The National Cooperative Geologic Mapping Program (NCGMP) is publishing a strategic plan titled Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge (Brock and others, in press). The plan provides a vision, mission, and goals for the program during the years 2020–2030, which are:
In order to achieve the goals outlined in the strategic plan, the NCGMP has developed an implementation plan. This plan will guide the annual review of projects carried out by USGS staff (FEDMAP) described in the plan and the development of the annual FEDMAP prospectus that will ensure the effective application of the NCGMP strategy.
This publication describes the implementation plan of the NCGMP strategy for the southern Pacific Border and Sierra-Cascade Mountains provinces, as defined by Fenneman (1917, 1928, and 1946). This implementation plan focuses on the geology of California and a sliver of Nevada surrounding Lake Tahoe. The southern Pacific Border and Sierra-Cascade Mountains provinces encompass the varied landscapes of the high Sierra Nevada, the Central Valley, and Coast Ranges in northern and central California and the Peninsular Ranges, Continental Borderland, Los Angeles Basin-San Gabriel-San Bernardino valleys, western and central Transverse Ranges, and northernmost Salton Trough in southern California. Societal demands create a need for earth-science data in each of these landscapes. The broader San Francisco Bay area, Central Valley, Los Angeles-San Gabriel-San Bernardino lowlands, and the coastal lowlands that border the Peninsular Ranges are densely populated (about 30 million people) areas at high risk of natural hazards. The mountains of the Sierra Nevada, Peninsular Ranges, and Transverse Ranges, and the coast all provide numerous recreational opportunities that attract visitors from around the world, whereas previously these ranges attracted people to mine their resources. The agricultural capacity of the Central Valley is a critical resource for the Nation that is increasingly water limited.
The southern. Pacific Border and Sierra-Cascade Mountains provinces, at the edge of the North American continent, were profoundly influenced by subduction zone tectonics during the Mesozoic and early Cenozoic (ongoing in northernmost California) and subsequently by the inception, development, and present activity of the San Andreas transform margin system. Although the geology of this region is the poster child of fundamental conceptual models of subduction zone complexes, forearc basins, ophiolite obductions, magmatic arcs, and suspect terranes, as well as hosting one of Earth’s most notorious continental transform faults—the San Andreas Fault—important questions that have important societal consequences remain to be answered. Most of California’s population reside in these provinces and live within 30 miles of an active fault (according to www.earthquakeauthority.com) yet new faults continue to be discovered, highlighting the importance of deformation off the main San Andreas Fault. Bedrock, surficial, and three-dimensional (3D) geologic maps depicting stratigraphic structure and depth to crystalline basement rocks provide critical context and information for understanding fault rupture, distributed deformation, fault connectivity, and history in addition to providing crucial data that enable forecasting of shaking amplitude and length from hypothetical earthquake scenarios.
The tectonic evolution of California produced not only stunning mountains, with associated hazards from landslides and active volcanoes, but also fertile valleys that make California the top agricultural producer in the country in terms of cash receipts (according to www.ers.usda.gov/faqs). These valleys lie atop large basins that not only store groundwater but, in many cases, host oil and gas fields, contributing to the fourth highest hydrocarbon production by State in the country in 2016 (according to https://www.aei.org/carpe-diem/animated-chart-of-us-oil-production-by-state-1981-2017). Water is a key resource increasingly stressed by growing agricultural, industrial, and residential needs. Warmer and drier conditions have led to an increased reliance on extracting groundwater resources, whose availability and quality are dictated at the first order by the 3D spatial distribution of bedrock and Quaternary surficial deposits. Thus, assessment of this critical resource is inextricably tied to knowledge of the surficial and subsurface geologic structure and material types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211012","usgsCitation":"Langenheim, V.E., Graymer, R.W., Powell, R.E., Schmidt, K.M., and Sweetkind, D.S., 2021, Implementation plan for the southern Pacific Border and Sierra-Cascade Mountains provinces: U.S. Geological Survey Open-File Report 2021–1012, 11 p., https://doi.org/10.3133/ofr20211012.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-121693","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":384840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1012/ofr20211012.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1012/covrthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.69486597787505\n ],\n [\n -117.20214843749999,\n 34.415973384481866\n ],\n [\n -116.806640625,\n 36.491973470593685\n ],\n [\n -119.35546875000001,\n 38.34165619279595\n ],\n [\n -119.3115234375,\n 39.30029918615029\n ],\n [\n -120.10253906249999,\n 40.212440718286466\n ],\n [\n -121.86035156249999,\n 42.06560675405716\n ],\n [\n -124.3212890625,\n 42.06560675405716\n ],\n [\n -124.541015625,\n 40.51379915504413\n ],\n [\n -123.70605468750001,\n 38.71980474264237\n ],\n [\n -122.607421875,\n 37.19533058280065\n ],\n [\n -121.59667968749999,\n 35.817813158696616\n ],\n [\n -120.58593749999999,\n 34.45221847282654\n ],\n [\n -117.94921874999999,\n 33.54139466898275\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -115.26855468749999,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Using a before-after study design in a stable, largely undisturbed pond habitat and a dataset spanning 33 years, we document and describe the decline of native Sonora mud turtles (Kinosternon sonoriense) after the introduction of non-native pond sliders (Trachemys scripta). The Sonora mud turtle population in Montezuma Well in central Arizona, USA, declined to less than 25% of previous numbers, from 372 ± 64 in 1983 to 80 ± 21 in 2011. We trapped and removed the non-native turtles between 2007 and 2012 and after removal of the non-natives, the Sonora mud turtle population increased to 139 ± 34 in 2015. The native turtles also significantly increased basking activity after removal of the non-natives, paralleling results of small-scale mesocosm studies showing that pond sliders negatively affect basking rates of native turtle species. Reproductive rates of female Sonora mud turtles (numbers of females with eggs) were lower during the period of peak non-native turtle abundance, and increased after removal of the non-native turtles. We hypothesize that the reduction in effective reproductive rate links interference competition (reflected in reduced basking rates) to the long-term decline of the native mud turtles. Results from the undisturbed natural system of Montezuma Well provide new insights on the overall occurrence, magnitude, and mechanisms of negative effects of introduced pond sliders on native turtle species. Sonora mud turtles are very different in their morphology, behavior, and ecology from pond sliders and from native turtles in other studies, suggesting that impacts of non-native pond sliders are more pervasive than previously thought.
","language":"English","publisher":"Reabic","doi":"10.3391/ai.2021.16.3.10","usgsCitation":"Drost, C.A., Lovich, J.E., Rosen, P.C., Malone, M., and Garber, S.D., 2021, Non-native Pond Sliders cause long-term decline of native Sonora Mud Turtles: A 33-year before-after study in an undisturbed natural environment: Aquatic Invasions, v. 16, no. 3, p. 542-570, https://doi.org/10.3391/ai.2021.16.3.10.","productDescription":"29 p.","startPage":"542","endPage":"570","ipdsId":"IP-101856","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":452811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/ai.2021.16.3.10","text":"Publisher Index Page"},{"id":436421,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EL65UI","text":"USGS data release","linkHelpText":"Sonora Mud Turtles and non-native turtles, Montezuma Well, Yavapai County, Arizona, 1983 - 2015"},{"id":387893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":821113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":821114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosen, Philip C.","contributorId":70311,"corporation":false,"usgs":true,"family":"Rosen","given":"Philip","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":821132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malone, Matthew","contributorId":264216,"corporation":false,"usgs":false,"family":"Malone","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":821133,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garber, Steven D.","contributorId":264217,"corporation":false,"usgs":false,"family":"Garber","given":"Steven","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":821134,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229019,"text":"70229019 - 2021 - Climate change may cause shifts in growth and instantaneous natural mortality of American Shad throughout their native range","interactions":[],"lastModifiedDate":"2022-02-25T13:01:46.692613","indexId":"70229019","displayToPublicDate":"2021-04-05T06:57:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Climate change may cause shifts in growth and instantaneous natural mortality of American Shad throughout their native range","docAbstract":"American Shad Alosa sapidissima is an anadromous species with populations ranging along the U.S. Atlantic coast. Past American Shad stock assessments have been data limited and estimating system-specific growth parameters or instantaneous natural mortality (M) was not possible. This precluded system-specific stock assessment and management due to reliance on these parameters for estimating other population dynamics (such as yield per recruit). Furthermore, climate-informed biological reference points remain a largely unaddressed need in American Shad stock assessment. Population abundance estimates of American Shad and other species often rely heavily on M derived from von Bertalanffy growth function (VBGF) parameters. Therefore, we developed Bayesian hierarchical models to estimate coastwide, regional, and system-specific VBGF parameters and M using data collected from 1982 to 2017. We tested predictive performance of models that included effects of various climate variables on VBGF parameters within these models. System-specific models were better supported than regional or coast-wide models. Mean asymptotic length (L∞) decreased with increasing mean annual sea surface temperature (SST) and degree days (DD) experienced by fish during their lifetime. Although uncertain, K (Brody growth coefficient) decreased over the same range of lifetime SST and DD. Assuming no adaptation, we projected changes in VBGF parameters and M through 2099 using modeled SST from two climate projection scenarios (Representative Concentration Pathways 4.5 and 8.5). We predicted reduced growth under both scenarios, and M was projected to increase by about 0.10. It is unclear how reduced growth and increased mortality may influence population productivity or life history adaptation in the future, but our results may inform stock assessment models to assess those trade-offs.
In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range-Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits.
Northeastern United States
Carnivora
We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6-year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient.
We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats (Lynx rufus) and coyotes (Canis latrans) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx (Lynx canadensis), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species.
We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community-wide hypotheses at macroecological scales and identify coarse-scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14112","usgsCitation":"Siren, A., Sutherland, C., Bernier, C., Royar, K., Kilborn, J.R., Callahan, C., Cliche, R., Prout, L.S., and Morelli, T.L., 2021, Abiotic stress and biotic factors mediate range dynamics on opposing edges: Journal of Biogeography, v. 48, no. 7, p. 1758-1772, https://doi.org/10.1111/jbi.14112.","productDescription":"15 p.","startPage":"1758","endPage":"1772","ipdsId":"IP-125344","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":452813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.14112","text":"Publisher Index Page"},{"id":410693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.47563781677009,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 42.221428132868596\n ],\n [\n -69.90726541446244,\n 45.75938677061683\n ],\n [\n -74.47563781677009,\n 45.75938677061683\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Siren, Alexej P. K.","contributorId":236810,"corporation":false,"usgs":false,"family":"Siren","given":"Alexej P. K.","affiliations":[],"preferred":false,"id":859342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sutherland, Christopher","contributorId":300051,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","affiliations":[{"id":65006,"text":"University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":859343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernier, Chris","contributorId":300052,"corporation":false,"usgs":false,"family":"Bernier","given":"Chris","email":"","affiliations":[{"id":65007,"text":"Vermont Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":859344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royar, Kimberly","contributorId":300053,"corporation":false,"usgs":false,"family":"Royar","given":"Kimberly","email":"","affiliations":[{"id":65007,"text":"Vermont Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":859345,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kilborn, Jillian R.","contributorId":236780,"corporation":false,"usgs":false,"family":"Kilborn","given":"Jillian","email":"","middleInitial":"R.","affiliations":[{"id":47548,"text":"Universidad de La Frontera, Temuco, Chile","active":true,"usgs":false}],"preferred":false,"id":859346,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Catherine","contributorId":236779,"corporation":false,"usgs":false,"family":"Callahan","given":"Catherine","email":"","affiliations":[{"id":47548,"text":"Universidad de La Frontera, Temuco, Chile","active":true,"usgs":false}],"preferred":false,"id":859347,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cliche, Rachel","contributorId":300056,"corporation":false,"usgs":false,"family":"Cliche","given":"Rachel","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":859348,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prout, Leighlan S.","contributorId":300057,"corporation":false,"usgs":false,"family":"Prout","given":"Leighlan","middleInitial":"S.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":859349,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859350,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263866,"text":"70263866 - 2021 - Improving paleoseismic earthquake magnitude estimates with rupture length information: Application to the Puget Lowland, Washington State, U.S.A.","interactions":[],"lastModifiedDate":"2025-02-27T14:15:14.906206","indexId":"70263866","displayToPublicDate":"2021-04-02T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Improving paleoseismic earthquake magnitude estimates with rupture length information: Application to the Puget Lowland, Washington State, U.S.A.","docAbstract":"Both earthquake displacement and rupture length correlate with magnitude, and therefore observations of each from past earthquakes can be used to estimate the magnitude of those earthquakes in the absence of instrumental records. We extend the Bayesian inversion method of Biasi and Weldon (2006), which estimates paleoearthquake magnitude from displacement observations, to incorporate both rupture length and surface displacement measurements into the magnitude inversion. We then use this method on 27 late Pleistocene to Holocene paleoearthquakes in the Puget Lowland region of Washington. Observations of (typically vertical) fault separation per event range from 0.6 to 7 m, implying net displacement per event of up to 10 ± 4 m for the largest event. Rupture lengths are estimated to vary between the smallest contiguous mapped scarps to the full extent of the faults mapped from geology and geophysical observations. Although a few of the ruptures may be longer than 150 km, the ruptures have a median of 53 km, indicating that earthquakes in the Puget Lowland have relatively high displacement to length ratios. By considering both datasets, we find that all events were between M 6.3 and 7.5, generally consistent with the expected seismicity from the USGS National Seismic Hazard Map for the region. The simultaneous use of both length and displacement data in the magnitude inversion decreases both the estimated earthquake magnitudes and the uncertainty. The magnitude reduction in particular is due to the relatively short rupture lengths possible for Puget Lowland faults. This implies a decrease in the seismic hazard (relative to a displacement-only assessment) to a highly populated and rapidly urbanizing region.","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0120200193","usgsCitation":"Styron, R., and Sherrod, B.L., 2021, Improving paleoseismic earthquake magnitude estimates with rupture length information: Application to the Puget Lowland, Washington State, U.S.A.: Bulletin of the Seismological Society of America, v. 111, no. 2, p. 1139-1153, https://doi.org/10.1785/0120200193.","productDescription":"15 p.","startPage":"1139","endPage":"1153","ipdsId":"IP-097868","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Lowland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.78690580010414,\n 49.01865333869071\n ],\n [\n -124.78690580010414,\n 47.31494250173583\n ],\n [\n -121.8722817961621,\n 47.31494250173583\n ],\n [\n -121.8722817961621,\n 49.01865333869071\n ],\n [\n -124.78690580010414,\n 49.01865333869071\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"111","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Styron, Richard","contributorId":201082,"corporation":false,"usgs":false,"family":"Styron","given":"Richard","email":"","affiliations":[],"preferred":false,"id":928755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928756,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237363,"text":"70237363 - 2021 - Graph-based reinforcement learning for active learning in real time: An application in modeling river networks","interactions":[],"lastModifiedDate":"2022-10-11T16:57:48.969639","indexId":"70237363","displayToPublicDate":"2021-04-01T11:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Graph-based reinforcement learning for active learning in real time: An application in modeling river networks","docAbstract":"Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2021 SIAM International Conference on Data Mining","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"2021 SIAM International Conference on Data Mining","conferenceDate":"April 29-May 1, 2021","conferenceLocation":"Online","language":"English","publisher":"SIAM","doi":"10.1137/1.9781611976700.70","usgsCitation":"Jia, X., Lin, B., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., and Read, J., 2021, Graph-based reinforcement learning for active learning in real time: An application in modeling river networks, in Proceedings of the 2021 SIAM International Conference on Data Mining, Online, April 29-May 1, 2021, p. 621-629, https://doi.org/10.1137/1.9781611976700.70.","productDescription":"9 p.","startPage":"621","endPage":"629","ipdsId":"IP-123542","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":452823,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1137/1.9781611976700.70","text":"Publisher Index Page"},{"id":408167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Beiyu","contributorId":297481,"corporation":false,"usgs":false,"family":"Lin","given":"Beiyu","email":"","affiliations":[{"id":64413,"text":"University of Texas - 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Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854273,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268709,"text":"70268709 - 2021 - Exploring strategies for investigating the mechanisms linking climate and individual-level child health outcomes: An analysis of birth weight in Mali","interactions":[],"lastModifiedDate":"2025-07-07T16:05:04.2424","indexId":"70268709","displayToPublicDate":"2021-04-01T11:03:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21981,"text":"Demography","active":true,"publicationSubtype":{"id":10}},"title":"Exploring strategies for investigating the mechanisms linking climate and individual-level child health outcomes: An analysis of birth weight in Mali","docAbstract":"The goal of this article is to consider data solutions to investigate the differential pathways that connect climate/weather variability to child health outcomes. We apply several measures capturing different aspects of climate/weather variability to different time periods of in utero exposure. The measures are designed to capture the complexities of climate-related risks and isolate their impacts based on the timing and duration of exposure. Specifically, we focus on infant birth weight in Mali and consider local weather and environmental conditions associated with the three most frequently posited potential drivers of adverse health outcomes: disease (malaria), heat stress, and food insecurity. We focus this study on Mali, where seasonal trends facilitate the use of measures specifically designed to capture distinct aspects of climate/weather conditions relevant to the potential drivers. Results indicate that attention to the timing of exposures and employing measures designed to capture nuances in each of the drivers provides important insight into climate and birth weight outcomes, especially in the case of factors impacted by precipitation. Results also indicate that high temperatures and low levels of agricultural production are consistently associated with lower birth weights, and exposure to malarious conditions may increase likelihood of nonlive birth outcomes.
","language":"English","publisher":"Duke University Press","doi":"10.1215/00703370-8977484","usgsCitation":"Grace, K., Verdin, A., Dorélien, A., Davenport, F., Funk, C., and Husak, G., 2021, Exploring strategies for investigating the mechanisms linking climate and individual-level child health outcomes: An analysis of birth weight in Mali: Demography, v. 58, no. 2, p. 499-526-526, https://doi.org/10.1215/00703370-8977484.","productDescription":"28 p.","startPage":"499-526","endPage":"526","ipdsId":"IP-121456","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1215/00703370-8977484","text":"Publisher Index Page"},{"id":491742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mali","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-11.83421,14.7991],[-11.66608,15.38821],[-11.3491,15.41126],[-10.65079,15.13275],[-10.08685,15.33049],[-9.70026,15.26411],[-9.55024,15.4865],[-5.53774,15.50169],[-5.31528,16.20185],[-5.48852,16.3251],[-5.97113,20.64083],[-6.45379,24.95659],[-4.92334,24.97457],[-1.55005,22.79267],[1.82323,20.61081],[2.06099,20.14223],[2.68359,19.85623],[3.14666,19.69358],[3.15813,19.05736],[4.26742,19.15527],[4.27021,16.85223],[3.72342,16.18428],[3.63826,15.56812],[2.74999,15.40952],[1.38553,15.32356],[1.01578,14.96818],[0.37489,14.92891],[-0.26626,14.92431],[-0.51585,15.11616],[-1.06636,14.97382],[-2.00104,14.55901],[-2.19182,14.24642],[-2.96769,13.79815],[-3.10371,13.54127],[-3.5228,13.33766],[-4.00639,13.47249],[-4.28041,13.22844],[-4.42717,12.54265],[-5.22094,11.71386],[-5.19784,11.37515],[-5.47056,10.95127],[-5.40434,10.37074],[-5.81693,10.22255],[-6.05045,10.09636],[-6.20522,10.52406],[-6.49397,10.4113],[-6.66646,10.43081],[-6.85051,10.13899],[-7.62276,10.14724],[-7.89959,10.29738],[-8.02994,10.20653],[-8.33538,10.49481],[-8.28236,10.7926],[-8.40731,10.90926],[-8.62032,10.81089],[-8.58131,11.13625],[-8.3763,11.39365],[-8.7861,11.81256],[-8.90526,12.08836],[-9.12747,12.30806],[-9.32762,12.33429],[-9.56791,12.19424],[-9.89099,12.06048],[-10.16521,11.84408],[-10.59322,11.92398],[-10.87083,12.17789],[-11.03656,12.21124],[-11.29757,12.07797],[-11.45617,12.07683],[-11.51394,12.44299],[-11.4679,12.75452],[-11.5534,13.14121],[-11.92772,13.42208],[-12.12489,13.99473],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mali\"}}]}","volume":"58","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, Kathryn","contributorId":145815,"corporation":false,"usgs":false,"family":"Grace","given":"Kathryn","email":"","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":941700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Verdin, Andrew","contributorId":145812,"corporation":false,"usgs":false,"family":"Verdin","given":"Andrew","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":941701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dorélien, Audrey","contributorId":357546,"corporation":false,"usgs":false,"family":"Dorélien","given":"Audrey","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":941702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":941703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":941704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":941705,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228373,"text":"70228373 - 2021 - Embracing ensemble species distribution models to inform at-risk species status assessments","interactions":[],"lastModifiedDate":"2022-02-09T17:03:42.769248","indexId":"70228373","displayToPublicDate":"2021-04-01T10:56:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Embracing ensemble species distribution models to inform at-risk species status assessments","docAbstract":"Conservation planning depends on reliable information regarding the geographic distribution of species. However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service's species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee, and Balduina atropurpurea) in the Southeastern United States. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-072","usgsCitation":"Ramirez-Reyes, C., Nazeri, M., Street, G., Jones-Ferrand, D.T., Vilella, F., and Evans, K.O., 2021, Embracing ensemble species distribution models to inform at-risk species status assessments: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 98-111, https://doi.org/10.3996/JFWM-20-072.","productDescription":"14 p.","startPage":"98","endPage":"111","ipdsId":"IP-114759","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-072","text":"Publisher Index Page"},{"id":395684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Florida, Georgia, Missouri, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.01074218749999,\n 33.063924198120645\n ],\n [\n -90.87890625,\n 34.125447565116126\n ],\n [\n -89.7802734375,\n 35.817813158696616\n ],\n [\n -89.3408203125,\n 36.80928470205937\n ],\n [\n -90.2197265625,\n 38.41055825094609\n ],\n [\n -90.2197265625,\n 38.89103282648846\n ],\n [\n -91.58203125,\n 40.38002840251183\n ],\n [\n -91.8017578125,\n 40.713955826286046\n ],\n [\n -95.9326171875,\n 40.613952441166596\n ],\n [\n -94.74609375,\n 39.33429742980725\n ],\n [\n -94.74609375,\n 37.92686760148135\n ],\n [\n -94.6142578125,\n 36.24427318493909\n ],\n [\n -94.658203125,\n 33.7243396617476\n ],\n [\n -93.8232421875,\n 33.247875947924385\n ],\n [\n -93.7353515625,\n 33.02708758002874\n ],\n [\n -91.01074218749999,\n 33.063924198120645\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.9375,\n 36.27970720524017\n ],\n [\n -81.7822265625,\n 36.4566360115962\n ],\n [\n -83.4521484375,\n 35.35321610123823\n ],\n [\n -83.5400390625,\n 34.92197103616377\n ],\n [\n -85.69335937499999,\n 34.95799531086792\n ],\n [\n -85.1220703125,\n 31.015278981711266\n ],\n [\n -86.66015624999999,\n 30.90222470517144\n ],\n [\n -86.30859375,\n 30.486550842588485\n ],\n [\n -85.1220703125,\n 29.649868677972304\n ],\n [\n -83.5400390625,\n 29.6880527498568\n ],\n [\n -82.6611328125,\n 27.644606381943326\n ],\n [\n -80.9033203125,\n 29.726222319395504\n ],\n [\n -81.2548828125,\n 31.42866311735861\n ],\n [\n -78.837890625,\n 33.32134852669881\n ],\n [\n -76.11328125,\n 34.59704151614417\n ],\n [\n -75.2783203125,\n 35.92464453144099\n ],\n [\n -75.9375,\n 36.27970720524017\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramirez-Reyes, C.","contributorId":275333,"corporation":false,"usgs":false,"family":"Ramirez-Reyes","given":"C.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nazeri, M.","contributorId":275334,"corporation":false,"usgs":false,"family":"Nazeri","given":"M.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834006,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Street, Garrett","contributorId":275335,"corporation":false,"usgs":false,"family":"Street","given":"Garrett","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones-Ferrand, D. 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O.","contributorId":275337,"corporation":false,"usgs":false,"family":"Evans","given":"K.","email":"","middleInitial":"O.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834010,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263748,"text":"70263748 - 2021 - An integrated population model for harvest management of Atlantic brant","interactions":[],"lastModifiedDate":"2025-02-21T15:59:51.150161","indexId":"70263748","displayToPublicDate":"2021-04-01T09:56:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"An integrated population model for harvest management of Atlantic brant","docAbstract":"Atlantic brant (Branta bernicla hrota) are important game birds in the Atlantic Flyway and several long-term monitoring data sets could assist with harvest management, including a count-based survey and demographic data. Considering their relative strengths and weaknesses, integrated analysis to these data would likely improve harvest management, but tools for integration have not yet been developed. Managers currently use an aerial count survey on the wintering grounds, the mid-winter survey, to set harvest regulations. We developed an integrated population model (IPM) for Atlantic brant that uses multiple data sources to simultaneously estimate population abundance, survival, and productivity. The IPM abundance estimates for data from 1975–2018 were less variable than annual mid-winter survey counts or Lincoln estimates, presumably reflecting better accounting for observer error and incorporation of demographic estimates by the IPM. Posterior estimates of adult survival were high (0.77–0.87), and harvest rates of adults and juveniles were positively correlated with more liberal hunting regulations (i.e., hunting days and the daily bag limit). Productivity was variable, with the percent of juveniles in the winter population ranging from 1% to >40%. We found no evidence for environmental relationships with productivity. Using IPM-predicted population abundances rather than mid-winter survey counts alone would have meant fewer annual changes to hunting regulations since 2004. Use of the IPM could improve harvest management for Atlantic brant by providing the ability to predict abundance before annual hunting regulations are set, and by providing more stable hunting regulations, with fewer annual changes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22037","usgsCitation":"Roberts, A., Dooly, J., Ross, B., Nichols, T., Leafloor, J., and Dufour, K., 2021, An integrated population model for harvest management of Atlantic brant: Journal of Wildlife Management, v. 85, no. 5, p. 897-908, https://doi.org/10.1002/jwmg.22037.","productDescription":"12 p.","startPage":"897","endPage":"908","ipdsId":"IP-119298","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":482337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.8343877582402,\n 62.41285920640681\n ],\n [\n -69.27612610313905,\n 62.9825936579802\n ],\n [\n -66.9360611726691,\n 66.43037175085522\n ],\n [\n -74.41355122876546,\n 71.18930968714878\n ],\n [\n -92.1985681614551,\n 73.69165787492997\n ],\n [\n -94.52383256314731,\n 72.23639925807228\n ],\n [\n -95.00348613973863,\n 68.4831465437872\n ],\n [\n -91.73869598878188,\n 66.06127536766604\n ],\n [\n -89.4591220679694,\n 64.29556910964087\n ],\n [\n -82.6298769556148,\n 61.229545172462025\n ],\n [\n -78.54052925132643,\n 61.70675111212782\n ],\n [\n -77.8343877582402,\n 62.41285920640681\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, A.J.","contributorId":351178,"corporation":false,"usgs":false,"family":"Roberts","given":"A.J.","affiliations":[{"id":36209,"text":"U.S. FWS","active":true,"usgs":false}],"preferred":false,"id":928111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dooly, J.L.","contributorId":351179,"corporation":false,"usgs":false,"family":"Dooly","given":"J.L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":928113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, T.C.","contributorId":351180,"corporation":false,"usgs":false,"family":"Nichols","given":"T.C.","affiliations":[{"id":83933,"text":"New Jersey Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leafloor, J.O.","contributorId":351181,"corporation":false,"usgs":false,"family":"Leafloor","given":"J.O.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dufour, K.W.","contributorId":351182,"corporation":false,"usgs":false,"family":"Dufour","given":"K.W.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":928116,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219585,"text":"70219585 - 2021 - Predicting the spatiotemporal exposure of aquatic species to intrusions of fire retardant in streams with limited data","interactions":[],"lastModifiedDate":"2021-04-15T12:51:24.287992","indexId":"70219585","displayToPublicDate":"2021-04-01T07:50:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the spatiotemporal exposure of aquatic species to intrusions of fire retardant in streams with limited data","docAbstract":"Because fire retardant can enter streams and harm aquatic species including endangered fish, agencies such as the U.S. Forest Service (USFS) must estimate the downstream extent of toxic effects every time fire retardant enters streams (denoted as an “intrusion”). A challenge in estimating the length of stream affected by the intrusion and the exposure time of species in the affected reach is the lack of data typically available on the stream's geometry and flow characteristics. Previously, the USFS estimated the affected reach length assuming instantaneous mixing of the retardant over the reach; however, this approach neglects key river mixing processes. An approach is described that accounts for advection and dispersion of the retardant as well as the downstream growth of the stream. Applied to 13 intrusions documented by the USFS, the new approach shows affected reach lengths range between 8.0 and 362 km; all 13 cases exceeded previous estimates from an instantaneous mixing model. The time that a stationary individual in the affected reach is exposed to concentrations above a pre-defined toxicity threshold (10% of 96-hour LC50, for example) ranges from 0.17 to 2.73 h, with all but one case having a maximum exposure time less than 1.5 h. Results from 1152 hypothetical intrusions provided by the USFS confirm that exposure times rarely exceed 5 h. This result suggests that 96-hour tests to determine toxicity (LC50) to various species should be reconsidered. Although the approach described can be improved in several ways, it provides a first estimate of the effects of fire retardant intrusions.
Precipitation events in urban areas often result in stormwater runoff containing a diverse array of chemical contaminants. Although many traditional contaminants, such as nutrients, heavy metals, and polycyclic aromatic hydrocarbons have been studied extensively, only recently has evidence emerged showing that trace organic compounds (TrOCs), including pharmaceuticals, personal care products and pesticides are frequently found in stormwater runoff. As there is little existing information about the sources of TrOCs in urban stormwater or their removal efficacy across a range of stormwater treatment options, we conducted a study to address these knowledge gaps and to characterize the potential contribution of TrOCs to groundwater resources from stormwater infiltration practices, based on several synoptic measurements. The current study allowed us to enhance an existing effort to assess TrOC presence and toxicity in stormwater runoff and treatment pond outflow by addressing questions related to TrOC sources to stormwater and TrOC transport to groundwaters.
Analysis of eDNA confirms multiple sources of TrOCs to stormwater including human sewage, dog waste, and feces from waterfowl. It is likely that the presence of some TrOCs detected in stormwater are the result of direct, untreated sewage inputs to stormwater from either human (i.e., leaking sewer infrastructure) or pet waste (washed from sidewalks into storm drains). The seasonal detection of avian eDNA is noteworthy as it highlights seasonality and patterns of migration patterns as contributing factors to stormwater contamination. In contrast to human and pet waste, which likely enters stormwater ponds via the stormwater conveyance system, avian feces may enter ponds either through stormwater runoff or through direct inputs by waterfowl stopping-over temporarily at stormwater ponds. Stormwater ponds had little effect in reducing TrOCs as determined by comparative inflow and outflow analysis. Our results also indicate that overall few TrOCs were present in receiving groundwater adjacent to underground infiltration basins, compared to inflow. However, some contaminants were present at relatively high concentrations compared to stormwater flowing into the basins. This is particularly true for pesticides and their degradants. Fewer TrOCs were detected in interstitial water collected near stormwater ponds compared to inflow and outflow. The presence and concentrations of TrOCs in outflow from ponds was generally similar to or higher than what was observed in inflow.
The data collected as part of this study can be used to guide future research or monitoring in an effort to better understand TrOC fate and transport in the environment via stormwater BMPs. Specifically, more work is needed to track parcels of water as they flow through BMPs to better quantify transport and degradation of TrOCs, monitor flow into and out of ponds for mass balance calculations, and conduct tracer tests to better quantify the amount of water that monitoring wells are intercepting from underground infiltration basins.
These results have been shared in multiple presentations and in meetings with high school teachers to develop age-appropriate curriculum to highlight the role of individuals in reducing and preventing stormwater contamination. The ongoing pandemic hindered some of these efforts (cancelled conferences; suspended MN Water Roundtable meetings; pre-occupation with teachers moving materials online), however, as dissemination activities become more common in the near future, we will continue to educate stakeholders and educators about the root causes and effects of urban stormwater contamination.
","language":"English","publisher":"University of Minnesota","usgsCitation":"Schoenfuss, H.L., Kiesling, R.L., Elliott, S.M., and Kohno, S., 2021, Identifying sources of contaminants in urban stormwater and evaluation of their removal efficacy across a continuum of urban best management practices: Final Report, 46 p.","productDescription":"46 p.","ipdsId":"IP-127948","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":387866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387830,"type":{"id":15,"text":"Index Page"},"url":"https://www.wrc.umn.edu/sites/wrc.umn.edu/files/identifying_sources_of_contaminants_scsu_usgs_final_report_march_2021.pdf"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.44970703125,\n 44.95265089681472\n ],\n [\n -93.01162719726562,\n 44.95265089681472\n ],\n [\n -93.01162719726562,\n 45.22364447346731\n ],\n [\n -93.44970703125,\n 45.22364447346731\n ],\n [\n -93.44970703125,\n 44.95265089681472\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":821064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kohno, Satomi","contributorId":264174,"corporation":false,"usgs":false,"family":"Kohno","given":"Satomi","email":"","affiliations":[],"preferred":false,"id":821066,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222365,"text":"70222365 - 2021 - Vegetation community monitoring: Species composition and biophysical gradients in Klamath Network parks","interactions":[],"lastModifiedDate":"2021-07-23T15:01:22.420272","indexId":"70222365","displayToPublicDate":"2021-03-31T09:57:17","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/KLMN/NRR—2021/2236","title":"Vegetation community monitoring: Species composition and biophysical gradients in Klamath Network parks","docAbstract":"The Klamath Network of the National Park Service consists of six park units located in northern California and southern Oregon. The Network began implementing a vegetation monitoring protocol in 2011 to identify ecologically significant vegetation trends in the parks. The premise of the protocol is that multivariate analyses of species composition data is the most robust early detection means for identifying vegetation change over time. Here we present these community metrics, based on our initial sampling efforts. We use these metrics to establish a baseline for comparison in future trend analysis, and to evaluate the adequacy of the protocol for meeting the Network’s objectives of detecting temporal changes across contrasting vegetation types.
The park landscapes were subdivided into three strata: Matrix (low- to mid-elevation upland habitats), Riparian (within 10 meters of a perennial stream), and High-Elevation (above a predefined elevation, park specific). Across the three strata, we established a total of 241 permanent plots at random locations to measure complete species composition and cover. We describe baseline biophysical conditions and relate them to the data obtained from all 241 plots using ordination analyses. The unconstrained gradient analyses were moderately robust at illustrating the relationships among plots and correlating them to environmental gradients. We also prepared species accumulation curves representing gamma diversity, which showed overall species richness, and also illustrated how well the observed vs. expected richness values of each stratum were captured by the sampling. Most park/strata were well sampled; for others, we found that additional samples would improve how well the protocol captures the vegetation composition within park/strata. Specifically, all sample frames at Whiskeytown and the High-Elevation sample frames at Lassen were not well sampled. Comparisons of alpha diversity values showed High-Elevations had the lowest diversity, while Riparian areas were by far the most diverse across all parks. The Matrix stratum at Oregon Caves National Monument was also especially diverse and had the highest Matrix alpha diversity we observed in all parks We suggest that after three rounds of sampling, the Network perform analyses to identify possible ways to improve statistical power. These options include adding sites or lengthening the sampling interval. Results of these analyses could support protocol modifications. This report on vegetation composition is the first in a series of analysis and synthesis reports. Future analysis and synthesis reports will analyze structure and function.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2284769","usgsCitation":"Smith, S.B., van Mantgem, P., and Odion, D., 2021, Vegetation community monitoring: Species composition and biophysical gradients in Klamath Network parks: Natural Resource Report NPS/KLMN/NRR—2021/2236, x, 64 p., https://doi.org/10.36967/nrr-2284769.","productDescription":"x, 64 p.","ipdsId":"IP-107362","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Network National Parks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.541015625,\n 40.43022363450862\n ],\n [\n -120.673828125,\n 40.43022363450862\n ],\n [\n -120.673828125,\n 43.59630591596548\n ],\n [\n -124.541015625,\n 43.59630591596548\n ],\n [\n -124.541015625,\n 40.43022363450862\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Sean B.","contributorId":168621,"corporation":false,"usgs":false,"family":"Smith","given":"Sean","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odion, Dennis","contributorId":168618,"corporation":false,"usgs":false,"family":"Odion","given":"Dennis","affiliations":[],"preferred":false,"id":819766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223888,"text":"70223888 - 2021 - Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network","interactions":[],"lastModifiedDate":"2021-09-13T14:00:38.859827","indexId":"70223888","displayToPublicDate":"2021-03-31T08:55:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network","docAbstract":"Marine fish movement plays a critical role in ecosystem functioning and is increasingly studied with acoustic telemetry. Traditionally, this research has focused on single species and small spatial scales. However, integrated tracking networks, such as the Integrated Tracking of Aquatic Animals in the Gulf of Mexico (iTAG) network, are building the capacity to monitor multiple species over larger spatial scales. We conducted a synthesis of passive acoustic monitoring data for 29 species (889 transmitters), ranging from large top predators to small consumers, monitored along the west coast of Florida, USA, over 3 yr (2016-2018). Space use was highly variable, with some groups using all monitored areas and others using only the area where they were tagged. The most extensive space use was found for Atlantic tarpon Megalops atlanticus and bull sharks Carcharhinus leucas. Individual detection patterns clustered into 4 groups, ranging from occasionally detected long-distance movers to frequently detected juvenile or adult residents. Synchronized, alongshore, long-distance movements were found for Atlantic tarpon, cobia Rachycentron canadum, and several elasmobranch species. These movements were predominantly northbound in spring and southbound in fall. Detections of top predators were highest in summer, except for nearshore Tampa Bay where the most detections occurred in fall, coinciding with large red drum Sciaenops ocellatus spawning aggregations. We discuss the future of collaborative telemetry research, including current limitations and potential solutions to maximize its impact for understanding movement ecology, conducting ecosystem monitoring, and supporting fisheries management.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13637","usgsCitation":"Friess, C., Lowerre-Barbieri, S.K., Poulakis, G.R., Hammerschlag, N., Gardiner, J.M., Kroetz, A.M., Bassos-Hull, K., Bickford, J., Bohaboy, E.C., Ellis, R., Menendez, H., Patterson, W.F., Price, M.E., Rehage, J., Shea, C.P., Smukall, M.J., Walters Burnsed, S., Wilkinson, K.A., Young, J., Collins, A.B., DeGroot, B.C., Peterson, C.T., Purtlebaugh, C., Randall, M.T., Scharer, R.M., Schloesser, R.W., Wiley, T.R., Alvarez, G.A., Danylchuk, A.J., Fox, A.G., Hill, A., Grubbs, R.D., Locascio, J.V., O’Donnell, P.M., Skomal, G.B., Whoriskey, F.G., and Griffin, L.P., 2021, Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network: Marine Ecology Progress Series, v. 663, p. 157-177, https://doi.org/10.3354/meps13637.","productDescription":"21 p.","startPage":"157","endPage":"177","ipdsId":"IP-122154","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452861,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/49131","text":"External Repository"},{"id":389144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"663","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Lowerre-Barbieri, Susan K.","contributorId":189591,"corporation":false,"usgs":false,"family":"Lowerre-Barbieri","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":823133,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Friess, Claudia","contributorId":265608,"corporation":false,"usgs":false,"family":"Friess","given":"Claudia","email":"","affiliations":[{"id":39849,"text":"Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":823097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowerre-Barbieri, Susan K.","contributorId":189591,"corporation":false,"usgs":false,"family":"Lowerre-Barbieri","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":823154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulakis, Gregg R.","contributorId":265609,"corporation":false,"usgs":false,"family":"Poulakis","given":"Gregg","email":"","middleInitial":"R.","affiliations":[{"id":54731,"text":"Charlotte Harbor Field Laboratory, Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":823098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammerschlag, Neil","contributorId":213059,"corporation":false,"usgs":false,"family":"Hammerschlag","given":"Neil","email":"","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":823099,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gardiner, Jayne M.","contributorId":265610,"corporation":false,"usgs":false,"family":"Gardiner","given":"Jayne","email":"","middleInitial":"M.","affiliations":[{"id":54732,"text":"Division of Natural Sciences, New College of Florida","active":true,"usgs":false}],"preferred":false,"id":823100,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kroetz, Andrea M.","contributorId":265611,"corporation":false,"usgs":false,"family":"Kroetz","given":"Andrea","email":"","middleInitial":"M.","affiliations":[{"id":54733,"text":"Riverside Technology, Inc. for NOAA, Southeast Fisheries Science Center, National Marine Fisheries 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of Environment, Florida International University","active":true,"usgs":false}],"preferred":false,"id":823109,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shea, Colin P.","contributorId":140147,"corporation":false,"usgs":false,"family":"Shea","given":"Colin","email":"","middleInitial":"P.","affiliations":[{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":823110,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smukall, Matthew J.","contributorId":265619,"corporation":false,"usgs":false,"family":"Smukall","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":54737,"text":"Bimini Biological Field Station Foundation","active":true,"usgs":false}],"preferred":false,"id":823111,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Walters Burnsed, Sarah","contributorId":265620,"corporation":false,"usgs":false,"family":"Walters 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T.","contributorId":209973,"corporation":false,"usgs":false,"family":"Peterson","given":"Cheston","email":"","middleInitial":"T.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":823117,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Purtlebaugh, Caleb","contributorId":265625,"corporation":false,"usgs":false,"family":"Purtlebaugh","given":"Caleb","email":"","affiliations":[{"id":54739,"text":"Senator George Kirkpatrick Marine Laboratory, Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":823118,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Randall, Michael T. 0000-0001-8805-0886 mrandall@usgs.gov","orcid":"https://orcid.org/0000-0001-8805-0886","contributorId":3127,"corporation":false,"usgs":true,"family":"Randall","given":"Michael","email":"mrandall@usgs.gov","middleInitial":"T.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":823119,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Scharer, Rachel M.","contributorId":265626,"corporation":false,"usgs":false,"family":"Scharer","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":54731,"text":"Charlotte Harbor Field Laboratory, Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":823120,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Schloesser, Ryan W.","contributorId":265627,"corporation":false,"usgs":false,"family":"Schloesser","given":"Ryan","email":"","middleInitial":"W.","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":823121,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Wiley, Tonya R.","contributorId":265628,"corporation":false,"usgs":false,"family":"Wiley","given":"Tonya","email":"","middleInitial":"R.","affiliations":[{"id":54740,"text":"Havenworth Coastal Conservation","active":true,"usgs":false}],"preferred":false,"id":823122,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Alvarez, Gina A.","contributorId":265629,"corporation":false,"usgs":false,"family":"Alvarez","given":"Gina","email":"","middleInitial":"A.","affiliations":[{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":823123,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Danylchuk, Andy J.","contributorId":138981,"corporation":false,"usgs":false,"family":"Danylchuk","given":"Andy","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":823124,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Fox, Adam G.","contributorId":265630,"corporation":false,"usgs":false,"family":"Fox","given":"Adam","email":"","middleInitial":"G.","affiliations":[{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":823125,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Hill, Ashley","contributorId":265631,"corporation":false,"usgs":false,"family":"Hill","given":"Ashley","email":"","affiliations":[{"id":54741,"text":"Lynker Technologies for NOAA, National Ocean Services, Office of Response and Restoration, Marine Debris Division","active":true,"usgs":false}],"preferred":false,"id":823127,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Grubbs, R. Dean","contributorId":167136,"corporation":false,"usgs":false,"family":"Grubbs","given":"R.","email":"","middleInitial":"Dean","affiliations":[{"id":24623,"text":"Florida State University Coastal and Marine Laboratory, Teresa, FL","active":true,"usgs":false}],"preferred":false,"id":823126,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Locascio, James V.","contributorId":265632,"corporation":false,"usgs":false,"family":"Locascio","given":"James","email":"","middleInitial":"V.","affiliations":[{"id":13147,"text":"Mote Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":823128,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"O’Donnell, Patrick M.","contributorId":265633,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Patrick","email":"","middleInitial":"M.","affiliations":[{"id":54742,"text":"Rookery Bay National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":823129,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Skomal, Gregory B.","contributorId":265634,"corporation":false,"usgs":false,"family":"Skomal","given":"Gregory","email":"","middleInitial":"B.","affiliations":[{"id":39892,"text":"Massachusetts Division of Marine Fisheries","active":true,"usgs":false}],"preferred":false,"id":823130,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Whoriskey, Fred G.","contributorId":265635,"corporation":false,"usgs":false,"family":"Whoriskey","given":"Fred","email":"","middleInitial":"G.","affiliations":[{"id":54743,"text":"Ocean Tracking Network, Department of Biology, Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":823131,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Griffin, Lucas P.","contributorId":265636,"corporation":false,"usgs":false,"family":"Griffin","given":"Lucas","email":"","middleInitial":"P.","affiliations":[{"id":54744,"text":"Department of Environmental Conservation, University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":823132,"contributorType":{"id":1,"text":"Authors"},"rank":37}]}} ,{"id":70220261,"text":"70220261 - 2021 - Habitat suitability index model improvement recommendations","interactions":[],"lastModifiedDate":"2021-04-29T13:20:08.027314","indexId":"70220261","displayToPublicDate":"2021-03-31T08:19:03","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Habitat suitability index model improvement recommendations","docAbstract":"As part of the model improvement effort for the 2023 Coastal Master Plan, the Habitat Suitability Index (HSI) models used during previous master plans were reevaluated to assess how the model relationships could be improved, and to determine what species should be included in the master plan analyses. This process considered the technical reviews, comments, and suggested improvements provided by model developers, advisory groups, and other experts during previous master plans. Reviews were then conducted to determine the availability of data and information that could be used to make model improvements. As a result of this effort, a recommended list of relevant species to model is provided, and HSI model improvements are recommended that are categorized by whether the suitability index (SI) relationship to be improved is statistical-based or literature-based. \n\nThe species recommended to be included in the 2023 Coastal Master Plan analyses are: eastern oyster, brown shrimp, white shrimp, blue crab, crayfish, gulf menhaden, spotted seatrout, largemouth bass, American alligator, gadwall, mottled duck, brown pelican, seaside sparrow, and bald eagle. These species were selected because they represent a range of taxonomies, life histories, trophic levels, and habitats, and most are commercially- or recreationally-important in coastal Louisiana. Most of these species were also included in the 2017 Coastal Master Plan analyses, and the models used during that effort should be further improved. Seaside sparrow and bald eagle are new for the master plan, and new models should be developed for the analyses. \n\nThe 2017 fish, shrimp, and blue crab HSI models included a water quality SI that was based on statistical analyses of species catch and environmental data collected by the Louisiana Department of Wildlife and Fisheries. As suggested during the 2017 Coastal Master Plan, the modeling approach used to develop the water quality SI was revisited and alternate modeling approaches were explored. Using literature and an evaluation of the general steps of model development, three components for HSI model improvement were identified, including 1) selecting alternative modeling approach(es); 2) detecting and resolving statistical issues; and 3) improving model fit and evaluation. Multiple options for each component were explored, which resulted in a proposed multi-step phased approach for model improvement. This proposed approach entails improving the generalized linear models used for the 2017 water quality SIs and then, if desired, comparing them to alternative model approaches (e.g., generalized additive models) to explore model performance and select the best approach to use for the 2023 Coastal Master Plan HSI models. \n\nAll of the existing master plan HSI models include literature-based SIs, which use information from published studies of species-habitat associations to derive suitability relationships. Similar to previous master plans, these literature-based SIs should be updated and improved for the 2023 Coastal Master Plan using recent literature and new ecological knowledge. Preliminary reviews were conducted and recent information was found that could be used to improve the eastern oyster, crayfish, and potentially brown pelican HSI models; but no appropriate recent literature was located for improvement of the American alligator, gadwall, and mottled duck HSI models. However, it is recommended that the literature reviews and information searches be continued. In addition to the statistical-based water quality SI, the 2017 fish, shrimp, and blue crab HSI models also included a structural habitat SI that was based on literature showing high densities of these species in fragmented marsh. The relationship used for this SI, however, did not account for the effects of other estuarine habitats, such as submerged aquatic vegetation and oyster reefs, which are also important to these species. Therefore, a meta-analysis approach is proposed that would estimate the relative importance of these habitats for each species, and the results of this analysis could be used to calculate a new structural habitat SI for the 2023 Coastal Master Plan.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Coastal Master Plan","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Coastal Protection and Restoration Authority","usgsCitation":"Sable, S.E., Lindquist, D.C., D’Acunto, L., Hijuelos, A., LaPeyre, M.K., O'Connell, A., and Robinson, E.M., 2021, Habitat suitability index model improvement recommendations, 49 p.","productDescription":"49 p.","ipdsId":"IP-109817","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385374,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/technical-resources/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sable, Shaye E.","contributorId":257728,"corporation":false,"usgs":false,"family":"Sable","given":"Shaye","email":"","middleInitial":"E.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":814922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindquist, David C.","contributorId":257729,"corporation":false,"usgs":false,"family":"Lindquist","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":201525,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":814926,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O'Connell, Ann M.","contributorId":257730,"corporation":false,"usgs":false,"family":"O'Connell","given":"Ann M.","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":814927,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Robinson, Elizabeth M.","contributorId":257731,"corporation":false,"usgs":false,"family":"Robinson","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814928,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220262,"text":"70220262 - 2021 - Habitat suitability index model improvements","interactions":[],"lastModifiedDate":"2021-04-29T13:18:04.649339","indexId":"70220262","displayToPublicDate":"2021-03-31T08:17:18","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Habitat suitability index model improvements","docAbstract":"Habitat suitability index (HSI) models were developed for the 2023 Coastal Master Plan to evaluate the potential effects of coastal restoration and protection projects on habitat for key coastal fish, shellfish, and wildlife species. These species included: eastern oyster, brown shrimp, white shrimp, blue crab, crayfish, gulf menhaden, spotted seatrout, largemouth bass, American alligator, gadwall, mottled duck, brown pelican, seaside sparrow, and bald eagle. Most of these species were included in the 2017 Coastal Master Plan analyses, and the HSI models from that effort were refined and improved following the recommendations described in the technical memorandum: 2023 Coastal Master Plan Habitat Suitability Index Model Improvement Recommendations (Sable et al., 2019). In addition to model improvements, HSI models were created for seaside sparrow and bald eagle, both of which are new species for the master plan analyses. \n\nFor the HSI models that are primarily literature-based, literature reviews were conducted for recent studies that could be used to improve the suitability index (SI) relationships that compose the models. As a result of this review, modifications were made to the salinity-related SIs of the oyster model including: expanding the time period used for salinity effects to spawning; adjusting the range of suitable annual average salinity to be more representative of Louisiana populations; and making oyster’s minimum salinity tolerance temperature dependent. In addition, a new SI was incorporated in the oyster HSI model that accounts for the effects of sediment deposition on oysters. The crayfish HSI model was improved by adjusting the time periods used for the SIs that describe the hydrology required for the crayfish life cycle, and the soil characteristics SI that was part of the 2017 crayfish model was removed because soil conditions do not appear to be limiting for crayfish burrow construction in coastal Louisiana. The other literature-based HSI models from the 2017 Coastal Master Plan, i.e., American alligator, gadwall, mottled duck, and brown pelican, were unchanged, with the exception of a small adjustment made to the suitability of forested wetlands for gadwall. Lastly, a literature-based HSI model was created for seaside sparrow that consists of SIs related to vegetated habitat type, marsh vegetation coverage, and marsh elevation. \n\nStatistical-based HSI models were developed for brown shrimp (both small and large juvenile stages), white shrimp (small and large juvenile stages), blue crab (juvenile stage), gulf menhaden (juvenile and adult stages), spotted seatrout (juvenile and adult stages), largemouth bass, and bald eagle. The bald eagle HSI model was developed from a bald eagle nest probability of occurrence model that related nest occurrence from survey data with land cover type. The resulting model showed that combinations of forested wetlands, flotant marsh, and open water habitats were most suitable for nesting bald eagles. The 2023 fish, shrimp, and blue crab HSI models were developed using new approaches for the formulation of the water quality and structural habitat SIs that compose the models. For the 2017 models, the water quality SI was derived using only generalized linear mixed models (GLMMs) to estimate the relationship between salinity, water temperature, and species’ catch. For the 2023 models, however, multiple GLMMs and generalized additive models (GAMMs) were created for each species or life stage. These alternative models were compared and a single model that performed well statistically and was ecologically reasonable was selected for the species’ water quality SI. The structural habitat SI was developed using a meta-analysis of published literature to estimate the relative importance of various estuarine habitats to the fish and shellfish species. The results of this analysis were then used to modify the 2017 structural habitat SI relationship to account for the added habitat value of submerged aquatic vegetation and oyster reefs, which are also important habitats for juvenile fish and shellfish. Similar to the 2017 fish, shrimp, and blue crab models, the water quality and structural habitat SIs were then combined to create the 2023 HSI models. \n\nThe 2023 Coastal Master Plan HSI models were integrated with the Integrated Compartment Model (and are referred to as ICM-HSIs) and tested using environmental output from the 2017 Coastal Master Plan Future Without Action scenario. The tests showed that, in general, the models produced reasonable representations of species’ habitat distribution. Furthermore, the improvements made to the oyster, crayfish, fish, shrimp, and blue crab HSI models generally yielded more realistic results compared to the 2017 HSI models.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Coastal Master Plan","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Coastal Protection and Restoration Authority","usgsCitation":"Lindquist, D.C., Sable, S.E., D’Acunto, L., Hijuelos, A., Johnson, E.I., Langlois, S.R., Michel, N.L., Nakashima, L., O’Connell, A.M., Percy, K.L., and Robinson, E.M., 2021, Habitat suitability index model improvements, 189 p.","productDescription":"189 p.","ipdsId":"IP-124495","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385375,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/technical-resources/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindquist, David C.","contributorId":257729,"corporation":false,"usgs":false,"family":"Lindquist","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sable, Shaye E.","contributorId":257728,"corporation":false,"usgs":false,"family":"Sable","given":"Shaye","email":"","middleInitial":"E.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":814930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":216667,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Erik I.","contributorId":257732,"corporation":false,"usgs":false,"family":"Johnson","given":"Erik","email":"","middleInitial":"I.","affiliations":[{"id":52099,"text":"Audubon Louisiana","active":true,"usgs":false}],"preferred":false,"id":814933,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langlois, Summer R.M","contributorId":257733,"corporation":false,"usgs":false,"family":"Langlois","given":"Summer","email":"","middleInitial":"R.M","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814934,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michel, Nicole L.","contributorId":257734,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L.","affiliations":[{"id":52101,"text":"Audubon Louisiana, National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":814935,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakashima, Lindsay","contributorId":257735,"corporation":false,"usgs":false,"family":"Nakashima","given":"Lindsay","affiliations":[{"id":52099,"text":"Audubon Louisiana","active":true,"usgs":false}],"preferred":false,"id":814936,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O’Connell, Ann M.","contributorId":257736,"corporation":false,"usgs":false,"family":"O’Connell","given":"Ann","email":"","middleInitial":"M.","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":814937,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Percy, Katie L.","contributorId":191722,"corporation":false,"usgs":false,"family":"Percy","given":"Katie","email":"","middleInitial":"L.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":814938,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robinson, Elizabeth M.","contributorId":257731,"corporation":false,"usgs":false,"family":"Robinson","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814939,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70219236,"text":"70219236 - 2021 - A review of timing accuracy across the Global Seismographic Network","interactions":[],"lastModifiedDate":"2021-06-30T17:57:52.115563","indexId":"70219236","displayToPublicDate":"2021-03-31T07:53:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"A review of timing accuracy across the Global Seismographic Network","docAbstract":"The accuracy of timing across a seismic network is important for locating earthquakes as well as studies that use phase‐arrival information (e.g., tomography). The Global Seismographic Network (GSN) was designed with the goal of having reported timing be better than 10 ms. In this work, we provide a brief overview of how timing is kept across the GSN and discuss how clock‐quality metrics are embedded in Standard for Exchange of Earthquake Data records. Specifically, blockette 1001 contains the timing‐quality field, which can be used to identify time periods when poor clock quality could compromise timing accuracy. To verify the timing across the GSN, we compare cross‐correlation lags between collocated sensors from 1 January 2000 to 1 January 2020. We find that the mean error is less than 10 ms, with much of the difference likely coming from the method or uncertainty in the phase response of the instruments. This indicates that timing across the GSN is potentially better than 10 ms. We conclude that unless clock quality is compromised (as indicated in blockette 1001), GSN data’s timing accuracy should be suitable for most current seismological applications that require 10 ms accuracy. To assist users, the GSN network operators have implemented a “gsn_timing” metric available via the Incorporated Research Institutions for Seismology Data Management Center that helps users identify data with substandard timing accuracy (the 10 ms design goal of the GSN).
Potential natural vegetation (PNV), the final successional stage of vegetation, plays a key role in ecological restoration, the design of nature reserves, and development of agriculture and livestock production. Meteorological data from historical and current periods including the last inter-glacial (LIG), last glacial maximum (LGM), mid Holocene (MH) periods and the present day (PD), plus derived data from 2050 and 2070, in conjunction with the Comprehensive and Sequential Classification System (CSCS) model, were used to classify global PNV. The 42 classes of global PNV were regrouped into 10 groups to facilitate analysis of spatial changes. Finally, spatio-temporal patterns and successional processes of global PNV as well as the response to climate changes were analysed. Our study made the following five conclusions. (1) Only one missing class (IA1 frigid-extrarid frigid desert, alpine desert) arose in periods of LIG, MH, 2050, and 2070 for global PNV. (2) The frigid-arid groups were mainly distributed in higher latitudes and elevations, but temperate-humid groups and tropical-perhumid groups occurred in middle and low latitudes, respectively. Temperate zonal forest steppe, warm desert, savanna and tropical zonal forest steppe increased, while six other groups decreased. (3) The conversion from temperate zonal forest steppe to tundra and alpine steppe from LIG to LGM occupied the largest area, indicating a drastic shift in climate and the associated response of terrestrial vegetation sensitive to climate change. (4) The CSCS could be used to simulate the long-term succession of global PNV. (5) As a consequence of global warming, forests shifted to the northern hemisphere and Tibet, areas with much higher latitude and elevation. The PNV groups with greater shift distance revealed the more serious effects of global climate change on vegetation.
Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models.
Effective soil depth class observations (n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms.
Soil depth observations were split into separate training (n = 10,470) and validation (n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics.
Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.
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\"}}]}","edition":"Version 1.0: March 30, 2021; Version 1.1: October 17, 2022; Version 1.2: March 19, 2025","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Incorporating spatial and temporal scales into greater sage-grouse (Centrocercus urophasianus) population monitoring strategies is challenging and rarely implemented. Sage-grouse populations experience fluctuations in abundance that lead to temporal oscillations, making trend estimation difficult. Accounting for stochasticity is critical to reliably estimate population trends and investigate variation related to deterministic factors on the landscape, which are amenable to management action. Here, we describe a novel, range-wide hierarchical monitoring framework for sage-grouse centered on four objectives: (1) create a standardized database of lek counts, (2) develop spatial population structures by clustering leks, (3) estimate spatial trends at different temporal extents based on abundance nadirs (troughs), and (4) develop a targeted annual warning system to help inform management decisions. Using automated and repeatable methods (software), we compiled a lek database (as of 2019) that contained 262,744 counts and 8,421 unique lek locations from disparate state data. The hierarchical population units (clusters) included 13 nested levels, identifying biologically relevant units and population structure that minimized inter-cluster sage-grouse movements. With these products, we identified spatiotemporal variation in trends in population abundance using Bayesian state-space models. We estimated 37.0, 65.2, and 80.7-percent declines in abundance range-wide during short (17 years), medium (33 years), and long (53 years) temporal scales, respectively. However, some areas exhibited evidence of increasing trends in abundance in recent decades. Models predicted 12.3, 19.2, and 29.6 percent of populations (defined as clusters of neighboring leks) consisted of over 50-percent probability of extirpation at 19, 38, and 56-year projections from 2019, respectively, based on averaged annual rate of change in apparent abundance across two, four, and six oscillations (average period of oscillation is 9.4 years). At the lek level, models predicted 45.7, 60.1, and 78.0 percent of leks with over 50-percent extirpation probabilities over the same time periods, respectively, mostly located on the periphery of the species’ range. The targeted annual warning system automates annual identification of local populations exhibiting asynchronous decline relative to regional population patterns using simulated management actions and an optimization algorithm for evaluating range-wide stabilization of population abundance. In 2019, approximately 3.2 percent of leks and 2.0 percent of populations were identified by the targeted annual warning system for management intervention range-wide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201154","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","usgsCitation":"Coates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework—Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020–1154, 243 p., https://doi.org/10.3133/ofr20201154.","productDescription":"Report: vi, 243 p.; 1 Table","numberOfPages":"243","onlineOnly":"Y","ipdsId":"IP-123421","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":384766,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154_table8.csv","text":"Table 8","size":"80 KB","linkFileType":{"id":7,"text":"csv"}},{"id":384765,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154_table8.xlsx","text":"Table 8","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":384760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154.pdf","text":"Report","size":"310 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1154/covrthb.jpg"}],"country":"United States","state":"California, Colorado, Idaho, Montana, Nevada, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.32226562500001,\n 35.67514743608467\n ],\n [\n -103.447265625,\n 35.67514743608467\n ],\n [\n -103.447265625,\n 48.69096039092549\n ],\n [\n -120.32226562500001,\n 48.69096039092549\n ],\n [\n -120.32226562500001,\n 35.67514743608467\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
This document presents the renewed vision, mission, and goals for the National Cooperative Geologic Mapping Program (NCGMP). The NCGMP, as authorized by the National Cooperative Geologic Mapping Act of 1992 (Public Law 102-285, 106 Stat. 166 and its reauthorizations), is tasked with expediting the production of a geologic database for the Nation based on modern geologic maps and their supporting data. In addition to highlighting the benefits of geologic maps for economic prosperity, national security, and environmental quality, the report describes the NCGMP structure and components. A renewed vision and mission for the NCGMP are stated, and three goals for guiding the program toward that vision for the next ten years are established. The vision of creating an integrated, three-dimensional, digital geologic map of the United States and its territories to address the changing needs of the Nation by 2030 is thereby defined to drive the activities of all NCGMP components for the next ten years. The strategic actions required to realize the NCGMP vision are identified for each of its components.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211013","usgsCitation":"Brock, J., Berry, K., Faulds, J., Berg, R., House, K., Marketti, M., McPhee, D., Schmidt, K., Schmitt, J., Soller, D., Spears, D., Thompson, R., Thorleifson, H., and Walsh, G., 2021, Renewing the National Cooperative Geologic Mapping Program as the Nation’s authoritative source for modern geologic knowledge: U.S. Geological Survey Open-File Report 2021–1013, 10 p., https://doi.org/10.3133/ofr20211013.","productDescription":"vi, 10 p.","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115733","costCenters":[{"id":412,"text":"National Cooperative Geologic Mapping Program","active":false,"usgs":true}],"links":[{"id":384680,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1013/ofr20211013.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1013"},{"id":384679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1013/coverthb.jpg"}],"contact":"National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Global Seismographic Network (GSN) is a multiuse, globally distributed seismic network used by seismologists, to both characterize earthquakes and study the Earth’s interior. Most stations in the network have two collocated broadband seismometers, which enable network operators to identify potential metadata and sensor issues. In this study, we investigate the accuracy with which surface waves can be measured across the GSN, by comparing waveforms of vertical‐component Rayleigh waves from
Groundwater-quality environmental data were collected from 983 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water Quality Program and are included in this report. The data were collected from six types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, microbiological indicators, and some constituents of special interest (arsenic speciation, hexavalent chromium [chromium (VI)], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Data for microbiological indicators for samples collected in 2016 are included in the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1136","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Kingsbury, J.A., Bexfield, L.M., Arnold, T., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey, B.D., and Belitz, K., 2021, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.","productDescription":"Report: x, 97 p.; 2 Appendixes; Data Release; Dataset","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118835","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science 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kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1136 Appendix Table 1.1","linkHelpText":"— Index of reports containing each network description"},{"id":384727,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1136/ds1136_table3.1.csv","text":"Table 3.1","size":"15.7 kB","linkFileType":{"id":7,"text":"csv"},"description":"DS 1136 Appendix Table 3.1","linkHelpText":"— Well identification numbers, Groundwater Ambient Monitoring and Assessment study unit, and report with water-quality data for wells in the California Coastal Basin aquifers and Central Valley aquifer system principal aquifer study networks"},{"id":384728,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1136/ds1136_table3.1.xlsx","text":"Table 3.1","size":"23.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1136 Appendix Table 3.1","linkHelpText":"— Well identification numbers, Groundwater Ambient Monitoring and Assessment study unit, and report with 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U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
This study investigates sedimentary and geomorphic processes in eastern Chuckwalla Valley, Riverside County, California, a region of arid, basin-and-range terrain where extensive solar-energy development is planned. The objectives of this study were to (1) measure local weather parameters and use them to model aeolian sediment-transport potential; (2) identify surface sedimentary characteristics in representative localities; and (3) evaluate long-term landscape evolution rates and processes by analyzing stratigraphy in combination with luminescence geochronology.
The new stratigraphic and geochronologic data presented in this report demonstrate the varying local significance of aeolian, alluvial fan, lacustrine (playa), and possibly Colorado River influence over a range of time scales. The dominant sand-transport direction in eastern Chuckwalla Valley is toward the northeast, consistent with the recognized regional west-to-east wind direction. However, occasional strong wind events from the north can transport large quantities of sand southward and temporarily reshape local geomorphic features. Influence of a northwest wind direction is also locally dominant around mountain ranges and controls the modern morphology of the Palen dune field. Modeled sand fluxes are on the order of 105 kilograms per meter width per year at the site of weather monitoring, 5 kilometers northwest of the Mule Mountains. Aeolian dunes are locally well developed and actively migrating. Their location and activity are determined largely by sediment supply from playa surfaces and ephemeral stream channels, which also control the dunes’ spatial extent and migration potential; stream channels act as both source and sink for aeolian sediment in this environment.
Excavations at five sites along a northwest-to-southeast transect reveal that playa deposits formed around 266–226 thousand years ago south of the McCoy Mountains and immediately north of the present location of Interstate 10. The playa material is overlain by late Pleistocene to Holocene alluvial fan deposits. To the southeast (south of Interstate 10, but north of the Mule Mountains), we identified rapid accumulation of alluvial sediment around the time of the Last Glacial Maximum (23–20 thousand years ago), unconformably overlain by a locally varying assemblage of recent aeolian material or Holocene alluvial fan sediment. We have used stratigraphic characteristics and luminescence ages to calculate accumulation rates for sites in eastern Chuckwalla Valley, and thereby to identify spatial variation in landscape stability over decadal and longer time scales.
If future solar-energy development plans are to include natural sand-transport corridors, plans would entail retaining the ability for sand to be transported eastward from the ephemeral stream channels and playas that supply sediment to the dunes, sand sheets, and sand ramps of Chuckwalla Valley, and also to allow for southward transport during episodic strong weather events several times per year. The aeolian sediment-transport corridors are dynamic spatially and temporally, reorganizing on the basis of seasonal changes to wind drift potential. Future landscape stability also will be determined by climate-driven changes to vegetation and thereby to aeolian sediment availability. In a warmer, drier climate, aeolian sediment activity is expected to increase, owing to a decrease in stabilizing vegetation cover and more extreme rain that supplies sediment to ephemeral stream channels and playas from which it is remobilized by wind.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215017","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"East, A.E., Gray, H.J., Redsteer, M.H., and Ballmer, M., 2021, Landscape evolution in eastern Chuckwalla Valley, Riverside County, California: U.S. Geological Survey Scientific Investigations Report 2021–5017, 46 p., https://doi.org/10.3133/sir20215017.","productDescription":"Report: vi, 46 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-124276","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":384720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5017/covrthb.jpg"},{"id":384721,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5017/sir20215017.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384722,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LZ02E4","linkHelpText":"Luminescence, weather, and grain-size data from eastern Chuckwalla Valley, Riverside County, California"}],"country":"United States","state":"California","county":"Riverside County","otherGeospatial":"Eastern Chuckwalla 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Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060