,"corporation":false,"usgs":false,"family":"Ribolini","given":"Adriano","email":"","affiliations":[{"id":62747,"text":"Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spagnolo, Matteo 0000-0002-2753-338X","orcid":"https://orcid.org/0000-0002-2753-338X","contributorId":291771,"corporation":false,"usgs":false,"family":"Spagnolo","given":"Matteo","email":"","affiliations":[{"id":62748,"text":"Department of Geography & Environment, University of Aberdeen, UK","active":true,"usgs":false}],"preferred":false,"id":853587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cyr, Andrew J. 0000-0003-2293-5395 acyr@usgs.gov","orcid":"https://orcid.org/0000-0003-2293-5395","contributorId":3539,"corporation":false,"usgs":true,"family":"Cyr","given":"Andrew","email":"acyr@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":853588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Federici, Paolo Roberto","contributorId":297167,"corporation":false,"usgs":false,"family":"Federici","given":"Paolo","email":"","middleInitial":"Roberto","affiliations":[{"id":64311,"text":"retired, Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853589,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237116,"text":"ofr20221079 - 2022 - Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15","interactions":[],"lastModifiedDate":"2026-03-30T20:33:32.538431","indexId":"ofr20221079","displayToPublicDate":"2022-10-03T06:40:00","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":"2022-1079","displayTitle":"Evaluation of the Bushy Park Reservoir Three-Dimensional Hydrodynamic and Water-Quality Model, South Carolina, 2012–15","title":"Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15","docAbstract":"<p>The Bushy Park Reservoir is a relatively shallow impoundment in southeastern South Carolina. The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.</p><p>The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.</p><p>The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.</p><p>Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.</p><p>For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.</p><p>For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll <i>a</i>. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll <i>a</i>, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.</p><p>With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll <i>a</i> model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.</p><p>Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.</p><p>Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.39794921875,\n              32.676372772089806\n            ],\n            [\n              -79.661865234375,\n              32.676372772089806\n            ],\n            [\n              -79.661865234375,\n              33.458942753687616\n            ],\n            [\n              -80.39794921875,\n              33.458942753687616\n            ],\n            [\n              -80.39794921875,\n              32.676372772089806\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/sa-water\" data-mce-href=\"https://www.usgs.gov/centers/sa-water\">South Atlantic Water Science Center</a><br>U.S. Geological Survey<br>720 Gracern Road, Suite 129<br>Columbia, SC 29210</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Hydrodynamic Model Calibration</li><li>Potential Modifications and Considerations for Model Improvements</li><li>Reservoir Operation Scenarios</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-10-03","noUsgsAuthors":false,"publicationDate":"2022-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akasapu-Smith, Madhu","contributorId":297121,"corporation":false,"usgs":false,"family":"Akasapu-Smith","given":"Madhu","email":"","affiliations":[{"id":16286,"text":"Tetra Tech","active":true,"usgs":false}],"preferred":false,"id":853379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petkewich, Matthew D. 0000-0002-5749-6356 mdpetkew@usgs.gov","orcid":"https://orcid.org/0000-0002-5749-6356","contributorId":982,"corporation":false,"usgs":true,"family":"Petkewich","given":"Matthew","email":"mdpetkew@usgs.gov","middleInitial":"D.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrads, Paul A. 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":198982,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853381,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237254,"text":"70237254 - 2022 - Barrier islands influence the assimilation of terrestrial energy in nearshore fishes","interactions":[],"lastModifiedDate":"2023-03-24T16:26:50.745968","indexId":"70237254","displayToPublicDate":"2022-10-03T06:35:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12614,"text":"Estuarine, Costal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Barrier islands influence the assimilation of terrestrial energy in nearshore fishes","docAbstract":"<div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">We examined the relative importance of landscape features on estuarine fish trophic structure and dependence on terrestrial organic matter (OM<sub>terr</sub>) in four barrier island lagoon systems along the Alaskan Beaufort Sea coast. Our study compared two relatively large lagoon systems characterized by high river discharge and relatively free ocean water exchanges (central region near Prudhoe Bay, Alaska) with two highly protected lagoons characterized by low river discharge and limited exchange with ocean waters (eastern region near Kaktovik, Alaska). We hypothesized that freshwater discharge would be a strong determinant of food web structure for both resident marine and diadromous fishes if more discharge increases availability of OM<sub>terr</sub><span>&nbsp;</span>relative to lagoons with limited or no river inputs. To consider differences in trophic characteristics in fishes between study regions, we estimated community-wide measures of trophic structure (hereafter, community metrics) and the relative use of OM<sub>terr</sub><span>&nbsp;</span>from mixing models using stable isotope composition (δ<sup>13</sup>C and δ<sup>15</sup>N; muscle tissue) among 12 species and identified the influences of region and body size. Fish captured in lagoons well protected by barrier islands had more distinct and diverse isotopic niches relative to those in more exposed lagoons based on community metrics. The use of OM<sub>terr</sub><span>&nbsp;</span>by nearshore fishes in both regions was substantial and was &gt;50% for diadromous species. Between regions, OM<sub>terr</sub><span>&nbsp;</span>use differed in 6 of the 8 species considered but was not consistently higher in one region. The relative importance of OM<sub>terr</sub><span>&nbsp;</span>varied with fish size in 7 of 10 species considered, with more OM<sub>terr</sub><span>&nbsp;</span>used by smaller individuals. This work highlights the importance of OM<sub>terr</sub><span>&nbsp;</span>to Arctic fishes and fisheries, some of which are of subsistence importance, even when feeding grounds are primarily marine. We propose that landscape features, particularly barrier islands, play an important role in structuring nearshore food webs. Barrier islands may provide a previously undocumented ecosystem service of increasing food web complexity, which may promote system resilience.</p></div></div><div id=\"abs0015\" class=\"abstract graphical\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2022.108094","usgsCitation":"Stanek, A.E., von Biela, V.R., Laske, S.M., Taylor, R.L., and Dunton, K., 2022, Barrier islands influence the assimilation of terrestrial energy in nearshore fishes: Estuarine, Costal and Shelf Science, v. 278, 108094, 12 p., https://doi.org/10.1016/j.ecss.2022.108094.","productDescription":"108094, 12 p.","ipdsId":"IP-137604","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":446244,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2022.108094","text":"Publisher Index Page"},{"id":435669,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DAFMJD","text":"USGS data release","linkHelpText":"Nearshore Fish Isotope Values, Beaufort Sea, Alaska, 2017-2019"},{"id":407949,"rank":1,"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        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.21484375,\n              68.75231494434473\n            ],\n            [\n              -141.1083984375,\n              68.75231494434473\n            ],\n            [\n              -141.1083984375,\n              71.91088787611527\n            ],\n            [\n              -155.21484375,\n              71.91088787611527\n            ],\n            [\n              -155.21484375,\n              68.75231494434473\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"278","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanek, Ashley E. 0000-0001-5184-2126","orcid":"https://orcid.org/0000-0001-5184-2126","contributorId":290682,"corporation":false,"usgs":true,"family":"Stanek","given":"Ashley","email":"","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":853856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":853857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Laske, Sarah M. 0000-0002-6096-0420 slaske@usgs.gov","orcid":"https://orcid.org/0000-0002-6096-0420","contributorId":204872,"corporation":false,"usgs":true,"family":"Laske","given":"Sarah","email":"slaske@usgs.gov","middleInitial":"M.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":853858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":853859,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunton, Kenneth H.","contributorId":171775,"corporation":false,"usgs":false,"family":"Dunton","given":"Kenneth H.","affiliations":[],"preferred":false,"id":853860,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70247393,"text":"70247393 - 2022 - Waveform signatures of earthquakes located close to the subducted Gorda Plate interface","interactions":[],"lastModifiedDate":"2024-09-16T10:52:41.577713","indexId":"70247393","displayToPublicDate":"2022-10-03T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Waveform signatures of earthquakes located close to the subducted Gorda Plate interface","docAbstract":"<p><span>Complex seismic velocity structure near the earthquake source can affect rupture dynamics and strongly modify the seismic waveforms recorded near the fault. Fault‐zone waves are commonly observed in continental crustal settings but are less clear in subduction zones due to the spatial separation between seismic stations and the plate boundary fault. We observed anomalously long duration&nbsp;</span><i>S</i><span>&nbsp;waves from earthquake clusters located near the interface of the subducted Gorda plate north of the Mendocino triple junction. In contrast, earthquakes located just a few kilometers below each cluster show impulsive&nbsp;</span><i>S</i><span> waves. A nodal array experiment was conducted around the Northern California Seismic Network station KCT for two months to investigate the origin of the complex </span><i>S</i><span>&nbsp;waves. Beamforming analysis shows that the&nbsp;</span><i>S</i><span>&nbsp;waves contain three arrivals that have different horizontal slownesses, which we term&nbsp;</span><i>S</i><span>1,&nbsp;</span><i>S</i><span>2, and&nbsp;</span><i>S</i><span>&nbsp;coda. Similar analysis on&nbsp;</span><i>P</i><span>&nbsp;waves also show two arrivals with different horizontal slownesses, which we term&nbsp;</span><i>P</i><span>1 and&nbsp;</span><i>P</i><span>2.&nbsp;</span><i>P</i><span>1 and&nbsp;</span><i>S</i><span>1 have larger horizontal slowness than&nbsp;</span><i>P</i><span>2 and&nbsp;</span><i>S</i><span>2, respectively, indicating that the phase pairs are body waves with different ray paths. Building upon a seismic refraction profile, we construct 1D velocity models and test different thicknesses and </span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"msub\"><i><span id=\"MathJax-Span-4\" class=\"mi\">V</span></i><sub><span id=\"MathJax-Span-5\" class=\"mi\">P</span></sub></span><span id=\"MathJax-Span-6\" class=\"mo\">/</span><span id=\"MathJax-Span-7\" class=\"msub\"><i><span id=\"MathJax-Span-8\" class=\"mi\">V</span></i><sub><span id=\"MathJax-Span-9\" class=\"mi\">S</span></sub></span></span></span></span></span></span><span>&nbsp;ratios for the subducted oceanic crust. The arrival times and relative slownesses of&nbsp;</span><i>P</i><span>1/</span><i>P</i><span>2 and&nbsp;</span><i>S</i><span>1/</span><i>S</i><span>2 phases indicate that they are the direct and the Moho reflected phases, respectively. Their properties are consistent with a crustal thickness of ∼6 km and a moderate&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\"><span id=\"MathJax-Span-10\" class=\"math\"><span><span id=\"MathJax-Span-11\" class=\"mrow\"><span id=\"MathJax-Span-12\" class=\"msub\"><i><span id=\"MathJax-Span-13\" class=\"mi\">V</span></i><sub><span id=\"MathJax-Span-14\" class=\"mi\">P</span></sub></span><span id=\"MathJax-Span-15\" class=\"mo\">/</span><span id=\"MathJax-Span-16\" class=\"msub\"><i><span id=\"MathJax-Span-17\" class=\"mi\">V</span></i><sub><span id=\"MathJax-Span-18\" class=\"mi\">S</span></sub></span></span></span></span></span></span><span>&nbsp;ratio (∼1.8). The&nbsp;</span><i>S</i><span>&nbsp;coda is more difficult to characterize but has a clear dominant frequency that likely reflects the near‐source velocity and attenuation structure. Our study indicates that waveforms from earthquakes near the interface of the subducted slab can be used to infer detailed structural information about the plate‐boundary zone at seismogenic depths.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210261","usgsCitation":"Gong, J., and McGuire, J.J., 2022, Waveform signatures of earthquakes located close to the subducted Gorda Plate interface: Bulletin of the Seismological Society of America, v. 112, no. 5, p. 2440-2453, https://doi.org/10.1785/0120210261.","productDescription":"14 p.","startPage":"2440","endPage":"2453","ipdsId":"IP-133636","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419498,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Gorda Plate interface","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -124,\n              41\n            ],\n            [\n              -125.5,\n              41\n            ],\n            [\n              -125.5,\n              40\n            ],\n            [\n              -124,\n              40\n            ],\n            [\n              -124,\n              41\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"112","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gong, Jianhua","contributorId":317847,"corporation":false,"usgs":false,"family":"Gong","given":"Jianhua","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":879443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":220939,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879444,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70262263,"text":"70262263 - 2022 - Small anthropogenic landforms from past charcoal production control moisture dynamics and chemistry in northcentral Appalachian soils","interactions":[],"lastModifiedDate":"2025-01-17T16:53:38.4344","indexId":"70262263","displayToPublicDate":"2022-10-03T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Small anthropogenic landforms from past charcoal production control moisture dynamics and chemistry in northcentral Appalachian soils","docAbstract":"Throughout the northeastern United States (U.S.) and Europe, relict charcoal hearths (RCHs) are regularly being discovered in proximity to furnaces once used for the extraction of metal from ore or quick-lime production; charcoal produced in hearths was used as a furnace fuel. Given previous research has shown that topographic and subsurface disturbance can be great when a hearth is constructed, we hypothesize that hearth construction alters surface hydrology and soil chemistry in environments in and near hearths. We used a landscape classification process to identify 6,758 hearths near furnaces at Greenwood and Pine Grove Furnace State Park, central and southcentral Pennsylvania, U.S. Two types of digital elevation model wetness indexes were used to quantify surface hydrology effects in and around hearths. Modeled wetness conditions were compared to field soil volumetric water content in RCHs near Greenwood Furnace State Park. Modeled wetness indexes indicate that RCH interiors are significantly wetter than RCH rim areas; RCHs are acting as a landscape moisture sink. Results also indicate that RCHs on slopes result in downslope drier conditions below RCHs. Field measured volumetric water content indicates that as distance from the center of the hearth increases, soil moisture significantly decreases. Geomorphic position was found to not be related to RCH wetness. Soil from RCHs, compared to nearby native soils, has significantly higher total C, a lower Mehlich 3 extractable acidity, higher Ca and P. No trend was evident with RCH soil chemistry and geomorphic position. The high frequency of RCH occurrence, in proximity to the furnace’s RCHs supported, suggests that RCHs today could locally be an important niche for understory flora and fauna. Further research could explore how RCHs might be affecting surrounding plant populations and how within RCH patterns, especially on hillslopes, might represent a distinctly different scale of physical and chemical variability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2022.108379","usgsCitation":"Bayuzick, S., Guarin, D., Bonhage, A., Hirsch, F., Diefenbach, D.R., McDill, M., Raab, T., and Drohan, P., 2022, Small anthropogenic landforms from past charcoal production control moisture dynamics and chemistry in northcentral Appalachian soils: Geomorphology, v. 415, 108379, 11 p., https://doi.org/10.1016/j.geomorph.2022.108379.","productDescription":"108379, 11 p.","ipdsId":"IP-143732","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2022.108379","text":"Publisher Index Page"},{"id":480751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Greenwood Furnace State Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -77.96106385041638,\n              40.65430370223336\n            ],\n            [\n              -77.96106385041638,\n              40.5738214187607\n            ],\n            [\n              -77.78470131822338,\n              40.5738214187607\n            ],\n            [\n              -77.78470131822338,\n              40.65430370223336\n            ],\n            [\n              -77.96106385041638,\n              40.65430370223336\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bayuzick, S.","contributorId":348658,"corporation":false,"usgs":false,"family":"Bayuzick","given":"S.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guarin, D.","contributorId":348662,"corporation":false,"usgs":false,"family":"Guarin","given":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonhage, A.","contributorId":348664,"corporation":false,"usgs":false,"family":"Bonhage","given":"A.","affiliations":[{"id":83395,"text":"Brandenburg University of Technology","active":true,"usgs":false}],"preferred":false,"id":923686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hirsch, F.","contributorId":348665,"corporation":false,"usgs":false,"family":"Hirsch","given":"F.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDill, M.","contributorId":348666,"corporation":false,"usgs":false,"family":"McDill","given":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raab, T.","contributorId":348667,"corporation":false,"usgs":false,"family":"Raab","given":"T.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923690,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Drohan, P.J.","contributorId":348668,"corporation":false,"usgs":false,"family":"Drohan","given":"P.J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":923691,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70237667,"text":"70237667 - 2022 - Climate and land use driven ecosystem homogenization in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-10-18T14:46:34.062232","indexId":"70237667","displayToPublicDate":"2022-10-02T09:39:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Climate and land use driven ecosystem homogenization in the Prairie Pothole Region","docAbstract":"<p><span>The homogenization of freshwater ecosystems and their biological communities has emerged as a prevalent and concerning phenomenon because of the loss of ecosystem multifunctionality. The millions of prairie-pothole wetlands scattered across the Prairie Pothole Region (hereafter PPR) provide critical ecosystem functions at local, regional, and continental scales. However, an estimated loss of 50% of historical wetlands and the widespread conversion of grasslands to cropland make the PPR a heavily modified landscape. Therefore, it is essential to understand the current and potential future stressors affecting prairie-pothole wetland ecosystems in order to conserve and restore their functions. Here, we describe a conceptual model that illustrates how (a) historical wetland losses, (b) anthropogenic landscape modifications, and (c) climate change interact and have altered the variability among remaining depressional wetland ecosystems (i.e., ecosystem homogenization) in the PPR. We reviewed the existing literature to provide examples of wetland ecosystem homogenization, provide implications for wetland management, and identify informational gaps that require further study. We found evidence for spatial, hydrological, chemical, and biological homogenization of prairie-pothole wetlands. Our findings indicate that the maintenance of wetland ecosystem multifunctionality is dependent on the preservation and restoration of heterogenous wetland complexes, especially the restoration of small wetland basins.</span></p>","language":"English","publisher":"MPDI","doi":"10.3390/w14193106","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J., 2022, Climate and land use driven ecosystem homogenization in the Prairie Pothole Region: Water, v. 14, no. 19, 3106, 19 p., https://doi.org/10.3390/w14193106.","productDescription":"3106, 19 p.","ipdsId":"IP-142441","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446248,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14193106","text":"Publisher Index Page"},{"id":408480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -95.712890625,\n              43.58039085560784\n            ],\n            [\n              -94.74609375,\n              41.50857729743935\n            ],\n            [\n              -92.548828125,\n              41.77131167976407\n            ],\n            [\n              -92.900390625,\n              43.32517767999296\n            ],\n            [\n              -94.04296874999999,\n              45.460130637921004\n            ],\n            [\n              -95.537109375,\n              48.45835188280866\n            ],\n            [\n              -96.85546875,\n              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Center","active":true,"usgs":true}],"preferred":true,"id":854915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon","contributorId":298028,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854917,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70237223,"text":"70237223 - 2022 - Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","interactions":[],"lastModifiedDate":"2026-03-18T15:16:45.794674","indexId":"70237223","displayToPublicDate":"2022-10-01T10:07:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":23618,"text":"Quality Assurance Project Plan","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"22-03-109","title":"Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","docAbstract":"<p>The United States Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) are collaborating on the development of refined, seasonal load estimates of total nitrogen and total phosphorus within watersheds draining to Washington waters of the Salish Sea for the period 2005-2020. The modeling approach for this work is based on SPARROW (Spatially Referenced Regression on Watershed Attributes), a watershed modeling technique developed by the USGS. SPARROW is typically used to estimate stream loads throughout a stream network. &nbsp;</p><p>The estimated loads will be used within the context of the Puget Sound Nutrient Source Reduction Project to evaluate the influence of watershed contributions of nutrients throughout the stream network and to marine waters. This quality assurance project plan (QAPP) contains details about the technical approach, observational data, spatial and temporal source data, limitations, and quality assurance procedures that will be employed to develop the SPARROW models so that they can be used to inform additional actions to address excess nutrients.&nbsp;</p>","language":"English","publisher":"Washington State Department of Ecology (Ecology)","usgsCitation":"Figueroa-Kaminsky, C., Wasielewski, J., McCarthy, S., Schmadel, N., Wise, D., Johnson, Z., and Black, R.W., 2022, Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW): Quality Assurance Project Plan 22-03-109, 76 p.","productDescription":"76 p.","ipdsId":"IP-143204","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":501247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501246,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://apps.ecology.wa.gov/publications/SummaryPages/2203109.html"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.52217372194855,\n              48.967370797762754\n            ],\n            [\n              -123.88225129810027,\n              48.967370797762754\n            ],\n            [\n              -123.88225129810027,\n              45.85254256141661\n            ],\n            [\n              -120.52217372194855,\n              45.85254256141661\n            ],\n            [\n              -120.52217372194855,\n              48.967370797762754\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Figueroa-Kaminsky, Cristiana","contributorId":350514,"corporation":false,"usgs":false,"family":"Figueroa-Kaminsky","given":"Cristiana","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":957136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasielewski, Jamie K. 0009-0005-7497-3344","orcid":"https://orcid.org/0009-0005-7497-3344","contributorId":344993,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Jamie K.","affiliations":[{"id":82458,"text":"Washington Dept. of Ecology","active":true,"usgs":false}],"preferred":false,"id":957137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCarthy, Sheelagh","contributorId":367235,"corporation":false,"usgs":false,"family":"McCarthy","given":"Sheelagh","affiliations":[],"preferred":false,"id":957138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":957139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":957140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":957141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853672,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70239208,"text":"70239208 - 2022 - Survival and growth of four floodplain forest species in an Upper Mississippi River underplanting","interactions":[],"lastModifiedDate":"2023-01-04T15:30:54.592533","indexId":"70239208","displayToPublicDate":"2022-10-01T09:30:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12993,"text":"Tree Planters Notes","active":true,"publicationSubtype":{"id":10}},"title":"Survival and growth of four floodplain forest species in an Upper Mississippi River underplanting","docAbstract":"<p>Forest restoration efforts commonly occur in degraded ecosystems. For the floodplain forests of the Upper Mississippi River, the combination of aging canopy trees and expansion of invasive species such as reed canary grass (<i>Phalaris arundinacea</i> L.) can shift forested ecosystems to open meadows. Before this shift occurs, there may be opportunities to proactively underplant. Our study reports 2-year survival and growth of four tree species (swamp white oak (<i>Quercus bicolor</i> Wild.), silver maple (<i>Acer saccharinum</i> L.), hackberry (<i>Celtis occidentalis</i> L.), and sycamore (<i>Platanus occidentalis</i> L.) planted under a moderate canopy of silver maple (approximately 60 percent overstory cover) across three elevational gradients. Swamp white oak had high survival across all three of the elevational zones and showed limited effects by herbivory or insects. Growth and survival of sycamore and hackberry depended on the elevational zone; sycamore performed better on lower elevational sites and hackberry did better on higher elevational sites. Our results highlight the potential for underplanting in floodplain forests as a proactive restoration strategy, with consideration given to local site conditions.&nbsp;</p>","language":"English","publisher":"Reforestation, Nurseries, & Genetic Resources","usgsCitation":"Windemuller-Campione, M., Van Appledorn, M., Meier, A.R., and Reuling, L.F., 2022, Survival and growth of four floodplain forest species in an Upper Mississippi River underplanting: Tree Planters Notes, v. 65, no. 2, p. 87-97.","productDescription":"11 p.","startPage":"87","endPage":"97","ipdsId":"IP-138620","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":411344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411319,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://rngr.net/publications/tpn/65-2"}],"country":"United States","state":"Iowa","county":"Allamakee County","otherGeospatial":"Kains Switch South forest management site, Upper Mississippi River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.13233393731261,\n              43.07252428912568\n            ],\n            [\n              -91.33906110754354,\n              43.07252428912568\n            ],\n            [\n              -91.33906110754354,\n              42.898986750210554\n            ],\n            [\n              -91.13233393731261,\n              42.898986750210554\n            ],\n            [\n              -91.13233393731261,\n              43.07252428912568\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"65","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Windemuller-Campione, Marcella","contributorId":300543,"corporation":false,"usgs":false,"family":"Windemuller-Campione","given":"Marcella","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":860765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meier, Andrew R.","contributorId":215691,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":860766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reuling, Laura F.","contributorId":292937,"corporation":false,"usgs":false,"family":"Reuling","given":"Laura","email":"","middleInitial":"F.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860767,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237944,"text":"70237944 - 2022 - Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","interactions":[],"lastModifiedDate":"2022-11-01T12:04:00.80791","indexId":"70237944","displayToPublicDate":"2022-10-01T06:59:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000&nbsp;km<sup>2</sup><span>&nbsp;of croplands, monitoring unintentional and intentional surface water inundation is important for&nbsp;<a class=\"topic-link\" title=\"Learn more about water resource management from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/water-resources-development\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/water-resources-development\">water resource management</a>&nbsp;and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n&nbsp;=&nbsp;216 months) derived using two&nbsp;<a class=\"topic-link\" title=\"Learn more about satellite remote sensing from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\">satellite remote sensing</a>&nbsp;platforms (Landsat and&nbsp;<a class=\"topic-link\" title=\"Learn more about Moderate Resolution Imaging Spectroradiometer from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis\">Moderate Resolution Imaging Spectroradiometer</a>&nbsp;[MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.</span></p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100837","usgsCitation":"Smith, B.W., Soulard, C.E., Walker, J., and Wein, A., 2022, Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆: Remote Sensing Applications: Society and Environment, v. 28, 100837, 15 p., https://doi.org/10.1016/j.rsase.2022.100837.","productDescription":"100837, 15 p.","ipdsId":"IP-140896","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":435671,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKSSXX","text":"USGS data release","linkHelpText":"County-level maps of cropland surface water inundation measured from Landsat and MODIS"},{"id":408970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.88667659285775,\n              42.496396873623155\n            ],\n            [\n              -125.88667659285775,\n              31.587887454403287\n            ],\n            [\n              -113.05464534285768,\n              31.587887454403287\n            ],\n            [\n              -113.05464534285768,\n              42.496396873623155\n            ],\n            [\n              -125.88667659285775,\n              42.496396873623155\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Britt Windsor 0000-0003-1556-2383","orcid":"https://orcid.org/0000-0003-1556-2383","contributorId":287481,"corporation":false,"usgs":true,"family":"Smith","given":"Britt","email":"","middleInitial":"Windsor","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856296,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70237117,"text":"sir20225091 - 2022 - Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon","interactions":[],"lastModifiedDate":"2022-10-03T11:05:36.759623","indexId":"sir20225091","displayToPublicDate":"2022-09-30T12:22:27","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":"2022-5091","displayTitle":"Assessing the Impact of Chloride Deicer Application in the Siskiyou Pass, Southern Oregon","title":"Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon","docAbstract":"<p class=\"p1\">Chloride deicers have been applied by the Oregon Department of Transportation (ODOT) to Interstate Route 5 (I–5) from the Oregon-California border north to mile marker 10 for several years in the high-elevation area known as the Siskiyou Pass. Magnesium chloride (MgCl<sub><span class=\"s1\">2</span></sub>) and sodium chloride (NaCl) are applied to keep the interstate highway safe for drivers and allow for efficient transport of goods and people through adverse weather conditions, particularly snow and ice. The U.S. Geological Survey entered into a cooperative agreement with ODOT to research the effects of chloride deicers in the Carter and Wall Creek watersheds that drain the vicinity of the Siskiyou Pass.</p><p class=\"p1\">The Stochastic Empirical Loading and Dilution Model (SELDM) was used to estimate combinations of prestorm-streamflow, stormflow, highway-runoff, and event mean constituent concentrations (EMCs), as well as stormwater-constituent loads at sites of interest. The study evaluated the effects of roadway application of chloride deicers on downstream and highway-runoff conditions (particularly EMCs), exceedance rates of criterion maximum concentrations, and concurrent runoff loads of stormwater constituents from a site of interest. SELDM was also used to evaluate the efficiency of hydrograph extension best management practices to reduce peak constituent concentrations. Several SELDM scenarios were developed as sensitivity analyses to evaluate the model benefit of collecting specific local sets of data, such as streamflow, precipitation, highway-runoff and riverine water-quality samples, and volumetric runoff coefficient statistics.</p><p class=\"p1\">Results of the study showed that for SELDM modeling in the Siskiyou Pass area, (1) the inclusion of local streamflow data is important for obtaining accurate downstream EMCs, (2) the inclusion of precipitation data is important for highway and concurrent runoff load calculations, and (3) water-quality constituent EMC data from highway runoff and upstream stormflows are the most important data to collect for highway runoff and upstream water-quality constituent concentration statistics.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225091","collaboration":"Prepared in cooperation with the Oregon Department of Transportation","usgsCitation":"Stonewall, A.J., Yates, M.C., and Granato, G.E., 2022, Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5091, 94 p., https://doi.org/10.3133/sir20225091.","productDescription":"Report: xii, 93 p.; Data Release; 4 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107308","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":407662,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_3.csv","text":"Table 3","size":"13 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 3"},{"id":407660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5091/coverthb.jpg"},{"id":407661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5091"},{"id":407666,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_17.csv","text":"Table 17","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 17"},{"id":407668,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_26.csv","text":"Table 26","size":"4 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 26"},{"id":407670,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5091/images"},{"id":407671,"rank":10,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.XML"},{"id":407664,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_10.csv","text":"Table 10","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 10"},{"id":407673,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q1PP61","text":"USGS data release","description":"USGS data release","linkHelpText":"Stochastic Empirical Loading and Dilution Model (SELDM) model archive and instructions for the Siskiyou Pass, Oregon"},{"id":407675,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26_csv.zip","text":"Tables 3, 10, 17, and 26 CSV files","size":"7 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"},{"id":407674,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26.xlsx","text":"Tables 3, 10, 17, and 26 Excel file","size":"36 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"}],"country":"United States","state":"Oregon","otherGeospatial":"Siskiyou Pass","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.32,\n              42.02\n            ],\n            [\n              -122.32,\n              42.02\n            ],\n            [\n              -122.32,\n              42.02\n            ],\n            [\n              -122.40,\n              42.08\n            ],\n            [\n              -122.40,\n              42.08\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/oregon-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/oregon-water-science-center\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>2130 SW 5th Avenue<br>Portland, Oregon 97201</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>SELDM Background</li><li>Hydrologic Setting</li><li>Acquisition of Local Hydrological and Meteorological Data</li><li>Regional Background Concentrations of Chloride, Magnesium, and Sodium</li><li>Development of SELDM Scenarios</li><li>SELDM Results</li><li>Limitations of the Analyses</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2022-09-30","noUsgsAuthors":false,"publicationDate":"2022-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Stonewall, Adam J. 0000-0002-3277-8736 stonewal@usgs.gov","orcid":"https://orcid.org/0000-0002-3277-8736","contributorId":2699,"corporation":false,"usgs":true,"family":"Stonewall","given":"Adam J.","email":"stonewal@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":853382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yates, Matthew C. 0000-0002-9117-5046 myates@usgs.gov","orcid":"https://orcid.org/0000-0002-9117-5046","contributorId":297123,"corporation":false,"usgs":false,"family":"Yates","given":"Matthew","email":"myates@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":853383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":197631,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory","email":"ggranato@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853384,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70238675,"text":"70238675 - 2022 - Lateral moraines, ice-dammed lakes, and meltwater-carved channels in the Pelham, Shutesbury, Leverett area of west-central Massachusetts: A record of Connecticut Valley ice lobe retreat","interactions":[],"lastModifiedDate":"2022-12-05T16:02:34.496533","indexId":"70238675","displayToPublicDate":"2022-09-30T09:48:24","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"chapter":"B4","title":"Lateral moraines, ice-dammed lakes, and meltwater-carved channels in the Pelham, Shutesbury, Leverett area of west-central Massachusetts: A record of Connecticut Valley ice lobe retreat","docAbstract":"Temporary ice-dammed glacial lakes formed high in the landscape in several westward sloping valleys on the east side of the Connecticut Valley lowland during late Wisconsinan deglaciation. These lakes were impounded by a lengthy lobe of ice that extended farther south in the lowland than at upland retreatal ice-margin positions (fig. 1). The formation, lowering, and drainage of these ice-dammed lakes successively preceded the extension of glacial Lake Hitchcock northward in the Deerfield Basin as the ice lobe retreated to the north of the Holyoke Range. Detailed surficial geologic mapping of the Shutesbury Quadrangle (Stone, J.R., 1978), along with regional compilation for the Surficial Materials Map of Massachusetts (Stone, J.R. and others, 2018), and ongoing compilation of the Quaternary Geologic Map of Massachusetts has established the extent of these lake deposits and locations of their meltwater- carved spillways. However, recently obtained Lidar-derived high-resolution digital elevation model (DEM) images provide new evidence for mapping of previously unrecognized features not visible on traditional 1:24,000-scale, 10-ft contour interval topographic maps. The newly mapped features have less than 10 ft (3 m) of relief and are believed to be successive lateral moraines left behind by the west-northwesterly retreating Connecticut Valley ice lobe. On the Fieldtrip, we will examine deltaic deposits of ice-dammed glacial lakes in the valleys of Amethyst Brook in the town of Pelham and Roaring Brook in Shutesbury and Leverett, as well as their associated meltwater-carved spillways and feeding meltwater channels. Physical aspects of the low ridges interpreted as lateral moraines will be examined on the ground. Much of the landscape in Pelham, Shutesbury, and Leverett is forested, much is classified as open space, and there are many trails.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Guidebook for field trips in Massachusetts and surrounding area","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"New England Intercollegiate Geological Conference","conferenceDate":"Sep 30-Oct 2, 2022","language":"English","publisher":"New England Intercollegiate Geological Conference","usgsCitation":"Stone, J.R., and DiGiacomo-Cohen, M.L., 2022, Lateral moraines, ice-dammed lakes, and meltwater-carved channels in the Pelham, Shutesbury, Leverett area of west-central Massachusetts: A record of Connecticut Valley ice lobe retreat, <i>in</i> Guidebook for field trips in Massachusetts and surrounding area, Sep 30-Oct 2, 2022, 20 p.","productDescription":"20 p.","ipdsId":"IP-144989","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":410049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":410034,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://storymaps.arcgis.com/stories/6979f221b3d441baa34c8cc4bb970d53","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","city":"Leverett, Pelham, Shutesbury","otherGeospatial":"Connecticut Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -72.52216594151821,\n              42.49325545704872\n            ],\n            [\n              -72.52216594151821,\n              42.34160499755882\n            ],\n            [\n              -72.36557015522463,\n              42.34160499755882\n            ],\n            [\n              -72.36557015522463,\n              42.49325545704872\n            ],\n            [\n              -72.52216594151821,\n              42.49325545704872\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stone, Janet R. 0000-0003-0095-8238 jrstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0095-8238","contributorId":299644,"corporation":false,"usgs":true,"family":"Stone","given":"Janet","email":"jrstone@usgs.gov","middleInitial":"R.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":858243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiGiacomo-Cohen, Mary L. 0000-0003-2384-8912 mdicohen@usgs.gov","orcid":"https://orcid.org/0000-0003-2384-8912","contributorId":2527,"corporation":false,"usgs":true,"family":"DiGiacomo-Cohen","given":"Mary","email":"mdicohen@usgs.gov","middleInitial":"L.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":858244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70247095,"text":"70247095 - 2022 - Snake River fall Chinook salmon research and monitoring","interactions":[],"lastModifiedDate":"2023-07-24T14:53:26.050509","indexId":"70247095","displayToPublicDate":"2022-09-30T09:34:09","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Snake River fall Chinook salmon research and monitoring","docAbstract":"<p>In 2021, the U.S. Geological Survey (USGS) focused adult salmon survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use used a boat-mounted underwater video camera to count 93 deepwater redds at 17 of the 28 sites surveyed. Redd depths averaged 3.9 m. In conjunction with the Idaho Power Company, we collected genetic samples from 346 live fall Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and 15 carcasses at 48 unique geographic locations that spanned 90 river kilometers. Seventy-two fish were recovered at Eureka Bar (river kilometer [rkm] 307.1) and High Range (rkm 332.3), which accounted for 20% of all collected fish in 2021. Most (291 fish) post-spawned salmon were collected during the first two weeks of November around the peak of spawning. A summary of 2021 PBT results produced by the Idaho Power Company can be found in Appendix A.2. </p><p>Beach seining and PIT tagging of subyearling fall Chinook salmon was conducted in Snake and Salmon rivers to obtain information on population metrics and growth as well as to provide data for ongoing life-cycle modeling. In the Snake River, we collected 13,710 subyearlings, tagged 6,299, and recaptured 981 (15.6%). Using 8-mm tags in 45–49-mm fish allowed us to represent an additional 40% of the juvenile population through PIT tagging beyond just using standard 9- and 12-mm tags. In the Salmon River, we captured 103 natural subyearlings with the majority (60%) of fish being captured at two sites: rkm 11 and 20. We tagged 27 subyearlings, and no fish were recaptured. In Lower Granite Reservoir, we captured 4,887 subyearlings, PIT tagged 2,585, and recaptured 312. </p><p>Many of the subyearlings we tagged in the Snake River were detected passing Lower Granite Dam, but no fish tagged in the Salmon River were detected. In total we detected 673 (7.6%) tagged fish at Lower Granite Dam, and detection rates varied by tag size and passage route. More subyearlings were detected passing via the removable spill weir (RSW) earlier in the season while more fish were detected passing through the juvenile fish bypass system (JBS) later in the season. In general, fish tagged with 12-mm PIT tags had higher detection rates than fish tagged with smaller tags. Survival to Lower Granite Dam was low and ranged from 0.195 to 0.228. Growth of subyearlings was similar between riverine and reservoir reaches but was slightly lower in Lower Granite Reservoir. Only 19 subyearlings were recaptured at Lower Granite Dam in early autumn that predominantly originated from the Clearwater River (1 fish was from the Snake River). Mean standard deviation (SD) fork length and mass growth rates were 0.92±0.10 mm/d and 0.06±0.01 g/g/d, respectively.</p>","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"2022, Snake River fall Chinook salmon research and monitoring, 66 p.","productDescription":"66 p.","ipdsId":"IP-146145","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":419248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419239,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.38976363947546,\n              46.95039331929084\n            ],\n            [\n              -117.38976363947546,\n              45.32672178879673\n            ],\n            [\n              -116.27624197937624,\n              45.32672178879673\n            ],\n            [\n              -116.27624197937624,\n              46.95039331929084\n            ],\n            [\n              -117.38976363947546,\n              46.95039331929084\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878877,"contributorType":{"id":2,"text":"Editors"},"rank":1}]}}
]}