We determine the long-term growth rate and longevity of an Azorella compacta growing on Misti volcano, near Arequipa, Peru to investigate the species' capacity as a geochronological resource. Using 14C dating on stem pieces sequestered within the plant's cushion, which grows larger through time, we obtain ages of 15 ± 15 14C yrs BP and 165 ± 15 14C yrs BP at depths of 15 cm and 29 cm below the cushion's living surface, respectively. Applying a mixed calibration curve with a Bayesian growth model yields calendar age ranges of 1948–1958 CE and 1802–1935 CE for our 14C dates, respectively. Such ages provide sufficiently precise constraints for investigations requiring dating during the last few hundred years when individual 14C dates yield imprecise calendar age ranges. We infer a long-term growth rate of 1.3–3.5 mm yr−1, corroborating published maximum short-term growth rates. Extrapolating our growth model to the A. compacta's core suggests that it began growing as early as 1462–1830 CE. At such age it lived through myriad important geological and historical events, including regional earthquakes, volcanic unrest at Misti, decades to centuries of the Little Ice Age, and a broad transect of Peruvian history possibly beginning during the Inca Empire. A. compacta may provide another important geochronological resource in the arid Central Andes that can be applied to date volcanological, glacial, mass-movement, and archaeological features, especially where dendrochronology and lichenometry are not possible.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2021.104470","usgsCitation":"Harpel, C., Kleier, C., and Aguilar, R., 2021, Azorella compacta's long-term growth rate, longevity, and potential for dating geomorphological and archaeological features in the arid southern Peruvian Andes: Journal of Arid Environments, v. 188, 104470, 5 p., https://doi.org/10.1016/j.jaridenv.2021.104470.","productDescription":"104470, 5 p.","ipdsId":"IP-120064","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":453367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2021.104470","text":"Publisher Index Page"},{"id":383610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Peruvian Andes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5859375,\n 0.08789059053082422\n ],\n [\n -78.134765625,\n -2.8991526985043006\n ],\n [\n -80.244140625,\n -3.337953961416472\n ],\n [\n -81.38671875,\n -4.477856485570586\n ],\n [\n -81.03515625,\n -6.053161295714067\n ],\n [\n -75.6298828125,\n -15.114552871944102\n ],\n [\n -70.1806640625,\n -18.687878686034182\n ],\n [\n -69.4775390625,\n -17.26672782352052\n ],\n [\n -68.994140625,\n -16.299051014581817\n ],\n [\n -68.466796875,\n -12.382928338487396\n ],\n [\n -69.8291015625,\n -10.833305983642491\n ],\n [\n -70.6201171875,\n -9.44906182688142\n ],\n [\n -70.9716796875,\n -4.127285323245357\n ],\n [\n -70.09277343749999,\n -2.67968661580376\n ],\n [\n -75.5859375,\n 0.08789059053082422\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"188","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleier, Catherine","contributorId":252546,"corporation":false,"usgs":false,"family":"Kleier","given":"Catherine","email":"","affiliations":[{"id":50430,"text":"College of Agriculture, Forestry, and Environmental Science, California State Polytechnic University, San Luis Obispo","active":true,"usgs":false}],"preferred":false,"id":810901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aguilar, Rigoberto","contributorId":252547,"corporation":false,"usgs":false,"family":"Aguilar","given":"Rigoberto","affiliations":[{"id":50431,"text":"Observatorio Vulcanologico del Instituto Geologico, Minero y Metalurgico del Peru","active":true,"usgs":false}],"preferred":false,"id":810902,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218251,"text":"70218251 - 2021 - Salinity changes the dynamics of pyrethroid toxicity in terms of behavioral effects on newly hatched delta smelt larvae","interactions":[],"lastModifiedDate":"2021-02-22T13:41:06.189067","indexId":"70218251","displayToPublicDate":"2021-02-20T06:37:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7597,"text":"Toxics","active":true,"publicationSubtype":{"id":10}},"title":"Salinity changes the dynamics of pyrethroid toxicity in terms of behavioral effects on newly hatched delta smelt larvae","docAbstract":"Mangrove forests are likely vulnerable to accelerating sea-level rise; however, we lack the tools necessary to understand their future resilience. On the Pacific island of Pohnpei, Federated States of Micronesia, mangroves are habitat to endangered species and provide critical ecosystem services that support local communities. We developed a generalizable modeling framework for mangroves that accounts for species interactions and the belowground processes that dictate soil elevation. The modeling framework was calibrated with extensive field datasets, including accretion rates derived from thirty 1-meter-deep soil cores dated with lead-210, more than 300 forest inventory plots, water-level monitoring, and differential leveling elevation surveys. We applied the model using a community of five mangrove species and across seven regions around Pohnpei to identify which regions are most vulnerable to sea-level rise. The responses of mean elevation and the mangrove community composition were analyzed under four global sea-level rise scenarios: an increase of 37, 52, 67, or 117 centimeters by 2100. The model was validated against a 20-year surface elevation table record (1999–2019) and showed good agreement when driven by observed water levels.
The model projected that mangroves around Pohnpei can build their elevations relative to moderate rates of sea-level rise to prevent submergence, with limited changes in mangrove community composition through 2060. By 2100, however, the model projected a decreasing abundance of high-elevation mangrove species and an increasing abundance of lower elevation species adapted to more persistent flooding. Under higher sea-level rise scenarios, forest elevation decreased substantially relative to mean sea level and there were more drastic changes in the tree community composition and loss of suitable mangrove habitat by 2100. Variation in accretion rates, water levels, and initial forest elevation led to differential vulnerability around the island, such that mangroves on the leeward side of the island generally were the most at-risk to higher rates of sea-level rise. Our findings indicate that the relatively undisturbed state of the mangrove forests and the surrounding landscape is an important factor in their ability to keep pace with sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211002","collaboration":"Prepared in cooperation with the U.S. Forest Service","usgsCitation":"Buffington, K.J., MacKenzie, R.A., Carr, J.A., Apwong, M., Krauss, K.W., and Thorne, K.M., 2021, Mangrove species’ response to sea-level rise across Pohnpei, Federated States of Micronesia: U.S. Geological Survey Open-File Report 2021–1002, 44 p., https://doi.org/10.3133/ofr20211002.","productDescription":"Report: vii, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-121673","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436498,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R8MZQ","text":"USGS data release","linkHelpText":"Mangrove Elevation and Species' Responses to Sea-level Rise Across Pohnpei, Federated States of Micronesia (ver. 1.1, December 2021)"},{"id":383370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1002/covrthb.jpg"},{"id":383371,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1002/ofr20211002.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383372,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1002/ofr20211002.xml"},{"id":383373,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1002/images"},{"id":383374,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDZX32","linkHelpText":"Pohnpei, Federated States of Micronesia Mangrove Elevation Survey Data"}],"country":"Federated States of Micronesia","state":"Pohnpei","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 158.06442260742188,\n 6.7723525317661215\n ],\n [\n 158.3740997314453,\n 6.7723525317661215\n ],\n [\n 158.3740997314453,\n 7.013667927566642\n ],\n [\n 158.06442260742188,\n 7.013667927566642\n ],\n [\n 158.06442260742188,\n 6.7723525317661215\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Human enterprise has led to large‐scale changes in landscapes and altered wildlife population distribution and abundance, necessitating efficient and effective conservation strategies for impacted species. Greater sage‐grouse (Centrocercus urophasianus; hereafter sage‐grouse) are a widespread sagebrush (Artemisia spp.) obligate species that has experienced population declines since the mid‐1900s resulting from habitat loss and expansion of anthropogenic features into sagebrush ecosystems. Habitat loss is especially evident in North Dakota, USA, on the northeastern fringe of sage‐grouse’ distribution, where a remnant population remains despite recent development of energy‐related infrastructure. Resource managers in this region have determined a need to augment sage‐grouse populations using translocation techniques that can be important management tools for countering species decline from range contraction. Although translocations are a common tool for wildlife management, very little research has evaluated habitat following translocation, to track individual behaviors such as habitat selection and fidelity to the release site, which can help inform habitat requirements to guide selection of future release sites. We provide an example where locations from previously released radio‐marked sage‐grouse are used in a resource selection function framework to evaluate habitat selection following translocation and identify areas of seasonal habitat to inform habitat management and potential restoration needs. We also evaluated possible changes in seasonal habitat since the late 1980s using spatial data provided by the Rangeland Analysis Platform coupled with resource selection modeling results. Our results serve as critical baseline information for habitat used by translocated individuals across life stages in this study area, and will inform future evaluations of population performance and potential for long‐term recovery.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.7228","usgsCitation":"Lazenby, K.D., Coates, P.S., O’Neil, S.T., Kohl, M.T., and Dahlgren, D.K., 2021, Nesting, brood rearing, and summer habitat selection by translocated greater sage‐grouse in North Dakota, USA: Ecology and Evolution, v. 11, no. 6, p. 2741-2760, https://doi.org/10.1002/ece3.7228.","productDescription":"20 p.","startPage":"2741","endPage":"2760","ipdsId":"IP-119290","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453379,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.7228","text":"External Repository"},{"id":436499,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91GQXVE","text":"USGS data release","linkHelpText":"Geospatial Information and Predictive Maps of Greater Sage-grouse Habitat Selection in Southwestern North Dakota, USA"},{"id":385280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.7216796875,\n 45.48324350868221\n ],\n [\n -103.3154296875,\n 45.48324350868221\n ],\n [\n -103.3154296875,\n 46.70973594407157\n ],\n [\n -104.7216796875,\n 46.70973594407157\n ],\n [\n -104.7216796875,\n 45.48324350868221\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.2208251953125,\n 42.05337156043361\n ],\n [\n -106.8585205078125,\n 42.05337156043361\n ],\n [\n -106.8585205078125,\n 42.549033612225145\n ],\n [\n -108.2208251953125,\n 42.549033612225145\n ],\n [\n -108.2208251953125,\n 42.05337156043361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lazenby, Kade D.","contributorId":257564,"corporation":false,"usgs":false,"family":"Lazenby","given":"Kade","email":"","middleInitial":"D.","affiliations":[{"id":52056,"text":"Department of Wildland Resources, Jack H. Berryman Institute, S. J. Quinney College of Natural Resources, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":814629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kohl, Michel T.","contributorId":204214,"corporation":false,"usgs":false,"family":"Kohl","given":"Michel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":814632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahlgren, David K.","contributorId":257565,"corporation":false,"usgs":false,"family":"Dahlgren","given":"David","email":"","middleInitial":"K.","affiliations":[{"id":52056,"text":"Department of Wildland Resources, Jack H. Berryman Institute, S. J. Quinney College of Natural Resources, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":814633,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236823,"text":"70236823 - 2021 - Response study of a 51-story-tall Los Angeles, California building inferred from motions of the Mw7.1 July 5, 2019 Ridgecrest, California earthquake","interactions":[],"lastModifiedDate":"2024-09-24T18:42:02.563556","indexId":"70236823","displayToPublicDate":"2021-02-19T08:55:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1101,"text":"Bulletin of Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Response study of a 51-story-tall Los Angeles, California building inferred from motions of the Mw7.1 July 5, 2019 Ridgecrest, California earthquake","docAbstract":"A 51-story building in downtown Los Angeles that is equipped with a seismic monitoring accelerometric array recorded the Mw7.1 Ridgecrest, California earthquake of July 5, 2019. The building is a dual-core reinforced-concrete shear-wall and perimeter-column structure with ~ 80% of floors constructed as post-tensioned flat slabs, which makes it a trending design. Using system identification methods, spectral analyses, and coherence-phase angle computations, the recorded response data allowed the identification of dynamic response characteristics (fundamental frequencies of [NS] 0.21 Hz, [EW] 0.28 Hz, and [Torsional] 0.45 Hz, critical damping percentages < 2.5%, and associated mode shapes), as well as computation of drift ratios with maximum peaks of 0.145% for both NS and EW directions. The critical damping percentages are consistent with those recommended by LATBSDC (2017). There is no indication from the records that post-tensioned slab design played any role in altering the dynamic characteristics.
","language":"English","publisher":"Springer","doi":"10.1007/s10518-021-01053-9","usgsCitation":"Celebi, M., Swensen, D., and Haddadi, H., 2021, Response study of a 51-story-tall Los Angeles, California building inferred from motions of the Mw7.1 July 5, 2019 Ridgecrest, California earthquake: Bulletin of Earthquake Engineering, v. 19, p. 1797-1814, https://doi.org/10.1007/s10518-021-01053-9.","productDescription":"18 p.","startPage":"1797","endPage":"1814","ipdsId":"IP-119102","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":406956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.28189849853514,\n 34.022786817002\n ],\n [\n -118.21495056152342,\n 34.022786817002\n ],\n [\n -118.21495056152342,\n 34.07768740409027\n ],\n [\n -118.28189849853514,\n 34.07768740409027\n ],\n [\n -118.28189849853514,\n 34.022786817002\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swensen, Dan","contributorId":296724,"corporation":false,"usgs":false,"family":"Swensen","given":"Dan","email":"","affiliations":[{"id":35312,"text":"CGS-CSMIP","active":true,"usgs":false}],"preferred":false,"id":852279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haddadi, Hamid","contributorId":296690,"corporation":false,"usgs":false,"family":"Haddadi","given":"Hamid","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":852280,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218754,"text":"70218754 - 2021 - Re‐purposing groundwater flow models for age assessments: Important characteristics","interactions":[],"lastModifiedDate":"2021-09-14T16:00:16.897383","indexId":"70218754","displayToPublicDate":"2021-02-19T08:37:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Re‐purposing groundwater flow models for age assessments: Important characteristics","docAbstract":"Groundwater flow model construction is often time‐consuming and costly, with development ideally focused on a specific purpose, such as quantifying well capture from water bodies or providing flow fields for simulating advective transport. As environmental challenges evolve, the incentive to re‐purpose existing groundwater flow models may increase. However, few studies have evaluated which characteristics of groundwater flow models deserve greatest consideration when re‐purposing models for groundwater age and advective transport simulations. In this paper, we compare simulated age metrics produced by three MODFLOW‐MODPATH models of the same area but with differing levels of complexity (layering and heterogeneity). Comparisons are made at three watershed scales (HUC 8 to HUC 12). Groundwater age metrics, specifically the young fraction and median age of the young and old fractions, are used for evaluation because they relate to intrinsic susceptibility of aquifers and are simpler to interpret than full age distributions used for advective transport. Results indicate that: 1. the young fraction is less sensitive to model layering than the median age of young and old fractions, suggesting that simple models may suffice for basic intrinsic susceptibility assessments; 2. water table mounding and associated discharge into partially penetrating boundaries, such as head‐water streams, is important for simulating both the young fraction and the median age of the young fraction; and 3. the influence of partially penetrating head‐water streams is maintained regardless of the porosity distribution. Results of this work should aid modelers with evaluating the appropriateness of re‐purposing existing groundwater flow models for age simulations.
In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 m yr−1, respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely (1) diffuse recharge from irrigation and precipitation across the landscape and (2) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 m yr−1) consistent with previous estimates from groundwater age dating in shallow wells (0.38 m yr−1). The saturated zone mean velocity yielded a recharge rate (1.31 m yr−1) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 m yr−1). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7 years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-25-811-2021","usgsCitation":"Wells, M.J., Gilmore, T., Nelson, N., Mittelstet, A., and Bohlke, J., 2021, Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning: Hydrology and Earth System Sciences, v. 25, p. 811-829, https://doi.org/10.5194/hess-25-811-2021.","productDescription":"19 p.","startPage":"811","endPage":"829","ipdsId":"IP-118404","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453386,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-25-811-2021","text":"Publisher Index Page"},{"id":383417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","county":"Scotts Bluff County, Sioux County","otherGeospatial":"Dutch Flats","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.03228759765625,\n 41.27367811566259\n ],\n [\n -102.39257812499999,\n 41.27367811566259\n ],\n [\n -102.39257812499999,\n 42.407234661551875\n ],\n [\n -104.03228759765625,\n 42.407234661551875\n ],\n [\n -104.03228759765625,\n 41.27367811566259\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Martin J.","contributorId":251868,"corporation":false,"usgs":false,"family":"Wells","given":"Martin","email":"","middleInitial":"J.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmore, Troy E.","contributorId":251869,"corporation":false,"usgs":false,"family":"Gilmore","given":"Troy E.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Natalie","contributorId":251870,"corporation":false,"usgs":false,"family":"Nelson","given":"Natalie","affiliations":[{"id":50407,"text":"North Carolina State U","active":true,"usgs":false}],"preferred":false,"id":810737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mittelstet, Aaron","contributorId":251871,"corporation":false,"usgs":false,"family":"Mittelstet","given":"Aaron","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":810739,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218809,"text":"70218809 - 2021 - National-scale reservoir thermal energy storage pre-assessment for the United States","interactions":[],"lastModifiedDate":"2021-03-15T13:24:49.326555","indexId":"70218809","displayToPublicDate":"2021-02-19T08:19:31","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"National-scale reservoir thermal energy storage pre-assessment for the United States","docAbstract":"The U.S. Geological Survey is performing a pre-assessment of the cooling potential for reservoir thermal energy storage (RTES) in five generalized geologic regions (Basin and Range, Coastal Plains, Illinois Basin, Michigan Basin, Pacific Northwest) across the United States. Reservoir models are developed for the metropolitan areas of eight cities (Albuquerque, New Mexico; Charleston, South Carolina; Chicago and Decatur, Illinois; Lansing, Michigan; Memphis, Tennessee; Phoenix, Arizona; and Portland, Oregon) so that computed metrics can be compared to evaluate RTES potential across diverse climates, geologic settings, and physiography. Permeable, semi-confined/confined units that underlie more-utilized aquifers and contain low-quality groundwater are selected for each city. Energy storage metrics are computed for the anticipated total thickness of stratigraphy for which RTES might be feasible, including estimated required well spacing, thermal storage capacity, and thermal recovery efficiency over time. Falta et al. (2016) showed that for a modern 25,000 square-foot (2,323 square-meter), two-story office building, cooling needs exceed heating demand for almost every region of the country. We therefore use Falta et al.’s cooling demand for each city as the representative RTES stress condition for metric computation, allowing comparisons across regions. Results indicate that favorable RTES conditions exist in each region, particularly in the Illinois Basin, Coastal Plains, and Basin and Range. Thermal recovery efficiencies are very high in all regions and increase over time. The thermal storage capacity metric is most informative in the pre-assessment and underscores the importance of mapping reservoir thicknesses and porosities to permit detailed mapping of thermal storage capacity per unit area as a key RTES resource classification standard. This assessment provides a basic understanding of the RTES potential in several metropolitan areas and geologic regions throughout the United States and will aid further evaluation of national RTES efficacy.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 46th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceDate":"February 16-18, 2021","conferenceLocation":"Sanford, California","language":"English","publisher":"Stanford Geothermal Workshop","usgsCitation":"Pepin, J.D., Burns, E., Dickinson, J.E., Duncan, L.L., Kuniansky, E.L., and Reeves, H.W., 2021, National-scale reservoir thermal energy storage pre-assessment for the United States, in Proceedings, 46th workshop on geothermal reservoir engineering, Sanford, California, February 16-18, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-125276","costCenters":[{"id":128,"text":"Arizona Water Science 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0000-0002-5581-0225","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":214542,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":812080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812081,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224974,"text":"70224974 - 2021 - An attention U-Net model for detection of fine-scale hydrologic streamlines","interactions":[],"lastModifiedDate":"2021-10-11T12:42:39.84326","indexId":"70224974","displayToPublicDate":"2021-02-19T07:38:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"An attention U-Net model for detection of fine-scale hydrologic streamlines","docAbstract":"Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.
The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots.
Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods.
While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-021-00091-7","usgsCitation":"Applestein, C., and Germino, M., 2021, Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires: Fire Ecology, v. 17, no. 5, 11 p., https://doi.org/10.1186/s42408-021-00091-7.","productDescription":"11 p.","ipdsId":"IP-121781","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-021-00091-7","text":"Publisher Index Page"},{"id":383586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Idaho, Nevada, Utah","otherGeospatial":"Sagebrush steppe of the Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35546875000001,\n 40.84706035607122\n ],\n [\n -111.357421875,\n 40.84706035607122\n ],\n [\n -111.357421875,\n 44.15068115978094\n ],\n [\n -119.35546875000001,\n 44.15068115978094\n ],\n [\n -119.35546875000001,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-19","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":810810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810811,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218762,"text":"70218762 - 2021 - A 100-km wide slump along the upper slope of the Canadian Arctic was likely preconditioned for failure by brackish pore water flushing","interactions":[],"lastModifiedDate":"2021-03-12T13:47:50.163175","indexId":"70218762","displayToPublicDate":"2021-02-18T07:42:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"A 100-km wide slump along the upper slope of the Canadian Arctic was likely preconditioned for failure by brackish pore water flushing","docAbstract":"Exploration of the continental slope of the Canadian Beaufort Sea has revealed a remarkable coalescence of slide scars with headwalls between 130 and 1100 m water depth (mwd). With increased depth, the scars widen and merge into one gigantic regional slide scar that is more than 100 km wide below ~1100 mwd. To understand the development of these features, five sites were investigated with an Autonomous Underwater Vehicle, which provided 1-m bathymetric grids and Chirp profiles, and surveyed with a Remotely Operated Vehicle. The morphologies are consistent with retrograde failures that occurred on failure planes located between 30 and 75 m below the modern seafloor. At issue is whether the continental slope in this area is preconditioned for failure. While rapid sedimentation during glacial periods, and the presence of shallow gas cannot be ruled out, given the geological environment, it is unclear that they are primary preconditioning factors. Evidence of widespread flushing of the slope with brackish waters, and observed flows of brackish water within slide scars, suggest fluid venting and overpressure may play a role in the development of the extensive slope failures seen along this margin. The impact of pore water salinity changes at the depth of the failure plane on slope stability has not been considered in marine settings previously.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2021.106453","usgsCitation":"Paull, C., Dallimore, S., Caress, D., Gwiazda, R., Lundsten, E., Anderson, K., Melling, H., Jin, Y., Duchesne, M., S-G., K., Kim, S., Riedel, M., King, E., and Lorenson, T., 2021, A 100-km wide slump along the upper slope of the Canadian Arctic was likely preconditioned for failure by brackish pore water flushing: Marine Geology, v. 435, 106453, 16 p., https://doi.org/10.1016/j.margeo.2021.106453.","productDescription":"106453, 16 p.","ipdsId":"IP-118551","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2021.106453","text":"Publisher Index Page"},{"id":384346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Arctic Sea, Canadian Beaufort Sea","volume":"435","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Paull, C. K.","contributorId":255036,"corporation":false,"usgs":false,"family":"Paull","given":"C. K.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":811726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dallimore, S.R.","contributorId":255038,"corporation":false,"usgs":false,"family":"Dallimore","given":"S.R.","affiliations":[{"id":48501,"text":"Geological Survey of Canada (Pacific)","active":true,"usgs":false}],"preferred":false,"id":811727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caress, D.W.","contributorId":255041,"corporation":false,"usgs":false,"family":"Caress","given":"D.W.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":811728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gwiazda, R.","contributorId":255044,"corporation":false,"usgs":false,"family":"Gwiazda","given":"R.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":811729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lundsten, E.","contributorId":255047,"corporation":false,"usgs":false,"family":"Lundsten","given":"E.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":811730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, K.","contributorId":255050,"corporation":false,"usgs":false,"family":"Anderson","given":"K.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":811731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Melling, H.","contributorId":255053,"corporation":false,"usgs":false,"family":"Melling","given":"H.","affiliations":[{"id":51402,"text":"Fisheries and Oceans Canada, Sidney, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":811732,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jin, Y.K.","contributorId":255055,"corporation":false,"usgs":false,"family":"Jin","given":"Y.K.","affiliations":[{"id":51404,"text":"Korea Polar Research Institute, Incheon, South Korea","active":true,"usgs":false}],"preferred":false,"id":811733,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duchesne, M.J.","contributorId":255056,"corporation":false,"usgs":false,"family":"Duchesne","given":"M.J.","email":"","affiliations":[{"id":51406,"text":"Geological Survey of Canada, Quebec, Canada","active":true,"usgs":false}],"preferred":false,"id":811734,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"S-G., Kang","contributorId":255057,"corporation":false,"usgs":false,"family":"S-G.","given":"Kang","email":"","affiliations":[{"id":51404,"text":"Korea Polar Research Institute, Incheon, South Korea","active":true,"usgs":false}],"preferred":false,"id":811735,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kim, S.","contributorId":229605,"corporation":false,"usgs":false,"family":"Kim","given":"S.","affiliations":[{"id":41694,"text":"Department of Civil and Earth Resources Engineering, Kyoto University, Kyoto, Japan","active":true,"usgs":false}],"preferred":false,"id":811736,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Riedel, M.","contributorId":238948,"corporation":false,"usgs":false,"family":"Riedel","given":"M.","affiliations":[{"id":47829,"text":"GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstr. 1 – 3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":811737,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"King, E.L.","contributorId":255058,"corporation":false,"usgs":false,"family":"King","given":"E.L.","affiliations":[{"id":51407,"text":"Geological Survey of Canada, Dartmouth, Canada","active":true,"usgs":false}],"preferred":false,"id":811738,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811739,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70218307,"text":"70218307 - 2021 - A structured approach to remediation site assessment: Lessons from 15 years of fish spawning habitat creation in the St. Clair‐Detroit River system","interactions":[],"lastModifiedDate":"2021-06-30T17:44:58.900933","indexId":"70218307","displayToPublicDate":"2021-02-18T07:26:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A structured approach to remediation site assessment: Lessons from 15 years of fish spawning habitat creation in the St. Clair‐Detroit River system","docAbstract":"Ideally, restoration re‐establishes natural processes in degraded habitats (e.g., flow and sediment regimes). However, in altered systems where process‐based restoration is not feasible, habitat construction is another approach to mitigate degradation. Because habitat construction does not directly focus on restoring processes that build and maintain desired habitats, projects must be developed and placed within the contemporary regulatory, ecological, and hydrogeomorphic context of a system, to maximize effectiveness. Here, we develop a framework for evaluating the regulatory, ecological, and hydrogeomorphic components using 15 years of fish spawning habitat construction in the St. Clair‐Detroit River System. The process began by identifying regulatory requirements at a coarse resolution to quickly focus on locations where ecological potential and hydrogeomorphic constraints could be assessed at finer resolutions. Next, ecological potential was assessed using a lithophilic fish spawning habitat suitability index. The suitability index identified five sites for habitat construction and Lake sturgeon spawning was documented at each site following construction. However, qualitative monitoring showed fine sediments accumulated at older sites. Thus, geomorphic assessments were incorporated to identify sediment sources and model flow within targeted areas. Since geomorphic assessments required the finest resolution and had the most uncertainty, they were conducted after broad‐scale regulatory considerations and ecological assessments narrowed focus to a few candidate sites. The order of operations identified in this case study evolved from the iterative approach of the restoration team, but in retrospect, it helped develop a framework that directed project development resources to aspects with more uncertainty, where learning is most critical.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13359","usgsCitation":"Fischer, J., Roseman, E., Mayer, C., Wills, T., Vaccaro, L., Read, J., Manny, B.A., Kennedy, G.W., Ellison, R., Drouin, R., DeBruyne, R., Cotel, A., Chiotti, J., Boase, J., and Bennion, D., 2021, A structured approach to remediation site assessment: Lessons from 15 years of fish spawning habitat creation in the St. Clair‐Detroit River system: Restoration Ecology, v. 29, no. 4, e13359, 7 p., https://doi.org/10.1111/rec.13359.","productDescription":"e13359, 7 p.","ipdsId":"IP-122141","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":453409,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/rec.13359","text":"External Repository"},{"id":383617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St. Clair‐Detroit River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.7158203125,\n 41.77131167976407\n ],\n [\n -81.4306640625,\n 41.77131167976407\n ],\n [\n -81.4306640625,\n 43.43696596521823\n ],\n [\n -83.7158203125,\n 43.43696596521823\n ],\n [\n -83.7158203125,\n 41.77131167976407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Fischer, J. 0000-0001-7226-6500","orcid":"https://orcid.org/0000-0001-7226-6500","contributorId":240599,"corporation":false,"usgs":false,"family":"Fischer","given":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":810930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":810932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wills, Todd","contributorId":237770,"corporation":false,"usgs":false,"family":"Wills","given":"Todd","affiliations":[{"id":36986,"text":"Michigan Department of Natural 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Center","active":true,"usgs":true}],"preferred":true,"id":810936,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kennedy, Gregory W. 0000-0003-1686-6960 gkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-1686-6960","contributorId":3700,"corporation":false,"usgs":true,"family":"Kennedy","given":"Gregory","email":"gkennedy@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810937,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ellison, Roseanne","contributorId":140057,"corporation":false,"usgs":false,"family":"Ellison","given":"Roseanne","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":810938,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Drouin, Richard","contributorId":70288,"corporation":false,"usgs":false,"family":"Drouin","given":"Richard","email":"","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":810939,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"DeBruyne, Robin 0000-0002-9232-7937","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":240598,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin","affiliations":[{"id":48111,"text":"Univ. Toledo","active":true,"usgs":false}],"preferred":false,"id":810940,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cotel, Aline","contributorId":252831,"corporation":false,"usgs":false,"family":"Cotel","given":"Aline","email":"","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":810941,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Chiotti, Justin A.","contributorId":26629,"corporation":false,"usgs":false,"family":"Chiotti","given":"Justin A.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":810942,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":810943,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bennion, David 0000-0003-4927-4195 dbennion@usgs.gov","orcid":"https://orcid.org/0000-0003-4927-4195","contributorId":149533,"corporation":false,"usgs":true,"family":"Bennion","given":"David","email":"dbennion@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810944,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70219567,"text":"70219567 - 2021 - Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory","interactions":[],"lastModifiedDate":"2021-04-14T12:03:32.609531","indexId":"70219567","displayToPublicDate":"2021-02-18T06:55:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory","docAbstract":"Streams in semi‐arid urban and agricultural environments are often heavily diverted for anthropogenic purposes. However, they simultaneously receive substantial inflows from a variety of ungaged sources including stormwater returns, tile drainage, and irrigation runoff that help sustain flow during dry periods. Due to the inability to identify sources or directly gage many of these inflows, there is a clear need for methods to understand source origination while quantifying potential gains and losses over highly impacted reaches. In the context of the Logan River Observatory, historical gage data illustrate the importance of ungaged and unidentified inflows on maintaining or enhancing flows in both urban and agricultural reaches containing large diversions. To understand the inflows in this portion of the Logan River, we first analysed water samples for ions collected from a subset of representative inflow sources and applied clustering analyses to establish inflow source classifications and associated ion concentration ranges. These representative concentration ranges, combined with mainstem flow and river ion samples taken at sub‐reach scales, allow for the application of flow and mass balances to quantify inflow rates from different sources as well as any losses. These calculations demonstrate significant gains and losses occurring in many sub‐reaches during three sampling events. The dominant land use (urban or agriculture) and flow regime at the time of sampling were the primary drivers of gains and losses. These exchanges were found to be most important below large diversions during low flow conditions. This highlights the need to classify inflow sources (urban or agriculture, surface or groundwater) and estimate their contributions to anticipate instream consequences of land use and water management decisions. As irrigation and water conveyance practices become more efficient, a portion of these ungaged inflows could be diminished or eliminated, thus further depleting streamflow during dry periods.
Kelp forests form an important biogenic habitat that responds to natural and human drivers. Global concerns exist about threats to kelp forests, yet long-term information is limited and research suggests that trends are geographically distinct. We examined distribution of the bull kelp Nereocystis luetkeana over 145 years in South Puget Sound (SPS), a semi-protected inner basin in a fjord estuary complex in the northeast Pacific Ocean. We synthesized 48 historical and modern Nereocystis surveys and examined presence/absence within 1-km segments along 452 km of shoreline. Compared to the earliest baseline in 1878, Nereocystis extent in 2017 decreased 63%, with individual sub-basins showing up to 96% loss. Losses have persisted for decades, across a range of climate conditions. In recent decades, Nereocystis predominantly occurred along shorelines with intense currents and mixing, where temperature and nutrient concentrations did not reach thresholds for impacts to Nereocystis performance, and high current speeds likely excluded grazers. Losses predominated in areas with elevated temperature, lower nutrient concentrations, and relatively low current velocities. The pattern of long-term losses in SPS contrasts with stability in floating kelp abundance during the last century in an area of the Salish Sea with greater wave exposure and proximity to oceanic conditions. These findings support the hypothesis that kelp beds along wave-sheltered shorelines exhibit greater sensitivity to environmental stressors. Additionally, shorelines with strong currents and deep-water mixing may provide refugia within sheltered systems.
","language":"English","publisher":"PLOS ONE","doi":"10.1371/journal.pone.0229703","usgsCitation":"Berry, H., Mumford, T., Calloway, M., Ferrier, L., Christiaen, B., Dowty, P., vanArendonk, N.R., and Grossman, E.E., 2021, Long-term changes in kelp forests in an inner basin of the Salish Sea: PLoS, v. 16, no. 2, e0229703, 27 p., https://doi.org/10.1371/journal.pone.0229703.","productDescription":"e0229703, 27 p.","ipdsId":"IP-116575","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453416,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0229703","text":"Publisher Index Page"},{"id":385440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3984375,\n 46.70973594407157\n ],\n [\n -121.86035156249999,\n 46.70973594407157\n ],\n [\n -121.86035156249999,\n 48.019324184801185\n ],\n [\n -123.3984375,\n 48.019324184801185\n ],\n [\n -123.3984375,\n 46.70973594407157\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, H.D.","contributorId":257840,"corporation":false,"usgs":false,"family":"Berry","given":"H.D.","email":"","affiliations":[{"id":52135,"text":"WA Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mumford, T.F.","contributorId":257841,"corporation":false,"usgs":false,"family":"Mumford","given":"T.F.","email":"","affiliations":[{"id":52136,"text":"Marine Agronomics LLC","active":true,"usgs":false}],"preferred":false,"id":815161,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calloway, M.","contributorId":257844,"corporation":false,"usgs":false,"family":"Calloway","given":"M.","affiliations":[{"id":52135,"text":"WA Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815162,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferrier, L.","contributorId":257845,"corporation":false,"usgs":false,"family":"Ferrier","given":"L.","email":"","affiliations":[{"id":52135,"text":"WA Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815163,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Christiaen, B.","contributorId":257842,"corporation":false,"usgs":false,"family":"Christiaen","given":"B.","email":"","affiliations":[{"id":52135,"text":"WA Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815164,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dowty, P.","contributorId":257843,"corporation":false,"usgs":false,"family":"Dowty","given":"P.","email":"","affiliations":[{"id":52135,"text":"WA Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815165,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815140,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"vanArendonk, Nathan R. 0000-0003-3911-995X","orcid":"https://orcid.org/0000-0003-3911-995X","contributorId":219469,"corporation":false,"usgs":false,"family":"vanArendonk","given":"Nathan","email":"","middleInitial":"R.","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":815141,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221225,"text":"70221225 - 2021 - Stewardship and management of freshwater ecosystems: From Leopold's land ethic to a freshwater ethic","interactions":[],"lastModifiedDate":"2021-06-09T14:16:44.014248","indexId":"70221225","displayToPublicDate":"2021-02-17T06:51:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Stewardship and management of freshwater ecosystems: From Leopold's land ethic to a freshwater ethic","docAbstract":"Managing wildlife in landscapes under private ownership requires partnership between landowners, resource users, and governing agencies. Agencies often call on landowners to voluntarily change their practices to achieve collective goals. Landowner support for management action is partially a function of normative beliefs about managing wildlife. Understanding factors that support development of normative beliefs is important for program design, with implications beyond deer. Drawing on norm activation theory, identity theory, and community attachment, we hypothesized that landowners’ ascription of responsibility to manage deer were a function of their identity as a wildlife steward and attachment to their community. We tested our hypotheses using structural equation modeling with data from a survey of southeast Minnesota landowners. Results revealed ascribed responsibility to be a function of identity. In turn, identity was predicted by affect toward the community. Findings suggest community-based approaches to wildlife management could improve goal achievement.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/08941920.2020.1852636","usgsCitation":"Landon, A.C., Fulton, D.C., Pradhananga, A., Cornicelli, L., and Davenport, M., 2021, Community attachment and stewardship identity influence responsibility to manage wildlife: Society & Natural Resources: An International Journal, v. 34, no. 5, p. 571-584, https://doi.org/10.1080/08941920.2020.1852636.","productDescription":"14 p.","startPage":"571","endPage":"584","ipdsId":"IP-113324","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":396046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Amit","contributorId":279374,"corporation":false,"usgs":false,"family":"Pradhananga","given":"Amit","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":834908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornicelli, Lou","contributorId":279375,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Lou","email":"","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":834909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davenport, Mae A.","contributorId":279376,"corporation":false,"usgs":false,"family":"Davenport","given":"Mae A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":834910,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218215,"text":"70218215 - 2021 - Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest","interactions":[],"lastModifiedDate":"2021-02-19T19:52:58.010283","indexId":"70218215","displayToPublicDate":"2021-02-16T13:48:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest","docAbstract":"Mountain ranges generate clouds, precipitation, and perennial streamflow for water supplies, but the role of forest cover in mountain hydrometeorology and cloud formation is not well understood. In the Luquillo Experimental Forest of Puerto Rico, mountains are immersed in clouds nightly, providing a steady precipitation source to support the tropical forest ecosystems and human uses. A severe drought in 2015 and the removal of forest canopy (defoliation) by Hurricane Maria in 2017 created natural experiments to examine interactions between the living forest and hydroclimatic processes. These unprecedented land-based observations over 4.5 y revealed that the orographic cloud system was highly responsive to local land-surface moisture and energy balances moderated by the forest. Cloud layer thickness and immersion frequency on the mountain slope correlated with antecedent rainfall, linking recycled terrestrial moisture to the formation of mountain clouds; and cloud-base altitude rose during drought stress and posthurricane defoliation. Changes in diurnal cycles of temperature and vapor-pressure deficit and an increase in sensible versus latent heat flux quantified local meteorological response to forest disturbances. Temperature and water vapor anomalies along the mountain slope persisted for at least 12 mo posthurricane, showing that understory recovery did not replace intact forest canopy function. In many similar settings around the world, prolonged drought, increasing temperatures, and deforestation could affect orographic cloud precipitation and the humans and ecosystems that depend on it.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2021646118","usgsCitation":"Scholl, M.A., Bassiouni, M., and Torres-Sanchez, A.J., 2021, Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest: PNAS, v. 118, no. 7, e2021646118, 8 p., https://doi.org/10.1073/pnas.2021646118.","productDescription":"e2021646118, 8 p.","ipdsId":"IP-117419","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":453423,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7896295","text":"Publisher Index Page"},{"id":436507,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UQCN4T","text":"USGS data release","linkHelpText":"Temperature, relative humidity and cloud immersion data for Luquillo Mountains, eastern Puerto Rico, 2014-2019"},{"id":383390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.94543457031249,\n 18.152376614237202\n ],\n [\n -65.5718994140625,\n 18.152376614237202\n ],\n [\n -65.5718994140625,\n 18.449649414656722\n ],\n [\n -65.94543457031249,\n 18.449649414656722\n ],\n [\n -65.94543457031249,\n 18.152376614237202\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Scholl, Martha A. 0000-0001-6994-4614 mascholl@usgs.gov","orcid":"https://orcid.org/0000-0001-6994-4614","contributorId":1920,"corporation":false,"usgs":true,"family":"Scholl","given":"Martha","email":"mascholl@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":810446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bassiouni, Maoya 0000-0001-5795-9894","orcid":"https://orcid.org/0000-0001-5795-9894","contributorId":251732,"corporation":false,"usgs":false,"family":"Bassiouni","given":"Maoya","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":810447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torres-Sanchez, Angel J. 0000-0002-5595-021X ajtorres@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-021X","contributorId":5623,"corporation":false,"usgs":true,"family":"Torres-Sanchez","given":"Angel","email":"ajtorres@usgs.gov","middleInitial":"J.","affiliations":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810448,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219485,"text":"70219485 - 2021 - Shade, light, and stream temperature responses to riparian thinning in second-growth redwood forests of northern California","interactions":[],"lastModifiedDate":"2021-04-12T11:51:35.478377","indexId":"70219485","displayToPublicDate":"2021-02-16T06:57:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Shade, light, and stream temperature responses to riparian thinning in second-growth redwood forests of northern California","docAbstract":"Resource managers in the Pacific Northwest (USA) actively thin second-growth forests to accelerate the development of late-successional conditions and seek to expand these restoration thinning treatments into riparian zones. Riparian forest thinning, however, may impact stream temperatures–a key water quality parameter often regulated to protect stream habitat and aquatic organisms. To better understand the effects of riparian thinning on shade, light, and stream temperature, we employed a manipulative field experiment following a replicated Before-After-Control-Impact (BACI) design in three watersheds in the redwood forests of northern California, USA. Thinning treatments were intended to reduce canopy closure or basal area within the riparian zone by up to 50% on both sides of the stream channel along a 100–200 m stream reach. We found that responses to thinning ranged widely depending on the intensity of thinning treatments. In the watersheds with more intensive treatments, thinning reduced shade, increased light, and altered stream thermal regimes in thinned and downstream reaches. Thinning shifted thermal regimes by increasing maximum temperatures, thermal variability, and the frequency and duration of elevated temperatures. These thermal responses occurred primarily during summer but also extended into spring and fall. Longitudinal profiles indicated that increases in temperature associated with thinning frequently persisted downstream, but downstream effects depended on the magnitude of upstream temperature increases. Model selection analyses indicated that local changes in shade as well as upstream thermal conditions and proximity to upstream treatments explained variation in stream temperature responses to thinning. In contrast, in the study watershed with less intensive thinning, smaller changes in shade and light resulted in minimal stream temperature responses. Collectively, our data shed new light on the stream thermal responses to riparian thinning. These results provide relevant information for managers considering thinning as a viable restoration strategy for second-growth riparian forests.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0246822","usgsCitation":"Roon, D., Dunham, J.B., and Groom, J.D., 2021, Shade, light, and stream temperature responses to riparian thinning in second-growth redwood forests of northern California: PLoS ONE, v. 16, no. 2, e0246822, 25 p., https://doi.org/10.1371/journal.pone.0246822.","productDescription":"e0246822, 25 p.","ipdsId":"IP-124305","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453427,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0246822","text":"Publisher Index Page"},{"id":384959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Redwood National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21142578125,\n 41.50034959128928\n ],\n [\n -123.651123046875,\n 41.50034959128928\n ],\n [\n -123.651123046875,\n 42.00032514831621\n ],\n [\n -124.21142578125,\n 42.00032514831621\n ],\n [\n -124.21142578125,\n 41.50034959128928\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Roon, David","contributorId":257063,"corporation":false,"usgs":false,"family":"Roon","given":"David","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":813772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":813773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Groom, Jeremiah D","contributorId":257065,"corporation":false,"usgs":false,"family":"Groom","given":"Jeremiah","email":"","middleInitial":"D","affiliations":[{"id":51978,"text":"Groom Analytics, LLC","active":true,"usgs":false}],"preferred":false,"id":813774,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228581,"text":"70228581 - 2021 - Modeling how to achieve localized areas of reduced white-tailed deer density","interactions":[],"lastModifiedDate":"2022-02-14T21:08:01.739762","indexId":"70228581","displayToPublicDate":"2021-02-15T15:00:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modeling how to achieve localized areas of reduced white-tailed deer density","docAbstract":"Localized management of white-tailed deer (Odocoileus virginianus) involves the removal of matriarchal family units with the intent to create areas of reduced deer density. However, application of this approach has not always been successful, possibly because of female dispersal and high deer densities. We developed a spatially explicit, agent-based model to investigate the intensity of deer removal required to locally reduce deer density depending on the surrounding deer density, dispersal behavior, and size and shape of the area of localized reduction. Application of this model is illustrated using the example of abundant deer populations in Pennsylvania, USA. Most scenarios required at least 5 years before substantial deer density reductions occurred. Our model indicated that a localized reduction was successful for scenarios in which the surrounding deer density was lowest (30 deer/mi²), localized antlerless harvest rates were 30%, and the removal area was 5 mi² or larger. When the size of the removal area was < 5 mi2, end population density was highly variable and, in some scenarios, exceeded the initial density. The shape of the area of localized reduction had less influence on the ability to reduce deer density than the size. There were no differences in mean deer density in the same size circle or square removal areas. Similarly, increasing the ratio of sides (length : width) in rectangular removal areas had little influence on the ability to locally reduce deer densities. Situations in which deer density was higher (40 or 50 deer/mi2) required antlerless removal rates to exceed 30% and took more than 5 years to considerably reduce density in the localized area regardless of its size. These results indicate that the size of the area of reduction, surrounding deer density, and antlerless harvest rate are the most influential factors in locally reducing deer density. Therefore, localized management likely can be an effective strategy for lower density herds, especially in larger removal areas. For high density herds, the success of this strategy would depend most on the ability of resource managers to achieve consistently high antlerless harvest rates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2020.109393","usgsCitation":"Van Buskirk, A.N., Rosenberry, C., Wallingford, B., Domoto, E.J., McDill, M., Drohan, P., and Diefenbach, D.R., 2021, Modeling how to achieve localized areas of reduced white-tailed deer density: Ecological Modelling, v. 442, 109393, 13 p., https://doi.org/10.1016/j.ecolmodel.2020.109393.","productDescription":"109393, 13 p.","ipdsId":"IP-118247","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"442","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Buskirk, Amanda N.","contributorId":276219,"corporation":false,"usgs":false,"family":"Van Buskirk","given":"Amanda","email":"","middleInitial":"N.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Christopher S.","contributorId":276220,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":834675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallingford, Bret D.","contributorId":276221,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret D.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":834676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Domoto, Emily Just","contributorId":276222,"corporation":false,"usgs":false,"family":"Domoto","given":"Emily","email":"","middleInitial":"Just","affiliations":[{"id":37212,"text":"Pennsylvania Department of Conservation and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":834677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDill, Marc E.","contributorId":276223,"corporation":false,"usgs":false,"family":"McDill","given":"Marc E.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drohan, Patrick","contributorId":276224,"corporation":false,"usgs":false,"family":"Drohan","given":"Patrick","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834679,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":834673,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217896,"text":"sir20205110 - 2021 - Geologic assessment of undiscovered oil and gas resources in the Cherokee Platform area of Kansas, Oklahoma, and Missouri","interactions":[],"lastModifiedDate":"2021-04-01T15:49:52.891672","indexId":"sir20205110","displayToPublicDate":"2021-02-15T11:15:00","publicationYear":"2021","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":"2020-5110","displayTitle":"Geologic Assessment of Undiscovered Oil and Gas Resources in the Cherokee Platform Province Area of Kansas, Oklahoma, and Missouri","title":"Geologic assessment of undiscovered oil and gas resources in the Cherokee Platform area of Kansas, Oklahoma, and Missouri","docAbstract":"In 2015, the U.S. Geological Survey completed a geology-based assessment to estimate the volumes of undiscovered, technically recoverable petroleum resources in the Cherokee Platform Province area of southeastern Kansas, northeastern Oklahoma, and southwestern Missouri. The U.S. Geological Survey identified four stratigraphic intervals that contain petroleum source rocks: (1) thin shales in the Middle to Upper Ordovician Simpson Group, (2) shales within the Upper Devonian to Lower Mississippian Woodford Shale and stratigraphically equivalent Chattanooga Shale, (3) coals and coal-associated shales and mudstones in the Middle Pennsylvanian (Desmoinesian) Cherokee and Marmaton Groups, and (4) thin marine shales within the Marmaton Group and the Upper Pennsylvanian (Missourian) Kansas City and Lansing Groups. Based on the nature of the petroleum accumulations, the characterization of the compositions and thermal maturity of the organic matter in the rocks, and the compositions of the produced petroleum, the U.S. Geological Survey identified three total petroleum systems (TPS) containing four assessment units (AU): the Paleozoic Composite TPS with the Paleozoic Conventional Assessment Unit (AU), the Woodford/Chattanooga TPS with the Woodford Shale Oil AU and the Woodford Biogenic Gas AU, and the Desmoinesian Coal TPS with the Desmoinesian Coalbed Gas AU. Assessment unit summaries follow
1. Three source rock intervals have contributed geochemically distinct oils to reservoirs within the Paleozoic Conventional AU. These intervals are the Simpson Group; the Woodford and Chattanooga Shales; and the Marmaton, Kansas City, and Lansing Groups. The major petroleum source rocks are the Woodford and Chattanooga Shales. The Paleozoic Conventional AU includes reservoirs that range in age from the Upper Cambrian Arbuckle Group to the lower Permian Chase Group. Most oil production in the province has been from Pennsylvanian sandstone reservoirs. Estimated undiscovered petroleum resources for this AU are a mean of 3 million barrels of oil (MMBO), 140 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL).
2. The Woodford Shale Oil AU contains undiscovered continuous petroleum resources within the Woodford Shale and Chattanooga Shale. The geologic model for the AU assumes that petroleum resources remain trapped within the shale following petroleum migration. For most of the AU, organic matter within the Woodford Shale and Chattanooga Shale is thermally mature with respect to petroleum generation as shown by vitrinite reflectance values between 0.6 and 1 percent. Petroleum has been produced from the Woodford Shale and Chattanooga Shale. Estimated undiscovered petroleum resources for this AU are means of 460 MMBO, 640 BCFG, and 7 MMBNGL.
3. The Woodford Shale Biogenic Gas AU contains undiscovered continuous petroleum resources in the east-central portion of the Cherokee Platform Province near the Ozark uplift where the Woodford Shale and Chattanooga Shale are at depths of 1,250 ft or shallower. At those depths, methanogenesis and(or) biodegradation of thermogenic natural gases can be found where the shale may be more fractured and more susceptible to groundwater penetrations. The mean assessed volume of undiscovered gas for this assessment unit is 416 BCFG and 1 MMBNGL.
4. The Desmoinesian Coalbed Gas AU contains undiscovered continuous petroleum resources within the Middle Pennsylvanian coals and coal-associated shales and mudstones. The boundaries for the Desmoinesian Coalbed Gas AU are, in part, defined by the extent, depth, and thickness of the coals. Within the Desmoinesian Coalbed Gas AU, a sweet spot area was delineated based on a 10 foot or greater net coal thickness. Gas analytical data show that natural gas produced from the coals has a mixed biogenic and thermogenic origin and that there is significant migration of natural gas into the coals from adjacent conventional sandstone reservoirs. The estimated mean volume of undiscovered gas is 10.0 trillion cubic ft of gas (TCFG), and 23 MMBNGL.
For the three continuous (unconventional) assessment units and one conventional assessment unit in the Cherokee Platform Province, total mean volumes of undiscovered petroleum resources are estimated to be 463 MMBO, 11.2 TCFG and 35 MMBNGL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205110","issn":"978-1-4113-4399-3","usgsCitation":"Drake, R.M., II, and Hatch, J.R., 2021, Geologic assessment of undiscovered oil and gas resources in the Cherokee Platform area of Kansas, Oklahoma, and Missouri: U.S. Geological Survey Scientific Investigations Report 2020–5110, 39 p., https://doi.org/10.3133/sir20205110.","productDescription":"viii, 39 p.","onlineOnly":"N","ipdsId":"IP-069652","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":383204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5110/sir20205110.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5110"},{"id":383203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5110/coverthb2.jpg"}],"country":"United States","state":"Kansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.2392578125,\n 33.43144133557529\n ],\n [\n -93.251953125,\n 33.578014746143985\n ],\n [\n -93.2958984375,\n 40.04443758460856\n ],\n [\n -100.0634765625,\n 40.04443758460856\n ],\n [\n -100.2392578125,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before‐after‐control‐impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial‐temporal dynamics can violate core assumptions of BACI‐type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACI failed to assess very localized impacts, and had low power but high precision when assessing region‐wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2304","usgsCitation":"Rassweiler, A., Okamoto, D.K., Reed, D.C., Kushner, D.J., Schroeder, D., and Lafferty, K.D., 2021, Improving the ability of a BACI design to detect impacts within a kelp‐forest community: Ecological Applications, v. 31, no. 4, e02304, 15 p., https://doi.org/10.1002/eap.2304.","productDescription":"e02304, 15 p.","ipdsId":"IP-118865","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":384711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rassweiler, Andrew 0000-0002-8760-3888","orcid":"https://orcid.org/0000-0002-8760-3888","contributorId":203606,"corporation":false,"usgs":false,"family":"Rassweiler","given":"Andrew","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":813105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okamoto, Daniel K","contributorId":256705,"corporation":false,"usgs":false,"family":"Okamoto","given":"Daniel","email":"","middleInitial":"K","affiliations":[{"id":51835,"text":"Department of Biological Science, Florida State University, Tallahassee, Florida, 32306 USA","active":true,"usgs":false}],"preferred":false,"id":813106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Daniel C.","contributorId":203607,"corporation":false,"usgs":false,"family":"Reed","given":"Daniel","email":"","middleInitial":"C.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":813107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kushner, David J","contributorId":256706,"corporation":false,"usgs":false,"family":"Kushner","given":"David","email":"","middleInitial":"J","affiliations":[{"id":51836,"text":"Channel Islands National Park, Ventura, California, 93001 USA","active":true,"usgs":false}],"preferred":false,"id":813108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Donna M","contributorId":256707,"corporation":false,"usgs":false,"family":"Schroeder","given":"Donna M","affiliations":[{"id":51837,"text":"Bureau of Ocean Energy Management, Pacific OCS Region, 760 Paseo Camarillo, Camarillo, California, 93010 USA","active":true,"usgs":false}],"preferred":false,"id":813109,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813110,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218783,"text":"70218783 - 2021 - The role of hydrates, competing chemical constituents, and surface composition on CLNO2 formation","interactions":[],"lastModifiedDate":"2021-03-12T13:46:18.062877","indexId":"70218783","displayToPublicDate":"2021-02-15T07:45:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7760,"text":"Environmental Science Technology","active":true,"publicationSubtype":{"id":10}},"title":"The role of hydrates, competing chemical constituents, and surface composition on CLNO2 formation","docAbstract":"Atomic chlorine (Cl•) affects air quality and atmospheric oxidizing capacity. Nitryl chloride (ClNO2) – a common Cl• source–forms when chloride-containing aerosols react with dinitrogen pentoxide (N2O5). A recent study showed that saline lakebed (playa) dust is an inland source of particulate chloride (Cl–) that generates high ClNO2. However, the underlying physiochemical factors responsible for observed yields are poorly understood. To elucidate these controlling factors, we utilized single particle and bulk techniques to determine the chemical composition and mineralogy of playa sediment and dust samples from the southwest United States. Single particle analysis shows trace highly hygroscopic magnesium and calcium Cl-containing minerals are present and likely facilitate ClNO2 formation at low humidity. Single particle and mineralogical analysis detected playa sediment organic matter that hinders N2O5 uptake as well as 10 Å-clay minerals (e.g., Illite) that compete with water and chloride for N2O5. Finally, we show that the composition of the aerosol surface, rather than the bulk, is critical in ClNO2 formation. These findings underscore the importance of mixing state, competing reactions, and surface chemistry on N2O5 uptake and ClNO2 yield for playa dusts and, likely, other aerosol systems. Therefore, consideration of particle surface composition is necessary to improve ClNO2 and air quality modeling.
Demographic models enhance understanding of drivers of population growth and inform conservation efforts to prevent population declines and extinction. For species with complex life histories, however, parameterizing demographic models is challenging because some life stages can be difficult to study directly. Integrated population models (IPMs) empower researchers to estimate vital rates for organisms that have cryptic or widely dispersing early life stages by integrating multiple demographic data sources. For a stream‐inhabiting frog (Rana boylii) that is declining through much of its range in Oregon and California, USA, we collected egg‐mass counts and capture–mark–recapture data on adults from two populations in California to fit IPMs that estimate adult abundance and the survival rate of both marked and unobserved life stages. Estimates of adult abundance based on long‐term monitoring of egg‐mass counts showed that study populations fluctuated greatly inter‐annually but were stable at longer timescales (i.e., decades). Adult female survival during 5–6 yr of capture–mark–recapture study periods was nearly equal in each population. Survival rate of R. boylii eggs to the subadult stage is low on average (0.002) but highly variable among years depending on post‐oviposition stream flow. Population viability analysis showed that survival of adult and subadult life stages has the greatest proportional effect on population growth; the survival of egg and tadpole life stages, however, is more malleable by management interventions. For example, simulations showed head‐starting of tadpoles, salvaging stranded egg masses, and limiting aseasonal pulsed flows could dramatically reduce the threat of extirpation. This study demonstrates the value of integrating multiple demographic data sources to construct models of population dynamics in species with complex life histories.
Private landowners are important actors in landscape-level wildfire risk management. Accordingly, wildfire programs and policy encourage wildland–urban interface homeowners to engage with local organizations to properly mitigate wildfire risk on their parcels. We investigate whether parcel-level wildfire risk assessment data, commonly used to inform community-level planning and resource allocation, can be used to “nudge” homeowners to engage further with a regional wildfire organization. We sent 4564 households in western Colorado a letter that included varying combinations of risk information about their community, their parcels, and their neighbors’ parcels, and we measured follow-up visits to a personalized “Web site”. We find that the effect of providing parcel-specific information depends on baseline conditions: Informing homeowners about their property’s wildfire risk increases information-seeking among homeowners of the highest-risk parcels by about 5 percentage points and reduces information-seeking among homeowners of lower-risk parcels by about 6 percentage points. Parcel-specific information also increases the overall response in the lowest risk communities by more than 10 percentage points. Further, we find evidence of a 6-percentage point increase in response rate associated with receiving a social comparison treatment that signals neighboring properties as being either low or moderate risk on average. These results, especially considered against the 13 percent overall average response rate, offer causal evidence that providing parcel-specific wildfire risk information can influence behavior. As such, we demonstrate the effectiveness of simple outreach in engaging wildland–urban interface homeowners with wildfire risk professionals in ways that leverage existing data.