Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.
Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00256-8","usgsCitation":"Hance, D., Moriarty, K.M., Hollen, B.A., and Perry, R., 2021, Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states: Movement Ecology, v. 9, 17, 22 p., https://doi.org/10.1186/s40462-021-00256-8.","productDescription":"17, 22 p.","ipdsId":"IP-123520","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":452803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00256-8","text":"Publisher Index Page"},{"id":384927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.55224609375,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 41.88592102814744\n ],\n [\n -121.17919921875001,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 42.84375132629021\n ],\n [\n -123.55224609375,\n 41.88592102814744\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moriarty, Katie M.","contributorId":256976,"corporation":false,"usgs":false,"family":"Moriarty","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":51930,"text":"National Council for Air and Stream Improvement, Inc., Corvallis, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":813626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hollen, Bruce A.","contributorId":256977,"corporation":false,"usgs":false,"family":"Hollen","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":51933,"text":"USDI Bureau of Land Management, Regional Office, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":813627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219254,"text":"sir20215011 - 2021 - Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","interactions":[],"lastModifiedDate":"2023-04-10T18:30:08.234211","indexId":"sir20215011","displayToPublicDate":"2021-04-05T11:15:06","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5011","displayTitle":"Aquaculture and Irrigation Water-Use Model (AIWUM) Version 1.0—An Agricultural Water-Use Model Developed for the Mississippi Alluvial Plain, 1999–2017","title":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017","docAbstract":"Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (<) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 37.16031654673677\n ],\n [\n -89.296875,\n 37.33522435930639\n ],\n [\n -89.9560546875,\n 37.71859032558816\n ],\n [\n -90.8349609375,\n 36.914764288955936\n ],\n [\n -91.5380859375,\n 35.96022296929667\n ],\n [\n -92.1533203125,\n 34.66935854524543\n ],\n [\n -92.373046875,\n 33.50475906922609\n ],\n [\n -91.0546875,\n 33.100745405144245\n ],\n [\n -89.7802734375,\n 33.50475906922609\n ],\n [\n -88.9453125,\n 35.639441068973944\n ],\n [\n -88.9892578125,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Kansas River provides drinking water to about 800,000 people in northeastern Kansas. Water-treatment facilities that use the Kansas River as a water-supply source use chemical and physical processes during water treatment to remove contaminants before public distribution. Advanced notification of changing water-quality conditions near water-supply intakes allows water-treatment facilities to proactively adjust treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne, collected water-quality data at the Kansas River at Wamego (USGS site 06887500; hereafter referred to as the “Wamego site”) and De Soto (USGS site 06892350; hereafter referred to as the “De Soto site”) monitoring sites to update previously published regression models relating continuous water-quality sensor measurements, streamflow, and seasonal components to discretely sampled water-quality constituent concentrations or densities. Linear regression analysis was used to update and develop models for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and fecal indicator bacteria at the Wamego and De Soto monitoring sites using data collected during July 2012 through September 2019. The water-quality information documented in this report can be used as guidance for water-treatment processes and to characterize changes in water-quality conditions in the Kansas River over time that would not be otherwise possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211018","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Williams, T.J., 2021, Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019: U.S. Geological Survey Open-File Report 2021–1018, 18 p., https://doi.org/10.3133/ofr20211018.","productDescription":"Report: vii, 18 p.; Appendixes: 1–32; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120556","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384812,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1018/downloads","text":"Appendixes 1–32","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1018 Appendixes 1–32"},{"id":384811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1018/ofr20211018.pdf","text":"Report","size":"1.16 MB","description":"OFR 2021–1018"},{"id":384810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1018/coverthb.jpg"},{"id":384813,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.66845703124999,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 38.151837403006766\n ],\n [\n -94.5703125,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 39.977120098439634\n ],\n [\n -97.66845703124999,\n 38.151837403006766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Emergent late Pliocene and Pleistocene shoreline deposits, morphologically identifiable Pleistocene shoreline units, and seaward-facing scarps characterize the easternmost Atlantic Coastal Plain (ACP) of the United States of America. In some areas of the ACP, these deposits, units, and scarps have been studied in detail. Within these areas, temporal and spatial data are sufficient for time-depositional frameworks for shoreline-evolution to have been developed and published. For other areas, such as the southeastern Atlantic Coastal Plain (SEACP), available data are conflicting and (or) insufficient to develop such a framework, or to make shoreline correlations. Differential epeirogenic uplift and shoreline deformation, resulting from mantle-flow and climate-induced isostatic adjustments, complicate regional shoreline correlations. In the SEACP, the topographically prominent Orangeburg Scarp (hereafter, the Scarp) rises tens of meters in elevation from southeastern Georgia to southeastern North Carolina. The degree to which the Scarp and shoreline units seaward of the Scarp are deformed continues to be debated, but there is general agreement that the lower Savannah River area (LSRA) of Georgia and South Carolina is the least deformed area of the SEACP.
This paper synthesizes published and previously unpublished numerical age and stratigraphic data for emergent Pliocene and younger shoreline deposits in the LSRA in Georgia. Age data are applied to these shoreline deposits as they are delineated (map units) on the 1976 geologic map of Georgia by Lawton and others. Age assignments are based on stratigraphic position, fossil content, soil and weathering diagnostic properties, and numerical ages as determined by meteoric Beryllium‑10 paleosol residence time (10BePRT), optically stimulated luminescence (OSL), uranium disequilibrium series (U-series), amino acid racemization (AAR), and radiocarbon (14C) analyses. These data provide a preliminary Pliocene-Pleistocene geochronology for the Orangeburg Scarp and shoreline deposits seaward of the Scarp in the LSRA of Georgia. Minimum ages and age ranges indicate the following:
Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
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
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. 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Intrusion whole-rock geochemistry and phenocryst and/or mineral trace element compositions coupled with new zircon U-Pb geo-chronology and zircon in situ Lu-Hf isotopes document distinct pulses of magma from beneath the caldera complex. Fourteen intrusions, the Chiquito Peak Tuff, and the dacite of Fisher Gulch were dated, showing intrusive magmatism began after the 28.8 Ma eruption of the Chiquito Peak Tuff and continued to 24 Ma. Additionally, magmatic-hydrothermal mineralization is associated with the intrusive magmatism within and around the margins of the Platoro caldera complex.
After caldera collapse, three plutons were emplaced within the subsided block between ca. 28.8 and 28.6 Ma. These have broadly similar modal miner-alogy and whole-rock geochemistry. Despite close temporal relations between the tuff and the intrusions, mineral textures and compositions indicate that the larger two intracaldera intrusions are discrete later pulses of magma. Intrusions outside the caldera are younger, ca. 28–26.3 Ma, and smaller in exposed area. They contain abundant glomerocrysts and show evidence of open-system processes such as magma mixing and crystal entrainment. The protracted magmatic history at the Platoro caldera complex documents the diversity of the multiple discrete magma pulses needed to generate large composite volcanic fields.
Elizabeth Gierlowski-Kordesch (1956–2016) was a leader and innovator in the specialty field of limnogeology since its beginnings in the late 1980s. Her excitement for field work and examining sediments was contagious, and she was always testing new research ideas. Beth would have been thrilled with the diversity of papers presented in the volume and the wide array of techniques used to determine the history, geochemistry, paleontology, and paleoclimate preserved in the sediments in basins that are located on every continent except Australia and Antarctica. She would also have been delighted that half the chapters were first authored by highly cited women scientists. Beth spent her career teaching, mentoring, conducting research with students and colleagues, and planning limnogeology conferences, books, and field trips. Her contributions span deep-time lakes from North and South America, Africa, Asia, and Europe, starting with her work on the Lower Jurassic East Berlin Formation where she conducted her Ph.D. research. Her work with Kerry Kelts at the University of Minnesota produced two books summarizing global lake research. These volumes are still used by many researchers, particularly as a starting point in their limnogeological studies. Her collaboration with Springer Nature® resulted in the series entitled Syntheses in Limnogeology, a publication that likely would not exist without her enthusiasm and perseverance. The papers in this second volume in the series describe a variety of Jurassic to modern lakes that range from fresh to hypersaline, shallow to deep, vary in size from <1 km2 to 100s of km2, and are found in a number of tectonic settings. Various proxies, including microfossils and trace fossils and analyses of lacustrine sedimentology, stratigraphy, and stable isotopes are used to evaluate the sediment cores and stratigraphic sections to evaluate human and climate influences on the environment, the effects of tectonic, seismic, and volcanic activity, and variations in hydrology. The contributions in this volume reflect the diverse research that Beth conducted herself and we hope is a fitting honor to one of the founding scientists of Limnogeology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66576-0_1","usgsCitation":"Rosen, M., Park Boush, L., Finkelstein, D., and Pla-Pueyo, S., 2021, Introduction to limnogeology: Progress, challenges, and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, chap. of Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, p. 3-16, https://doi.org/10.1007/978-3-030-66576-0_1.","productDescription":"14 p.","startPage":"3","endPage":"16","ipdsId":"IP-122491","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":413425,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosen, Michael R. 0000-0003-3991-0522","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":224435,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park Boush, Lisa 0000-0002-8169-4600","orcid":"https://orcid.org/0000-0002-8169-4600","contributorId":302674,"corporation":false,"usgs":false,"family":"Park Boush","given":"Lisa","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finkelstein, David 0000-0002-9787-1675","orcid":"https://orcid.org/0000-0002-9787-1675","contributorId":302675,"corporation":false,"usgs":false,"family":"Finkelstein","given":"David","email":"","affiliations":[{"id":65529,"text":"Hobart and William Smith Colleges","active":true,"usgs":false}],"preferred":false,"id":865072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pla-Pueyo, Sila 0000-0003-4884-4096","orcid":"https://orcid.org/0000-0003-4884-4096","contributorId":302677,"corporation":false,"usgs":false,"family":"Pla-Pueyo","given":"Sila","email":"","affiliations":[{"id":33422,"text":"University of Granada","active":true,"usgs":false}],"preferred":false,"id":865073,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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 - 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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.
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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":70228564,"text":"70228564 - 2021 - Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","interactions":[],"lastModifiedDate":"2022-02-14T15:58:57.838707","indexId":"70228564","displayToPublicDate":"2021-04-01T09:48:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","title":"Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska","docAbstract":"Polymorphism facilitates coexistence of divergent morphs (e.g., phenotypes) of the same species by minimizing intraspecific competition, especially when resources are limiting. Arctic char (Salvelinus sp.) are a Holarctic fish often forming morphologically, and sometimes genetically, divergent morphs. In this study, we assessed the morphological and genetic diversity and divergence of 263 individuals from seven populations of arctic char with varying length-frequency distributions across two distinct groups of lakes in northern Alaska. Despite close geographic proximity, each lake group occurs on landscapes with different glacial ages and surface water connectivity, and thus was likely colonized by fishes at different times. Across lakes, a continuum of physical (e.g., lake area, maximum depth) and biological characteristics (e.g., primary productivity, fish density) exists, likely contributing to characteristics of present-day char populations. Although some lakes exhibit bimodal size distributions, using model-based clustering of morphometric traits corrected for allometry, we did not detect morphological differences within and across char populations. Genomic analyses using 15,934 SNPs obtained from genotyping by sequencing demonstrated differences among lake groups related to historical biogeography, but within lake groups and within individual lakes, genetic differentiation was not related to total body length. We used PERMANOVA to identify environmental and biological factors related to observed char size structure. Significant predictors included water transparency (i.e., a primary productivity proxy), char density (fish·ha-1), and lake group. Larger char occurred in lakes with greater primary production and lower char densities, suggesting less intraspecific competition and resource limitation. Thus, char populations in more productive and connected lakes may prove more stable to environmental changes, relative to food-limited and closed lakes, if lake productivity increases concomitantly. Our findings provide some of the first descriptions of genomic characteristics of char populations in arctic Alaska, and offer important consideration for the persistence of these populations for subsistence and conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.7211","usgsCitation":"Klobucar, S., Rick, J., Mandeville, E., Wagner, C.E., and Budy, P., 2021, Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska: Ecology and Evolution, v. 11, no. 7, p. 3040-3057, https://doi.org/10.1002/ece3.7211.","productDescription":"18 p.","startPage":"3040","endPage":"3057","ipdsId":"IP-117493","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.7211","text":"Publisher Index Page"},{"id":395888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Mountain Range, Toolik Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.74365234374997,\n 68.49604022839505\n ],\n [\n -148.95538330078125,\n 68.49604022839505\n ],\n [\n -148.95538330078125,\n 68.70448628851169\n ],\n [\n -149.74365234374997,\n 68.70448628851169\n ],\n [\n -149.74365234374997,\n 68.49604022839505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Klobucar, Stephen L.","contributorId":172291,"corporation":false,"usgs":false,"family":"Klobucar","given":"Stephen L.","affiliations":[],"preferred":false,"id":834610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rick, Jessica A.","contributorId":276155,"corporation":false,"usgs":false,"family":"Rick","given":"Jessica A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":834611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":834612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":834613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834609,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238781,"text":"70238781 - 2021 - Heterotrophic respiration and the divergence of productivity and carbon sequestration","interactions":[],"lastModifiedDate":"2022-12-12T15:08:10.493171","indexId":"70238781","displayToPublicDate":"2021-04-01T09:00:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Heterotrophic respiration and the divergence of productivity and carbon sequestration","docAbstract":"Net primary productivity (NPP) and net ecosystem production (NEP) are often used interchangeably, as their difference, heterotrophic respiration (soil heterotrophic CO2 efflux, RSH = NPP−NEP), is assumed a near-fixed fraction of NPP. Here, we show, using a range-wide replicated experimental study in loblolly pine (Pinus taeda) plantations that RSH responds differently than NPP to fertilization and drought treatments, leading to the divergent responses of NPP and NEP. Across the natural range of the species, the moderate responses of NPP (+11%) and RSH (−7%) to fertilization combined such that NEP increased nearly threefold in ambient control and 43% under drought treatment. A 13% decline in RSH under drought led to a 26% increase in NEP while NPP was unaltered. Such drought benefit for carbon sequestration was nearly twofold in control, but disappeared under fertilization. Carbon sequestration efficiency, NEP:NPP, varied twofold among sites, and increased up to threefold under both drought and fertilization.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL092366","usgsCitation":"Noormets, A., Bracho, R., Ward, E., Seiler, J., Strahm, B., Lin, W., McElligott, K., Domec, J., Gonzalez-Benecke, C., Jokela, E.J., Markewitz, D.M., Meek, C., Miao, G., McNulty, S.G., King, J., Samuelson, L., Sun, G., Teskey, R., Vogel, J., Will, R.E., Yang, J., and Martin, T.A., 2021, Heterotrophic respiration and the divergence of productivity and carbon sequestration: Geophysical Research Letters, v. 48, no. 7, e2020GL092366, 10 p., https://doi.org/10.1029/2020GL092366.","productDescription":"e2020GL092366, 10 p.","ipdsId":"IP-128106","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452842,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl092366","text":"External 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Kristin","contributorId":299767,"corporation":false,"usgs":false,"family":"McElligott","given":"Kristin","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":858586,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Domec, Jean-Christophe","contributorId":146460,"corporation":false,"usgs":false,"family":"Domec","given":"Jean-Christophe","email":"","affiliations":[],"preferred":false,"id":858587,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gonzalez-Benecke, Carlos","contributorId":299768,"corporation":false,"usgs":false,"family":"Gonzalez-Benecke","given":"Carlos","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":858588,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jokela, Eric J.","contributorId":299769,"corporation":false,"usgs":false,"family":"Jokela","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":858589,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Markewitz, Daniel M.","contributorId":222099,"corporation":false,"usgs":false,"family":"Markewitz","given":"Daniel","email":"","middleInitial":"M.","affiliations":[{"id":37470,"text":"University of Georgia, Athens","active":true,"usgs":false}],"preferred":false,"id":858590,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meek, Cassandra","contributorId":299770,"corporation":false,"usgs":false,"family":"Meek","given":"Cassandra","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":858591,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Miao, Guofang","contributorId":299771,"corporation":false,"usgs":false,"family":"Miao","given":"Guofang","email":"","affiliations":[{"id":64946,"text":"Fujian Normal University","active":true,"usgs":false}],"preferred":false,"id":858592,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McNulty, Steve G.","contributorId":299772,"corporation":false,"usgs":false,"family":"McNulty","given":"Steve","email":"","middleInitial":"G.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":858593,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"King, John S.","contributorId":299773,"corporation":false,"usgs":false,"family":"King","given":"John S.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":858594,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Samuelson, Lisa","contributorId":222101,"corporation":false,"usgs":false,"family":"Samuelson","given":"Lisa","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":858595,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sun, Ge","contributorId":145893,"corporation":false,"usgs":false,"family":"Sun","given":"Ge","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":858596,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Teskey, Robert","contributorId":299774,"corporation":false,"usgs":false,"family":"Teskey","given":"Robert","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":858597,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Vogel, Jason R.","contributorId":288914,"corporation":false,"usgs":false,"family":"Vogel","given":"Jason R.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":858598,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Will, Rodney E.","contributorId":141456,"corporation":false,"usgs":false,"family":"Will","given":"Rodney","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":858599,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Yang, Jinyan","contributorId":166929,"corporation":false,"usgs":false,"family":"Yang","given":"Jinyan","email":"","affiliations":[],"preferred":false,"id":858600,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Martin, Timothy A.","contributorId":299775,"corporation":false,"usgs":false,"family":"Martin","given":"Timothy","email":"","middleInitial":"A.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":858601,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70229495,"text":"70229495 - 2021 - The formation, transport, and breakup of submerged oil-particle aggregates in Great Lakes riverine environments","interactions":[],"lastModifiedDate":"2022-03-09T14:30:11.986408","indexId":"70229495","displayToPublicDate":"2021-04-01T08:21:30","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":10269,"text":"Research Brief","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"EPA/600/S-21/061","title":"The formation, transport, and breakup of submerged oil-particle aggregates in Great Lakes riverine environments","docAbstract":"The formation, transport, and resuspension of oil-particle aggregates (OPA) in freshwater environments are of much interest to oil spill responders and scientists, especially as transportation of light and heavy crude oils has substantially increased across river corridors and coasts in the Great Lakes Basin. The persistent sheening from accumulated OPA along 60 km of the Kalamazoo River in Michigan’s lower peninsula resulted in a lengthy and expensive cleanup for the 2010 Enbridge Line 6B pipeline rupture. The interaction of oil with river mineral sediment and organic matter and its long-term fate depend on the physical properties of the oil and particles as well as the environmental setting of river, its climate, morphology, currents and mixing opportunities. This research brief describes the expanded work conducted for the cleanup for the 2010 Enbridge Line 6B pipeline rupture and includes laboratory experiments of aggregate characteristics with Cold Lake Blend and a range of sediment particle sizes, addition of an OPA formation algorithm to an existing sediment contaminant transport model, and development of a simplified, particle-tracking based rapid response model of OPA formation, transport, and deposition. A description of formulas developed for mixing energy in rivers in terms of river properties is also included.","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Berens, J., Boufadel, M., Fitzpatrick, F., Garcia, M., Hassan, J.S., Hayter, E., Jones, L., Mravik, S., and Waterman, D., 2021, The formation, transport, and breakup of submerged oil-particle aggregates in Great Lakes riverine environments (Revised March 7, 2022): Research Brief EPA/600/S-21/061, 26 p.","productDescription":"26 p.","ipdsId":"IP-130968","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396889,"type":{"id":15,"text":"Index Page"},"url":"https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=CESER&dirEntryId=354255"}],"country":"United States","state":"Michigan","otherGeospatial":"Kalamazoo River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.21795654296875,\n 42.23461834757937\n ],\n [\n -85.50384521484375,\n 42.23461834757937\n ],\n [\n -85.50384521484375,\n 42.6844544397102\n ],\n [\n -86.21795654296875,\n 42.6844544397102\n ],\n [\n -86.21795654296875,\n 42.23461834757937\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Revised March 7, 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berens, John","contributorId":288282,"corporation":false,"usgs":false,"family":"Berens","given":"John","email":"","affiliations":[{"id":61720,"text":"University of IL","active":true,"usgs":false}],"preferred":false,"id":837606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boufadel, Michel C.","contributorId":176576,"corporation":false,"usgs":false,"family":"Boufadel","given":"Michel C.","affiliations":[],"preferred":false,"id":837607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia, Marcelo H.","contributorId":74236,"corporation":false,"usgs":false,"family":"Garcia","given":"Marcelo H.","affiliations":[{"id":33106,"text":"University of Illinois at Urbana Champaign","active":true,"usgs":false}],"preferred":false,"id":837609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hassan, Jacob S.","contributorId":143668,"corporation":false,"usgs":false,"family":"Hassan","given":"Jacob","email":"","middleInitial":"S.","affiliations":[{"id":15293,"text":"USEPA Region V","active":true,"usgs":false}],"preferred":false,"id":837610,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayter, Earl","contributorId":143665,"corporation":false,"usgs":false,"family":"Hayter","given":"Earl","affiliations":[{"id":15290,"text":"USACE, Coastal and Hydraulic Laboratory","active":true,"usgs":false}],"preferred":false,"id":837611,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Lori","contributorId":288283,"corporation":false,"usgs":false,"family":"Jones","given":"Lori","email":"","affiliations":[{"id":61723,"text":"formerly with the University of IL","active":true,"usgs":false}],"preferred":false,"id":837612,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mravik, Susan","contributorId":288284,"corporation":false,"usgs":false,"family":"Mravik","given":"Susan","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":837613,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Waterman, David","contributorId":143664,"corporation":false,"usgs":false,"family":"Waterman","given":"David","email":"","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":837614,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"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.
Investigators rely on brood surveys to estimate annual fecundity of game birds. However, investigators often do not account for factors that influence brood detection probability nor rarely document how much females and their broods are disturbed (flush rates) during surveys, which could lead to biased survival estimates. We used 45 radio-tagged female Greater Sage-Grouse (Centrocercus urophasianus) with broods to compare detection probabilities and document disturbance among four survey methods to allow future investigators to select the method that best meets their objectives. These methods included daytime flush, daytime visual, nocturnal spotlight, and fecal surveys at nocturnal roost sites, with the latter being a novel method. We used Cormack–Jolly–Seber (CJS) models to compare detection probability and daily survival estimates for visual and fecal surveys of broods 0–47 d post-hatch and a double-survey approach to compare detection probabilities among flush, fecal, and spotlight surveys ~42 d post-hatch when investigators often determine brood fate. From CJS models, detection probability for visual surveys increased with brood age (0.618–0.881), whereas detection probability for fecal surveys did not (0.748). Daily survival probability estimates increased with brood age and differed annually based on fecal surveys (2016: 0.978–1.000 and 2017: 0.839–0.998). We detected age-specific daily survival probability with visual surveys (0.956–0.997), but not annual differences. Based on the double-survey approach, detection probability was high (0.857–1.000) for all methods. We flushed ~310–750% fewer females and broods during fecal and spotlight surveys than during both types of daytime surveys. Our results highlight the need to account for detection probabilities among methods and document disturbance to hens and broods that can help investigators design surveys to minimize impacts to birds. Furthermore, our result suggest that actions to improve brood survival during the first week post-hatch may improve local recruitment.
Seismograms from the South Pole have been important for seismological observations for over six decades by providing (until 2007) the only continuous seismic records from the interior of the Antarctic continent. The South Pole, Antarctica station has undergone many updates over the years, including conversion to a digital recording station as part of the Global Seismographic Network (GSN) in 1991 and being relocated to multiple deep (
Prymnesium parvum is a euryhaline, toxin-producing microalga. Although its abundance in inland waters and growth potential in the laboratory is reduced at high salinity (>20), the ability of inland strains to adjust their growth after long-term residence in high salinity is uncertain. An inland strain of P. parvum maintained at salinity of 5 in modified artificial seawater medium (ASM-5) was subjected to the following treatments over five sequential batch culture rounds: ASM-5 (control); modified ASM at salinity of 30, raised with NaCl; modified ASM at salinity incrementally increased to 30 with NaCl; and Instant Ocean® at salinity of 30 (IO-30). Exponential growth rate (r) was reduced when salinity was increased from 5 to 30 in ASM but returned to control values during the second round. When salinity was incrementally increased, a reduction in r still occurred when salinity reached 25-30. Maximum density was reduced at salinity of 30 in ASM upon abrupt transfer or incremental increase, and compensation did not occur. Growth performance in IO-30 was comparable to control values. In conclusion, (i) long-term compensation for acute inhibitory effects of high salinity occurred for r but not maximum density, (ii) incremental increases in salinity did not prevent growth inhibition, suggesting the existence of a salinity threshold of 25–30 for onset of salinity stress, and (iii) the presence of a seawater-like salt mixture prevented growth inhibition by high salinity. These findings provide new insights on P. parvum's long-term ability to adjust its growth in environments of different salinity and ionic composition.
","language":"English","publisher":"Phycological Society of America","doi":"10.1111/jpy.13172","usgsCitation":"Richardson, E.T., and Patino, R., 2021, Long-term salinity change and growth of the harmful alga, Prymnesium parvum: Journal of Phycology, v. 57, no. 4, p. 1335-1344, https://doi.org/10.1111/jpy.13172.","productDescription":"10 p.","startPage":"1335","endPage":"1344","ipdsId":"IP-109389","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Emily T.","contributorId":274795,"corporation":false,"usgs":false,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":833318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833317,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":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}]}} ]}