{"pageNumber":"466","pageRowStart":"11625","pageSize":"25","recordCount":165969,"records":[{"id":70254798,"text":"70254798 - 2021 - Mechanistic invasive species management models and their application in conservation","interactions":[],"lastModifiedDate":"2024-06-12T00:12:37.397059","indexId":"70254798","displayToPublicDate":"2021-09-17T19:10:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Mechanistic invasive species management models and their application in conservation","docAbstract":"

Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.

","language":"English","publisher":"Wiley","doi":"10.1111/csp2.533","usgsCitation":"Thompson, B., Alexander J. Jensen, and Converse, S.J., 2021, Mechanistic invasive species management models and their application in conservation: Conservation Science and Practice, v. 3, no. 11, e533, 18 p., https://doi.org/10.1111/csp2.533.","productDescription":"e533, 18 p.","ipdsId":"IP-127931","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450776,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.533","text":"Publisher Index Page"},{"id":429930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":338325,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle K.","affiliations":[],"preferred":false,"id":902600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander J. Jensen","contributorId":337657,"corporation":false,"usgs":false,"family":"Alexander J. Jensen","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902602,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223904,"text":"sir20215036 - 2021 - Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015","interactions":[],"lastModifiedDate":"2021-09-20T11:38:52.269074","indexId":"sir20215036","displayToPublicDate":"2021-09-17T12:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5036","displayTitle":"Estimates of Public-Supply, Domestic, and Irrigation Water Withdrawal, Use, and Trends in the Upper Rio Grande Basin, 1985 to 2015","title":"Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015","docAbstract":"

The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.

This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.

Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.

The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.

Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.

Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.

The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.

More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.

The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-09-17","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":823213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flickinger, Allison K. 0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":214612,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":200849,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[],"preferred":false,"id":823216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":217877,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823217,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":823218,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262594,"text":"70262594 - 2021 - The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows","interactions":[],"lastModifiedDate":"2025-01-21T16:56:03.166383","indexId":"70262594","displayToPublicDate":"2021-09-17T10:52:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows","docAbstract":"

Since 2011, seafloor temperatures, pressures, and seismic ground motions have been measured by the seafloor cabled Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) on the Nankai margin. These measurements, high-resolution bathymetry, and abundant contextual information make the DONET region seem ideally suited to provide constraints on seismic shaking-triggered sediment slope failures and gravity flows, particularly since numerous published studies have linked paleo- to modern earthquakes to failures and flows within the DONET. The occurrences of the local 2016 M6.0 Mie-ken and regional M7.0 Kumamoto earthquakes within and at regional distances, respectively, from the DONET data set provided an opportunity to explore this potential. We used DONET seismic recordings of the posited triggering shaking and to search for submarine slide signals and continuous temperature and pressure data to detect pulses of warm and densified water indicative of passing flows. We developed and applied a variety of analytical methods to eliminate signals generated by water column processes, while leaving slope failures and sediment gravity flow anomalies as residuals. Our explorations yielded no evidence that earthquake shaking initiated either phenomenon, which we suggest reflects the finicky nature both of the detection of and the physical processes that contribute to slope failures and flows (i.e., both require satisfying precise suites of conditions). Nonetheless, this negative result, our analyses, and the estimates of physical properties we derived for them, provide useful lessons and inputs for future studies.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB022588","usgsCitation":"Gomberg, J.S., Ariyoshi, K., Hautala, S., and Johnson, H., 2021, The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows: Journal of Geophysical Research, v. 126, e2021JB022588, 26 p., https://doi.org/10.1029/2021JB022588.","productDescription":"e2021JB022588, 26 p.","ipdsId":"IP-123242","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 135.61973524947484,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 34.29410264461973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":924643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, H.P.","contributorId":349727,"corporation":false,"usgs":false,"family":"Johnson","given":"H.P.","affiliations":[],"preferred":false,"id":924644,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224538,"text":"70224538 - 2021 - Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","interactions":[],"lastModifiedDate":"2022-01-24T16:16:48.709305","indexId":"70224538","displayToPublicDate":"2021-09-17T10:11:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5020,"text":"Microorganisms","active":true,"publicationSubtype":{"id":10}},"title":"Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","docAbstract":"

Freshwater mussels (Unionida) are suffering mass mortality events worldwide, but the causes remain enigmatic. Here, we describe an analysis of bacterial loads, community structure, and inferred metabolic pathways in the hemolymph of pheasantshells (Actinonaias pectorosa) from the Clinch River, USA, during a multi-year mass mortality event. Bacterial loads were approximately 2 logs higher in moribund mussels (cases) than in apparently healthy mussels (controls). Bacterial communities also differed between cases and controls, with fewer sequence variants (SVs) and higher relative abundances of the proteobacteria Yokenella regensburgei and Aeromonas salmonicida in cases than in controls. Inferred bacterial metabolic pathways demonstrated a predominance of degradation, utilization, and assimilation pathways in cases and a predominance of biosynthesis pathways in controls. Only two SVs correlated with Clinch densovirus 1, a virus previously shown to be strongly associated with mortality in this system: Deinococcota and Actinobacteriota, which were associated with densovirus-positive and densovirus-negative mussels, respectively. Overall, our results suggest that bacterial invasion and shifts in the bacterial microbiome during unionid mass mortality events may result from primary insults such as viral infection or environmental stressors. If so, bacterial communities in mussel hemolymph may be sensitive, if generalized, indicators of declining mussel health.

","language":"English","publisher":"MDPI","doi":"10.3390/microorganisms9091976","usgsCitation":"Richard, J., Campbell, L., Leis, E., Agbalog, R., Dunn, C.D., Waller, D.L., Knowles, S., Putnam, J.G., and Goldberg, T., 2021, Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve: Microorganisms, v. 9, no. 9, 1976, 15 p., https://doi.org/10.3390/microorganisms9091976.","productDescription":"1976, 15 p.","ipdsId":"IP-131640","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":450779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/microorganisms9091976","text":"Publisher Index Page"},{"id":389816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee, Virginia","otherGeospatial":"Clinch River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.2489013671875,\n 36.416862115300304\n ],\n [\n -81.485595703125,\n 37.413800350662896\n ],\n [\n -82.177734375,\n 37.71859032558816\n ],\n [\n -84.44091796875,\n 36.43012234551576\n ],\n [\n -83.2489013671875,\n 36.416862115300304\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richard, Jordan","contributorId":211789,"corporation":false,"usgs":false,"family":"Richard","given":"Jordan","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":823973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Lewis J. 0000-0002-7852-2250","orcid":"https://orcid.org/0000-0002-7852-2250","contributorId":244773,"corporation":false,"usgs":false,"family":"Campbell","given":"Lewis J.","affiliations":[],"preferred":false,"id":823974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Eric","contributorId":179325,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","affiliations":[],"preferred":false,"id":823975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agbalog, Rose","contributorId":239870,"corporation":false,"usgs":false,"family":"Agbalog","given":"Rose","affiliations":[{"id":48017,"text":"USFWS-Virginia Field Office","active":true,"usgs":false}],"preferred":false,"id":823976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Christopher D.","contributorId":225521,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":41155,"text":"Department of Pathobiological Sciences, University of Wisconsin-Madison,","active":true,"usgs":false}],"preferred":false,"id":823977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":823979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Putnam, Joel G. 0000-0002-5464-4587 jgputnam@usgs.gov","orcid":"https://orcid.org/0000-0002-5464-4587","contributorId":5783,"corporation":false,"usgs":true,"family":"Putnam","given":"Joel","email":"jgputnam@usgs.gov","middleInitial":"G.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823980,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goldberg, Tony","contributorId":211788,"corporation":false,"usgs":false,"family":"Goldberg","given":"Tony","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":823981,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255182,"text":"70255182 - 2021 - Mechanistic invasive species management models and their application in conservation","interactions":[],"lastModifiedDate":"2024-06-13T13:39:08.914783","indexId":"70255182","displayToPublicDate":"2021-09-17T08:36:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Mechanistic invasive species management models and their application in conservation","docAbstract":"

Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.533","usgsCitation":"Thompson, B.K., Olden, J., and Converse, S.J., 2021, Mechanistic invasive species management models and their application in conservation: Conservation Science and Practice, v. 3, no. 11, e533, 18 p., https://doi.org/10.1111/csp2.533.","productDescription":"e533, 18 p.","ipdsId":"IP-126294","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.533","text":"Publisher Index Page"},{"id":430128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":338912,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle","email":"","middleInitial":"K.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olden, Julian D.","contributorId":338914,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903683,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224582,"text":"70224582 - 2021 - Evaluation of SWIR crop residue bands for the Landsat Next mission","interactions":[],"lastModifiedDate":"2021-09-29T13:25:56.428904","indexId":"70224582","displayToPublicDate":"2021-09-17T08:22:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of SWIR crop residue bands for the Landsat Next mission","docAbstract":"
This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13183718","usgsCitation":"Hively, W.D., Lamb, B.T., Daughtry, C.S., Serbin, G., Dennison, P., Kokaly, R.F., Wu, Z., and Masek, J.G., 2021, Evaluation of SWIR crop residue bands for the Landsat Next mission: Remote Sensing, v. 13, no. 18, 3718, 31 p., https://doi.org/10.3390/rs13183718.","productDescription":"3718, 31 p.","ipdsId":"IP-130273","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":450786,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183718","text":"Publisher Index Page"},{"id":436198,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XK3867","text":"USGS data release","linkHelpText":"Reflectance spectra of agricultural field conditions supporting remote sensing evaluation of non-photosynthetic vegetation cover (ver. 1.1, November 2022)"},{"id":389948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":824166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daughtry, Craig S.T.","contributorId":214079,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S.T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":824167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Serbin, Guy 0000-0001-9345-1772","orcid":"https://orcid.org/0000-0001-9345-1772","contributorId":266030,"corporation":false,"usgs":false,"family":"Serbin","given":"Guy","email":"","affiliations":[{"id":54864,"text":"EOAnalytics","active":true,"usgs":false}],"preferred":false,"id":824168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dennison, Phillip 0000-0002-0241-1917","orcid":"https://orcid.org/0000-0002-0241-1917","contributorId":266031,"corporation":false,"usgs":false,"family":"Dennison","given":"Phillip","email":"","affiliations":[{"id":54865,"text":"Dept. Geography, Utah State University","active":true,"usgs":false}],"preferred":false,"id":824169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":824170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":824171,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":824172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224630,"text":"70224630 - 2021 - Engaging with stakeholders to produce actionable science: A framework and guidance","interactions":[],"lastModifiedDate":"2021-11-01T16:03:52.700493","indexId":"70224630","displayToPublicDate":"2021-09-17T08:16:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9363,"text":"Weather Climate and Society","active":true,"publicationSubtype":{"id":10}},"title":"Engaging with stakeholders to produce actionable science: A framework and guidance","docAbstract":"

Natural and cultural resource managers are increasingly working with the scientific community to create information on how best to adapt to the current and projected impacts of climate change. Engaging with these managers is a strategy that researchers can use to ensure that scientific outputs and findings are actionable (or useful and usable). In this article, the authors adapt Davidson’s wheel of participation to characterize and describe common stakeholder engagement strategies across the spectrum of Inform, Consult, Participate, and Empower. This adapted framework provides researchers with a standardized vocabulary for describing their engagement approach, guidance on how to select an approach, methods for implementing engagement, and potential barriers to overcome. While there is often no one “best” approach to engaging with stakeholders, researchers can use the objectives of their project and the decision context in which their stakeholders operate to guide their selection. Researchers can also revisit this framework over time as their project objectives shift and their stakeholder relationships evolve.

","language":"English","publisher":"American Meteorological Society","doi":"10.1175/WCAS-D-21-0046.1","usgsCitation":"Bamzai-Dodson, A., Cravens, A.E., Wade, A., and McPherson, R.A., 2021, Engaging with stakeholders to produce actionable science: A framework and guidance: Weather Climate and Society, v. 13, no. 4, p. 1027-1041, https://doi.org/10.1175/WCAS-D-21-0046.1.","productDescription":"15 p.","startPage":"1027","endPage":"1041","ipdsId":"IP-127628","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":390111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bamzai-Dodson, Aparna 0000-0002-2444-9051","orcid":"https://orcid.org/0000-0002-2444-9051","contributorId":247300,"corporation":false,"usgs":true,"family":"Bamzai-Dodson","given":"Aparna","affiliations":[{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":824445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":824446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wade, Alisa 0000-0003-3976-2224","orcid":"https://orcid.org/0000-0003-3976-2224","contributorId":266157,"corporation":false,"usgs":true,"family":"Wade","given":"Alisa","email":"","affiliations":[{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":824447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McPherson, Renee A. 0000-0002-1497-9681","orcid":"https://orcid.org/0000-0002-1497-9681","contributorId":266158,"corporation":false,"usgs":false,"family":"McPherson","given":"Renee","email":"","middleInitial":"A.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":824448,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224985,"text":"70224985 - 2021 - Distinguishing between regression model fits to global mean sea level reconstructions","interactions":[],"lastModifiedDate":"2021-10-13T12:40:05.364629","indexId":"70224985","displayToPublicDate":"2021-09-17T07:38:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9378,"text":"Journal of Geophysical Research- Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Distinguishing between regression model fits to global mean sea level reconstructions","docAbstract":"

Global mean sea level (GMSL) has been rising since the last century, posing a serious challenge for the coastal areas. A variety of regression models have been utilized for determining GMSL rise over the past one hundred years, resulting in a large spread of sea level rise rates and multidecadal variations. In this study, we develop a new nonparametric noise model that is data-dependent and considers overfitting due to regression. The noise model is used to determine whether one regression model has significantly better skill than others over the period 1900–2010. The choices of background noise and GMSL reconstruction influence whether two sea level models can be statistically distinguished. With our new nonparametric noise spectra, the differences of model skills in explaining sea level variance are significant only in 34% of model comparisons. However, stepwise trends with three inflection points are significantly more skillful than the linear, quadratic, or exponential trend for most GMSL reconstructions, suggesting the importance of multidecadal variability of sea level rise in the twentieth century. Nevertheless, stepwise trend models cannot be distinguished from models with a long-term harmonic oscillation, indicating that the shape of multidecadal variability is not conclusive. The multidecadal variability is also significant in the steric and barystatic sea level contributions and is related to both natural and anthropogenic forcings. GMSL predictions based on regression fits in the twentieth century underestimate the sea level rise rate over the period 2011–2020 because the sea level acceleration in the recent decade (2011–2020) is not well represented.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JC017347","usgsCitation":"Zhu, Y., Mitchum, G.T., Doran, K.S., Chambers, D.P., and Liang, X., 2021, Distinguishing between regression model fits to global mean sea level reconstructions: Journal of Geophysical Research- Oceans, v. 126, no. 10, e2021JC017347, 33 p., https://doi.org/10.1029/2021JC017347.","productDescription":"e2021JC017347, 33 p.","ipdsId":"IP-130906","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390467,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhu, Yingli","contributorId":267367,"corporation":false,"usgs":false,"family":"Zhu","given":"Yingli","email":"","affiliations":[{"id":55477,"text":"University of South Florida, St. Petersburg and University of Delaware","active":true,"usgs":false}],"preferred":false,"id":825059,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchum, Gary T.","contributorId":267368,"corporation":false,"usgs":false,"family":"Mitchum","given":"Gary","email":"","middleInitial":"T.","affiliations":[{"id":55478,"text":"University of South Florida, St. Petersburg","active":true,"usgs":false}],"preferred":false,"id":825060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727 kdoran@usgs.gov","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":148059,"corporation":false,"usgs":true,"family":"Doran","given":"Kara","email":"kdoran@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":825061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chambers, Don P.","contributorId":267369,"corporation":false,"usgs":false,"family":"Chambers","given":"Don","email":"","middleInitial":"P.","affiliations":[{"id":55478,"text":"University of South Florida, St. Petersburg","active":true,"usgs":false}],"preferred":false,"id":825062,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liang, Xinfeng","contributorId":267370,"corporation":false,"usgs":false,"family":"Liang","given":"Xinfeng","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":825063,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224567,"text":"70224567 - 2021 - Integrating airborne and mobile lidar data with UAV photogrammetry for rapid assessment of changing forest snow depth and cover","interactions":[],"lastModifiedDate":"2021-09-28T12:30:14.036461","indexId":"70224567","displayToPublicDate":"2021-09-17T07:27:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9346,"text":"Science of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Integrating airborne and mobile lidar data with UAV photogrammetry for rapid assessment of changing forest snow depth and cover","docAbstract":"

Forest structure and topography can influence the ecohydrologic function and resiliency to drought and changing climate. It is, therefore, important to understand how forest restoration treatments alter snowpack distribution and design the treatments accordingly. We use a combination of aerial lidar, multi-temporal terrestrial mobile lidar, and UAV photogrammetry to estimate rapidly changing snow depth and cover in northern Arizona, USA. We then examine the impact of forest structure and topography on snow depth and snow cover persistence to inform forest restoration treatments. Our results show that mobile lidar data can be used to estimate snow depth with standard errors of 8 cm when differenced with snow-off airborne lidar data. UAV-based Structure-from-Motion data can be used to estimate snow cover persistence with 92–97% overall accuracies in forested ecosystems. Random forest models indicate spatially varying importance of forest structural and topographic variables in predicting snow depth and cover persistence, when summarized at different spatial scales (from 5 m to 250 m) and with variable directional location offsets. Forest snow depth was best explained (R2 ≈ 0.46) by canopy height metrics at summary scales of >75 m, while canopy cover was most important at summary scales of <40 m (R2 ≈ 0.3). Snow cover persistence was best explained at very local scales by canopy cover (R2 ≈ 0.38) and less so at larger scales (>75 m) by topographic and forest patch characteristics (R2 ≈ 0.34). Our results demonstrate that 3-dimensional datasets are critical in rapidly characterizing changing snowpack to better understand the impacts of forest structure and topography to inform forest restoration treatment designs. The relationships observed in our study can inform currently ongoing regional-scale forest restoration in the southwest to improve forest health and resiliency.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.srs.2021.100029","usgsCitation":"Donager, J., Sankey, T., Sanchez-Meador, A., Sankey, J.B., and Springer, A.E., 2021, Integrating airborne and mobile lidar data with UAV photogrammetry for rapid assessment of changing forest snow depth and cover: Science of Remote Sensing, v. 4, 100029, 12 p., https://doi.org/10.1016/j.srs.2021.100029.","productDescription":"100029, 12 p.","ipdsId":"IP-104074","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.srs.2021.100029","text":"Publisher Index Page"},{"id":389863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Donager, Jonathon","contributorId":196772,"corporation":false,"usgs":false,"family":"Donager","given":"Jonathon","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":824106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen","contributorId":97000,"corporation":false,"usgs":true,"family":"Sankey","given":"Temuulen","affiliations":[],"preferred":false,"id":824107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanchez-Meador, Andrew","contributorId":266020,"corporation":false,"usgs":false,"family":"Sanchez-Meador","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":824108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":824109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Springer, Abraham E. 0000-0003-4826-9124","orcid":"https://orcid.org/0000-0003-4826-9124","contributorId":216651,"corporation":false,"usgs":false,"family":"Springer","given":"Abraham","email":"","middleInitial":"E.","affiliations":[{"id":39494,"text":"School of Earth Science and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":824110,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230355,"text":"70230355 - 2021 - Female persistence during toxicant treatment predicts survival probability of offspring in invasive brown treesnakes (Boiga irregularis)","interactions":[],"lastModifiedDate":"2022-04-08T12:20:32.767332","indexId":"70230355","displayToPublicDate":"2021-09-17T07:18:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Female persistence during toxicant treatment predicts survival probability of offspring in invasive brown treesnakes (Boiga irregularis)","docAbstract":"

Assessing the long-term efficacy of control methods is a critical component of invasive species management. For example, if traits related to control have significant heritability or are influenced by maternal effects, control methods may lose efficacy over time. The potential for these effects can be evaluated via parent/offspring survival analysis, which concomitantly recasts adaptive management as an evolutionary force for invasive species. However, difficulties can arise when the life history of an invasive is cryptic, precluding direct observations of familial relationships. Genomic pedigree reconstruction can facilitate such analyses by assigning offspring to parents in invasive species for which mating and reproduction are difficult to study. Here, we use genomic pedigree reconstruction to quantify parental longevity and probability of offspring survival for brown treesnakes (Boiga irregularis) on Guam in a landscape treated with toxic baits simulating application via an aerial delivery system (ADS). To do so, we used 398 single nucleotide polymorphisms (SNPs) to update an existing multi-generational genomic pedigree for a geographically-closed population of brown treesnakes. This facilitated assignment of parents to juveniles born during three consecutive years of toxic bait application under a simulated aerial treatment program (N = 72). We found that the offspring of dams that persisted until the end of the study displayed greater survival probability (cox proportional hazard model, P < 0.001), yet there was no such effect for sires. This sex-specific relationship between parental longevity and offspring survival indicates that heritability of traits contributing to resistance to ADS is unlikely, but it supports a role of maternal effects that could undermine ADS. Our study identifies potential risks associated with control efforts and also highlights the utility of parent-offspring survival analyses informed by genomic pedigree reconstruction as a tool for adaptive management.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01827","usgsCitation":"Levine, B., Yackel Adams, A.A., Douglas, M., Douglas, M., and Nafus, M., 2021, Female persistence during toxicant treatment predicts survival probability of offspring in invasive brown treesnakes (Boiga irregularis): Global Ecology and Conservation, v. 31, e01827, 7 p., https://doi.org/10.1016/j.gecco.2021.e01827.","productDescription":"e01827, 7 p.","ipdsId":"IP-125961","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450793,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01827","text":"Publisher Index Page"},{"id":436199,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9STNBGE","text":"USGS data release","linkHelpText":"Offspring, dam, sire pedigree assignments in a managed population of Brown Treesnakes on Guam"},{"id":398385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Brenna A","contributorId":243207,"corporation":false,"usgs":false,"family":"Levine","given":"Brenna A","affiliations":[{"id":38022,"text":"University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":840051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":840052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, Marlis","contributorId":289912,"corporation":false,"usgs":false,"family":"Douglas","given":"Marlis","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":840053,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, Michael","contributorId":289913,"corporation":false,"usgs":false,"family":"Douglas","given":"Michael","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":840054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nafus, Melia Gail 0000-0002-7325-3055","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":245717,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia Gail","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":840055,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229190,"text":"70229190 - 2021 - Honey bee foraged pollen reveals temporal changes in pollen protein content and changes in forager choice for abundant versus high protein flowers","interactions":[],"lastModifiedDate":"2022-03-02T13:13:23.833928","indexId":"70229190","displayToPublicDate":"2021-09-17T07:10:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10144,"text":"Agriculture, Ecosystems, and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Honey bee foraged pollen reveals temporal changes in pollen protein content and changes in forager choice for abundant versus high protein flowers","docAbstract":"

Protein derived from pollen is an essential component of healthy bee diets. Protein content in honey bee foraged-pollen varies temporally and spatially, but the drivers underlying this variation remain poorly characterized. We assessed the temporal and spatial variation in honey bee collected pollen in 12 Michigan apiaries over 3 summers (2015–2017). We simultaneously monitored forage in flowering habitats (uncultivated floristically-rich areas and conservation program land) near these apiaries throughout the growing season. We used these data, along with data from the literature on plant pollen protein content, to determine if honey bees collected a greater proportion of pollen from plant species growing in higher abundance or from plant species that have higher protein content. Protein content in honey bee collected pollen decreased from July to September every year, and there were among-year differences in pollen protein, highlighting the temporal variation in protein collected by these insects. Pollen protein was spatially consistent and broad-scale land use categories were not correlated with pollen protein content. Rather, our findings suggest flowering habitats found across land use categories can support honey bee foraging, which may confound broader land use effects. In early July and in early September, colonies collected a greater proportion of pollen from plants that grew in greater abundance in flowering habitats, but from late July through August, a greater proportion of pollen was collected from high-protein taxa, regardless of abundance. This suggests different factors may influence pollen forager decision-making throughout the season as colony needs and/or available forage communities change. Insights into the role of plant abundance and protein content on foraging could deepen our understanding of honey bee foraging behavior and help to inform habitat restoration programs for improved honey bee nutrition outcomes.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.agee.2021.107645","usgsCitation":"Quinlan, G., Milbrath, M., Otto, C., Smart, A., Iwanowicz, D.D., Isaacs, R., and Cornman, R.S., 2021, Honey bee foraged pollen reveals temporal changes in pollen protein content and changes in forager choice for abundant versus high protein flowers: Agriculture, Ecosystems, and Environment, v. 322, 107645, 10 p., https://doi.org/10.1016/j.agee.2021.107645.","productDescription":"107645, 10 p.","ipdsId":"IP-126772","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":450795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agee.2021.107645","text":"Publisher Index Page"},{"id":396646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.396484375,\n 42.032974332441405\n ],\n [\n -84.759521484375,\n 42.032974332441405\n ],\n [\n -84.759521484375,\n 43.24520272203356\n ],\n [\n -86.396484375,\n 43.24520272203356\n ],\n [\n -86.396484375,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"322","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Quinlan, Gabriela","contributorId":287574,"corporation":false,"usgs":false,"family":"Quinlan","given":"Gabriela","email":"","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":836899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milbrath, Megan","contributorId":287575,"corporation":false,"usgs":false,"family":"Milbrath","given":"Megan","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":836900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":836901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smart, Autumn","contributorId":287583,"corporation":false,"usgs":false,"family":"Smart","given":"Autumn","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":836902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":287584,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":836903,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isaacs, Rufus","contributorId":287577,"corporation":false,"usgs":false,"family":"Isaacs","given":"Rufus","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":836904,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":836905,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250006,"text":"70250006 - 2021 - High- and low-latitude forcings drive Atacama Desert rainfall variations over the past 16,000 years","interactions":[],"lastModifiedDate":"2023-11-12T13:10:15.117075","indexId":"70250006","displayToPublicDate":"2021-09-17T07:05:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"High- and low-latitude forcings drive Atacama Desert rainfall variations over the past 16,000 years","docAbstract":"
Late Quaternary precipitation dynamics in the central Andes have been linked to both high- and low-latitude atmospheric teleconnections. We use present-day relationships between fecal pellet diameters from ashy chinchilla rats (Abrocoma cinerea) and mean annual rainfall to reconstruct the timing and magnitude of pluvials (wet episodes) spanning the past 16,000 years in the Atacama Desert based on 81 14C-dated A. cinerea paleomiddens. A transient climate simulation shows that pluvials identified at 15.9 to 14.8, 13.0 to 8.6, and 8.1 to 7.6 ka B.P. can be linked to North Atlantic (high-latitude) forcing (e.g., Heinrich Stadial 1, Younger Dryas, and Bond cold events). Holocene pluvials at 5.0 to 4.6, 3.2 to 2.1, and 1.4 to 0.7 ka B.P. are not simulated, implying low-latitude internal variability forcing (i.e., ENSO regime shifts). These results help constrain future central Andean hydroclimatic variability and hold promise for reconstructing past climates from rodent middens in desert ecosystems worldwide.
","language":"English","publisher":"Science","doi":"10.1126/sciadv.abg1333","usgsCitation":"Gonzalez-Pinilla, F.J., Latorre, C.L., Rojas, M., Houston, J., Rocuant, M.I., Maldonado, A., Santoro, C., Quade, J., and Betancourt, J.L., 2021, High- and low-latitude forcings drive Atacama Desert rainfall variations over the past 16,000 years: Science Advances, v. 7, no. 38, eabg1333, 10 p., https://doi.org/10.1126/sciadv.abg1333.","productDescription":"eabg1333, 10 p.","ipdsId":"IP-120342","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":450797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abg1333","text":"External Repository"},{"id":422514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.19630215060201,\n -17.482067217629123\n ],\n [\n -73.19630215060201,\n -29.540369537651948\n ],\n [\n -65.98927090060181,\n -29.540369537651948\n ],\n [\n -65.98927090060181,\n -17.482067217629123\n ],\n [\n -73.19630215060201,\n -17.482067217629123\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","issue":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez-Pinilla, Francisco J.","contributorId":331519,"corporation":false,"usgs":false,"family":"Gonzalez-Pinilla","given":"Francisco","email":"","middleInitial":"J.","affiliations":[{"id":79225,"text":"Pontificia Universidad Católica de Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":887958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Latorre, Claudio L.","contributorId":331520,"corporation":false,"usgs":false,"family":"Latorre","given":"Claudio","email":"","middleInitial":"L.","affiliations":[{"id":79225,"text":"Pontificia Universidad Católica de Chile, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":887959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rojas, M.","contributorId":331521,"corporation":false,"usgs":false,"family":"Rojas","given":"M.","email":"","affiliations":[{"id":79227,"text":"Center for Climate and Resilience Research (CR)2 & Departamento de Geofísica, Universidad de Chile, Santiago, Chile.","active":true,"usgs":false}],"preferred":false,"id":887960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houston, J.","contributorId":331522,"corporation":false,"usgs":false,"family":"Houston","given":"J.","email":"","affiliations":[{"id":79228,"text":"Rocklea, Dorchester, DT2 9EN, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":887961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rocuant, M. Igancia","contributorId":331523,"corporation":false,"usgs":false,"family":"Rocuant","given":"M.","email":"","middleInitial":"Igancia","affiliations":[{"id":79230,"text":"Centro UC Desierto de Atacama & Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.","active":true,"usgs":false}],"preferred":false,"id":887962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maldonado, A.","contributorId":195883,"corporation":false,"usgs":false,"family":"Maldonado","given":"A.","email":"","affiliations":[],"preferred":false,"id":887963,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Santoro, Calogero","contributorId":331524,"corporation":false,"usgs":false,"family":"Santoro","given":"Calogero","email":"","affiliations":[{"id":79231,"text":"Instituto de Alta Investigación (IAI), Universidad de Tarapacá, Arica, Chile","active":true,"usgs":false}],"preferred":false,"id":887964,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quade, Jay","contributorId":104197,"corporation":false,"usgs":true,"family":"Quade","given":"Jay","email":"","affiliations":[],"preferred":false,"id":887980,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Betancourt, Julio L. 0000-0002-7165-0743 jlbetanc@usgs.gov","orcid":"https://orcid.org/0000-0002-7165-0743","contributorId":3376,"corporation":false,"usgs":true,"family":"Betancourt","given":"Julio","email":"jlbetanc@usgs.gov","middleInitial":"L.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":887965,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225495,"text":"70225495 - 2021 - Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity","interactions":[],"lastModifiedDate":"2021-11-16T15:52:35.903673","indexId":"70225495","displayToPublicDate":"2021-09-17T06:40:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity","docAbstract":"

Upscaling groundwater flow is a fundamental challenge in hydrogeology. This study proposed time-fractional flow equations (t-FFEs) for upscaling long-term, transient groundwater flow and propagation of pressure heads in heterogeneous media. Monte Carlo simulations showed that, with increasing variance and correlation of the hydraulic conductivity (K), flow dynamics gradually deviated from Darcian flow and exhibit sub-diffusive, time-dependent evolution which can be separated into three major stages. At the early stage, the interconnected high-K zones dominated flow, while at intermediate times, the transverse flow due to mixed high- and low-K zones caused delayed rise of the piezometric head. At late times when flow in the relatively high-K domains reached stability, cells with very low-K continued to block the entry of water and generate “islands” with low piezometric head, significantly extending the temporal evolution of the piezometric head. The elongated water breakthrough curve cannot be quantified by the flow equation with an effective K, the space-fractional flow equation, or the multi-rate mass transfer (MRMT) flow model with a few rates, motivating the development of t-FFEs assuming temporally non-Darcian flow. Model applications showed that both the early and intermediate stages of flow dynamics can be captured by a single-index t-FFE (whose index is the exponent of the power-law probability density function of the random operational time for water parcels), but the overall evolution of flow dynamics, especially the enhanced retention of flow at later times, required a distributed-order t-FFE with variable indexes for different flow phases that can dominate flow dynamics at different stages. Therefore, transient groundwater flow in aquifers with spatially stationary heterogeneity can be temporally non-Darcian and non-stationary, due to the time-sensitive, combined effects of interconnected high-K channels and isolated low-K deposits on flow dynamics (which is the hydrogeological mechanism for the temporally non-Darcian flow and sub-diffusive pressure propagation), whose long-term behavior can be quantified by multi-index stochastic models.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR029554","usgsCitation":"Xia, Y., Zhang, Y., Green, C., and Fogg, G., 2021, Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity: Water Resources Research, v. 57, no. 11, e2020WR029554, 30 p., https://doi.org/10.1029/2020WR029554.","productDescription":"e2020WR029554, 30 p.","ipdsId":"IP-119835","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450800,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/0pv0z4t0","text":"External Repository"},{"id":390598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xia, Yuan","contributorId":267790,"corporation":false,"usgs":false,"family":"Xia","given":"Yuan","email":"","affiliations":[{"id":55508,"text":"Guilin University of Technology","active":true,"usgs":false}],"preferred":false,"id":825278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Yong","contributorId":214040,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":825279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":825280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fogg, Graham 0000-0003-0676-1911","orcid":"https://orcid.org/0000-0003-0676-1911","contributorId":267791,"corporation":false,"usgs":false,"family":"Fogg","given":"Graham","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":825281,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226462,"text":"70226462 - 2021 - A new species of Helianthus (Asteracae) from Clark County, Nevada","interactions":[],"lastModifiedDate":"2021-11-18T12:39:36.836733","indexId":"70226462","displayToPublicDate":"2021-09-17T06:38:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2639,"text":"Madroño","active":true,"publicationSubtype":{"id":10}},"title":"A new species of Helianthus (Asteracae) from Clark County, Nevada","docAbstract":"

Helianthus devernii T.M.Draper is described as a new endemic species from two small desert spring populations found within Red Rock Canyon National Conservation Area, Clark County, NV. Morphological data and nuclear ribosomal ITS marker data place it in section Ciliares series Pumili. Furthermore, the molecular data allies it most closely to H. pumilus Nutt. Helianthus devernii differs from H. pumilus by its sessile one nerved opposite and alternate leaves, glabrous glaucous stems, and overall smaller heads. The two known populations of H. devernii of approximately 400 individuals occur near the Las Vegas Valley and are threatened by heavy recreational use and exotic plants and animals. A key to the species of Helianthus of Nevada is presented.

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Trent M","contributorId":269391,"corporation":false,"usgs":false,"family":"Draper","given":"Trent","email":"","middleInitial":"M","affiliations":[{"id":55968,"text":"Roy, Utah","active":true,"usgs":false}],"preferred":false,"id":826998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":826999,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224196,"text":"sir20215070 - 2021 - Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable","interactions":[],"lastModifiedDate":"2021-09-16T16:17:28.171074","indexId":"sir20215070","displayToPublicDate":"2021-09-16T09:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5070","displayTitle":"Estimating Invertebrate Response to Changes in Total Nitrogen, Total Phosphorus, and Specific Conductance at Sites Where Invertebrate Data are Unavailable","title":"Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable","docAbstract":"

The purpose of this report is to describe a possible approach to estimate changes in invertebrate taxa richness at sites with known water-quality trends but no invertebrate data. In this study, data from 1,322 sites were used to describe invertebrate response to changes in total nitrogen, total phosphorus, or specific conductance, and to estimate changes in invertebrate taxa richness at 259 sites with reported water-quality trends but no invertebrate data. Sites were stratified using propensity score analysis to control for confounding factors (for example, climate, land use, land cover). Generalized linear models were developed to describe changes in invertebrate taxa richness along gradients of total nitrogen, total phosphorus, and specific conductance values. The magnitude and direction of invertebrate response to gradients of water quality varied among parameters and strata, with changes in invertebrate taxa richness per natural log unit change in concentration ranging from –7 to +6. However, estimated changes in invertebrate taxa richness at sites with known water-quality trends were much less and did not exceed three taxa until changes in concentration were greater than 50 percent. Applying this approach provides (1) a first screening to identify where changes in invertebrate taxa richness are likely to occur and (2) the necessary groundwork to improve estimation of invertebrate response to trends in water quality where biological data are lacking.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215070","usgsCitation":"Zuellig, R.E., and Carlisle, D.M., 2021, Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable: U.S. Geological Survey Scientific Investigations Report 2021–5070, 24 p., https://doi.org/10.3133/sir20215070.","productDescription":"Report: v, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119660","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":389267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMFACO","text":"USGS data release","linkHelpText":"Datasets for estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable"},{"id":389266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5070/sir20215070.pdf","text":"Report","size":"5.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5070"},{"id":389265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5070/coverthb.jpg"}],"contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225

","tableOfContents":"","publishedDate":"2021-09-16","noUsgsAuthors":false,"publicationDate":"2021-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":223188,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823308,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240159,"text":"70240159 - 2021 - Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley","interactions":[],"lastModifiedDate":"2023-01-31T15:30:33.818125","indexId":"70240159","displayToPublicDate":"2021-09-16T09:24:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7446,"text":"FastTIMES","active":true,"publicationSubtype":{"id":10}},"title":"Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley","docAbstract":"

The Rio Grande is the primary source of recharge to the Mesilla Basin/Conejos-Médanos aquifer system (“Mesilla Basin aquifer system”) in the Mesilla Valley of New Mexico and Texas. The Mesilla Basin aquifer system is the primary source of water supply to several large cities along the United States–Mexico border. Identifying gaining and losing reaches of the Rio Grande in the Mesilla Valley is therefore critical for managing the quality and quantity of surface and groundwater-resources available to stakeholders in the Mesilla Valley and downstream. A Waterborne gradient Self-Potential (WaSP) logging survey was completed in the Rio Grande across the Mesilla Valley between June 26 and July 2, 2020 to identify reaches where surface-water gains and losses were occurring by interpreting an estimate of the streaming-potential component of the electrostatic field in the river, measured during bank-full flow. The WaSP survey, completed as part of the Transboundary Aquifer Assessment Program, began at Leasburg Dam State Park, New Mexico near the northern terminus of the Mesilla Valley and ended ~72 kilometers (km) downstream in Canutillo, Texas. Electric potential data indicated a net losing condition for ~32 km between Leasburg Dam and Mesilla Diversion Dam in New Mexico, with one 200-m long reach showing a localized gaining condition. Downstream from Mesilla Diversion Dam, electric-potential data indicated a neutral-to-mild gaining condition for 12-km that transitioned to a mild-to-moderate gaining condition between 12 and ~22 km from the dam before transitioning back to a losing condition along the remaining 18 km of the survey reach. The interpreted gaining and losing reaches are substantiated by potentiometric surface mapping in hydrostratigraphic units of the Mesilla Basin aquifer system between 2010 and 2011 and streamflow gains and losses quantified from annual streamflow gaging at 16 stations along the survey reach between 1988 and 1998 and between 2004 and 2013. The gaining and losing reaches of the Rio Grande in the Mesilla Valley, interpreted from electric potential data, compare notably well with streamflow gains and losses quantified at 16 locations along the 72-km long survey reach.

","language":"English","publisher":"Environmental and Engineering Geophysical Society","usgsCitation":"Ikard, S., and Teeple, A., 2021, Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley: FastTIMES, v. 26, no. 3, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-132631","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":412505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412478,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://fasttimesonline.co/waterborne-gradient-self-potential-wasp-logging-in-the-rio-grande-to-map-localized-and-regional-surface-and-groundwater-exchanges-across-the-mesilla-valley/"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Mesilla Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.44524373222445,\n 31.760633532310962\n ],\n [\n -106.6117012652096,\n 32.435777766328556\n ],\n [\n -106.97488133717744,\n 32.96635814299131\n ],\n [\n -107.04549968450479,\n 33.44327742390567\n ],\n [\n -107.58018145712424,\n 33.388542989837234\n ],\n [\n -107.50956310979689,\n 32.79267559856237\n ],\n [\n -107.07576469050201,\n 32.350592719244176\n ],\n [\n -106.67223127720436,\n 31.854939592806915\n ],\n [\n -106.4502878998906,\n 31.709153308763334\n ],\n [\n -106.44524373222445,\n 31.760633532310962\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232911,"text":"70232911 - 2021 - Does earthquake stress drop increase with depth in the crust?","interactions":[],"lastModifiedDate":"2022-07-13T11:44:36.452937","indexId":"70232911","displayToPublicDate":"2021-09-16T06:40:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Does earthquake stress drop increase with depth in the crust?","docAbstract":"

We combine earthquake spectra from multiple studies to investigate whether the increase in stress drop with depth often observed in the crust is real, or an artifact of decreasing attenuation (increasing Q) with depth. In many studies, empirical path and attenuation corrections are assumed to be independent of the earthquake source depth. We test this assumption by investigating whether a realistic increase in Q with depth (as is widely observed) could remove some of the observed apparent increase in stress drop with depth. We combine event spectra, previously obtained using spectral decomposition methods, for over 50,000 earthquakes (M0 to M5) from 12 studies in California, Nevada, Kansas and Oklahoma. We find that the relative high-frequency content of the spectra systematically increases with increasing earthquake depth, at all magnitudes. By analyzing spectral ratios between large and small events as a function of source depth, we explore the relative importance of source and attenuation contributions to this observed depth dependence. Without any correction for depth-dependent attenuation, we find a systematic increase in stress drop, rupture velocity, or both, with depth, as previously observed. When we add an empirical, depth-dependent attenuation correction, the depth dependence of stress drop systematically decreases, often becoming negligible. The largest corrections are observed in regions with the largest seismic velocity increase with depth. We conclude that source parameter analyses, whether in the frequency or time domains, should not assume path terms are independent of source depth, and should more explicitly consider the effects of depth-dependent attenuation.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB022314","usgsCitation":"Abercrombie, R., Trugman, D.T., Shearer, P.M., Chen, X., Zhang, J., Pennington, C.N., Hardebeck, J.L., Goebel, T.H., and Ruhl, C.J., 2021, Does earthquake stress drop increase with depth in the crust?: Journal of Geophysical Research, v. 126, no. 10, e2021JB022314, 22 p., https://doi.org/10.1029/2021JB022314.","productDescription":"e2021JB022314, 22 p.","ipdsId":"IP-129396","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":403587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Abercrombie, Rachel E.","contributorId":293131,"corporation":false,"usgs":false,"family":"Abercrombie","given":"Rachel E.","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":846471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trugman, Daniel T.","contributorId":197011,"corporation":false,"usgs":false,"family":"Trugman","given":"Daniel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":846472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shearer, Peter M.","contributorId":197012,"corporation":false,"usgs":false,"family":"Shearer","given":"Peter","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":846473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Xiaowei","contributorId":293132,"corporation":false,"usgs":false,"family":"Chen","given":"Xiaowei","email":"","affiliations":[{"id":48773,"text":"University of Oklahoma, Norman, Oklahoma","active":true,"usgs":false}],"preferred":false,"id":846474,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Jiewen","contributorId":293133,"corporation":false,"usgs":false,"family":"Zhang","given":"Jiewen","email":"","affiliations":[{"id":48773,"text":"University of Oklahoma, Norman, Oklahoma","active":true,"usgs":false}],"preferred":false,"id":846475,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pennington, Colin Nathanael 0000-0002-1474-9368","orcid":"https://orcid.org/0000-0002-1474-9368","contributorId":293134,"corporation":false,"usgs":true,"family":"Pennington","given":"Colin","email":"","middleInitial":"Nathanael","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":846476,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":846477,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goebel, Thomas H W","contributorId":293136,"corporation":false,"usgs":false,"family":"Goebel","given":"Thomas","email":"","middleInitial":"H W","affiliations":[{"id":63233,"text":"Center for Earthquake Research and Information, University of Memphis","active":true,"usgs":false}],"preferred":false,"id":846478,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ruhl, Christine J","contributorId":293138,"corporation":false,"usgs":false,"family":"Ruhl","given":"Christine","email":"","middleInitial":"J","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":846479,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255559,"text":"70255559 - 2021 - Monthly river temperature trends across the US confound annual changes","interactions":[],"lastModifiedDate":"2024-06-24T11:20:26.360545","indexId":"70255559","displayToPublicDate":"2021-09-16T06:06:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Monthly river temperature trends across the US confound annual changes","docAbstract":"

Climate variations and human modifications of the water cycle continue to alter the Earth's surface water and energy exchanges. It is therefore critical to ascertain how these changes impact water quality and aquatic ecosystem habitat metrics such as river temperatures. Though river temperature trend analyses exist in the literature, studies on seasonal trends in river temperatures across large spatial extents, e.g. the contiguous United States (US), are limited. As we show through both annual and monthly trend analyses for 20 year (n = 138 sites) and 40 year (n = 40 sites) periods, annual temperature trends across the US mask extensive monthly variability. While most sites exhibited annual warming trends, these annual trends obscured sub-annual cooling trends at many sites. Monthly trend anomalies were spatially organized, with persistent regional patterns at both reference and human-impacted sites. The largest warming and cooling anomalies happened at human impacted sites and during summer months. Though our analysis points to coherence in trends as well as the overall impact of human activity in driving these patterns, we did not investigate the impact of river temperature observation accuracy on reported trends, an area needed for future work. Overall, these patterns emphasize the need to consider sub-annual behavior when managing the ecological impacts of river temperature throughout lotic networks.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ac2289","usgsCitation":"Kelleher, C., Golden, H.E., and Archfield, S.A., 2021, Monthly river temperature trends across the US confound annual changes: Environmental Research Letters, v. 16, 104006, 10 p., https://doi.org/10.1088/1748-9326/ac2289.","productDescription":"104006, 10 p.","ipdsId":"IP-130039","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac2289","text":"Publisher Index Page"},{"id":430440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -129.57106419384183,\n 51.98412232384288\n ],\n [\n -129.57106419384183,\n 24.426025005896022\n ],\n [\n -65.41090794384175,\n 24.426025005896022\n ],\n [\n -65.41090794384175,\n 51.98412232384288\n ],\n [\n -129.57106419384183,\n 51.98412232384288\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelleher, Christa","contributorId":242798,"corporation":false,"usgs":false,"family":"Kelleher","given":"Christa","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":904669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Golden, Heather E.","contributorId":202423,"corporation":false,"usgs":false,"family":"Golden","given":"Heather","email":"","middleInitial":"E.","affiliations":[{"id":36429,"text":"USEPA ORD","active":true,"usgs":false}],"preferred":false,"id":904670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":904671,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224252,"text":"ofr20211081 - 2021 - Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California—Fall 2019, sixth annual repor","interactions":[],"lastModifiedDate":"2021-09-16T11:50:21.374636","indexId":"ofr20211081","displayToPublicDate":"2021-09-15T13:31:12","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1081","displayTitle":"Kelp Forest Monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2019, Sixth Annual Report","title":"Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California—Fall 2019, sixth annual repor","docAbstract":"

The U.S. Geological Survey conducts ecological monitoring of rocky subtidal communities at four permanent sites around San Nicolas Island. The sites—Nav Fac 100, West End, Dutch Harbor, and Daytona 100—were based on ones that had been monitored since 1980 by the U.S. Geological Survey and, in cooperation with the U.S. Navy, were combined or expanded in 2014 for better comparability with monitoring programs conducted at the other California Channel Islands. At the sites, we counted a suite of kelps and invertebrates on benthic band transects, measured bottom cover of algae and sessile invertebrate species in quadrats, and counted and sized fish on swimming transects. Holdfast diameter and number of stipes of giant kelp (Macrocystis pyrifera) were recorded on these transects and size data were collected for urchins, sea stars, and shelled mollusks. Bottom temperatures were recorded at hourly intervals by archival data loggers that were deployed at the sites. Typically, this monitoring work is conducted semi-annually, in fall and spring. Because the spring 2020 trip was cancelled due to the Coronavirus Disease 2019 pandemic, this report focuses primarily on data collected in fall 2019 and makes comparisons with data collected in previous years, beginning in fall 2014.

The sites are distributed around the island and differ in their physical and ecological characteristics. Nav Fac 100, situated on the north side of San Nicolas Island, has a relatively low benthic profile. The invasive brown alga Sargassum horneri was first observed at this site in 2015. West End, to the southwest of the island, also lacks much bottom relief but has more crevice habitat associated with boulders. For almost three decades, West End has been a focal point for the small, but growing, population of southern sea otters (Enhydra lutris nereis) at the island. Dutch Harbor, on the south side, has many high relief rocky reefs and had the greatest fish and non-motile invertebrate densities. Daytona 100, on the southeast side, has moderate relief and has remained a patchwork of kelp and sea urchin dominated areas.

There were no major changes at the sites since spring 2019, but some trends observed during the last few years continued whereas others changed. Red urchins continued a declining trend (observed during the last 4 years) at Daytona 100. The wavy turban snail (Megastraea undosa) began to increase rapidly at Nav Fav 100 in 2015 and has subsequently been increasing at the other sites as well, after more than a decade of very low numbers at all sites. Sea star wasting syndrome, which has devastated multiple species of sea stars along the Pacific coast of North America, affected most species at San Nicolas Island in the year prior to the fall 2014 sampling. Since then, there has been a reduction in the number of bat stars (Patiria miniata), and very few sea stars of other species have been observed. There has been a slight recovery of P. miniata since 2016 but little sign of change in other species. All the sites had a slight decline in the densities of purple urchins following an increase during the previous 2 years. Long-term data are presented to illustrate trends and changes during almost four decades of monitoring this dynamic system.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211081","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Wildlife Program","usgsCitation":"Kenner, M.C., and Tomoleoni, J., 2021, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California—Fall 2019, sixth annual report: U.S. Geological Survey Open-File Report 2021–1081, 97 p., https://doi.org/10.3133/ofr20211081.","productDescription":"ix, 97 p.","numberOfPages":"97","onlineOnly":"Y","ipdsId":"IP-128532","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":389297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1081/covrthb.jpg"},{"id":389298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1081/ofr20211081.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389299,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1081/ofr20211081.xml"},{"id":389300,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1081/images"}],"country":"California","otherGeospatial":"Naval Base Ventura County, San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.59304809570312,\n 33.20824398778792\n ],\n [\n -119.42138671875,\n 33.20824398778792\n ],\n [\n -119.42138671875,\n 33.29724715520414\n ],\n [\n -119.59304809570312,\n 33.29724715520414\n ],\n [\n -119.59304809570312,\n 33.20824398778792\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-09-15","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":823359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":823360,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223926,"text":"sir20215091 - 2021 - Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","interactions":[],"lastModifiedDate":"2021-09-16T11:37:37.283212","indexId":"sir20215091","displayToPublicDate":"2021-09-15T08:47:15","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":"2021-5091","displayTitle":"Evaluation of Hydrologic Simulation Models for Fields with Subsurface Drainage to Mitigated Wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","title":"Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","docAbstract":"

Proper identification of wetlands, along with a better understanding of the hydrology of mitigated wetlands, is needed to assist with conservation efforts aimed at maintaining the productivity and ecological function (wetland mitigation) of agricultural lands. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service, completed a study to evaluate two models for simulating hydrologic conditions in fields with subsurface drainage to mitigated wetlands at several sites in North Dakota. These two models were evaluated as possible tools for water resource managers to use for designing wetland mitigation projects in the area in the future.

The Soil-Plant-Atmosphere-Water (SPAW) model simulates the daily hydrologic water budgets of agricultural landscapes by two linked routines, one for farm fields (field hydrology) and one for impoundments such as wetlands and ponds (pond model). The DRAINMOD model was used in conjunction with the SPAW model because although the SPAW model can be used to simulate the hydrology of small drainage basins containing wetlands, the SPAW model does not contain routines to simulate drainage, either subsurface drainage or surface (drainage ditches), that can directly affect the wetland hydrology. The wetlands in the study areas in this report are all downstream from and adjacent to drained agricultural fields. SPAW and DRAINMOD models were developed and calibrated at three study areas (study areas B, D, and S) to evaluate how the models simulated field-scale hydrologic characteristics and the water balance in wetlands from January 1, 2003, through December 31, 2018.

The SPAW model developed for study area B included five modeled fields in the field hydrology portion of SPAW that contributed inflow to one wetland simulated in the pond model portion of SPAW. Simulated wetland water depths were most similar to water depths measured at site BWET1, with an absolute mean error of 0.10 foot and a root mean square error of 0.14 foot. Site BWET2 had slightly larger errors, with an absolute mean error of 0.22 foot and a root mean square error of 0.28 foot. Simulated water depths were similar to the pattern of measured water depths at BWET1 and BWET2 from about mid-April 2018 through about mid-September 2018, but overpredicted water depths in the fall from about mid-September 2018 through about mid-October 2018.

The SPAW model developed for study area D included six modeled fields in the field hydrology portion of SPAW that contributed inflow to five wetlands connected in series in the pond model portion of SPAW. Simulated water depths compared relatively well to water depths in the five wetlands, with the absolute mean error ranging from 0.17 foot (DWET1) to 0.39 foot (DWET2), and the root mean square error ranging from 0.28 foot (DWET1) to 0.56 foot (DWET5).

The SPAW model developed for study area S included one modeled field in the field hydrology portion of SPAW that contributed inflow to one wetland in the pond model portion of SPAW. Among the SPAW models developed for the three study areas, the model for study area S had the best comparison between simulated and measured water depths, with an absolute mean error of 0.06 foot and a root mean square error of 0.10 foot.

DRAINMOD models were developed and calibrated at the three study areas and provided inflow from subsurface drainage discharge to the SPAW models for simulating water levels in wetlands in the study areas. The calibrated DRAINMOD model for study area B showed the variability of hydrologic processes in the modeled field throughout the wide range of hydrologic conditions from January 1, 2003, through December 31, 2018. In general, the discharge through the modeled subsurface drainage system was in the spring and early summer (April through June) most years, with little to no discharge later in the year. Although the subsurface drainage system in study area D was the most complex among the three study areas and was simplified into a uniform system within DRAINMOD, simulated water table depths at study area D correlated better to measured water table depths compared to results from the model applications at the other two study areas. Simulated water table depths had an absolute mean error of 0.30 foot and root mean square error of 0.37 foot at site DGW1 and an absolute mean error of 0.29 foot and a root mean square error of 0.34 foot at site DGW2. Although the subsurface drainage system in study area S was the simplest and the modeled field was the smallest among the three study areas, simulated water table depths at study area S did not correlate as well to measured water table depths compared to results from the model applications at the other two study areas.

The SPAW and DRAINMOD model applications at the three study areas in southeast North Dakota adequately simulated the hydrologic processes for fields with subsurface drainage that are connected to adjacent wetlands. However, more measured data would be needed to fully evaluate the models throughout the range of possible climatic conditions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215091","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service","usgsCitation":"Galloway, J.M., Tatge, W.S., and Wheeling, S.L., 2021, Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5091, 58 p., https://doi.org/10.3133/sir20215091.","productDescription":"Report: vi, 58 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-128613","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":389200,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5091/images"},{"id":389199,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.xml","size":"386 kB","linkFileType":{"id":8,"text":"xml"}},{"id":389198,"rank":3,"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"},{"id":389196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5091/coverthb.jpg"},{"id":389197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.pdf","text":"Report","size":"5.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5091"}],"country":"United States","state":"North Dakota","county":"Barnes County, Dickey County, Sargent County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-97.961,47.241],[-97.7061,47.2402],[-97.7071,47.1529],[-97.7062,47.0665],[-97.7059,46.9792],[-97.6839,46.9792],[-97.683,46.6294],[-97.81,46.6297],[-97.9059,46.6293],[-97.9357,46.6294],[-98.0349,46.6293],[-98.1889,46.6297],[-98.2868,46.63],[-98.3152,46.63],[-98.4396,46.6296],[-98.4412,46.9789],[-98.4685,46.9788],[-98.4677,47.2402],[-97.9958,47.2411],[-97.9764,47.2412],[-97.961,47.241]]],[[[-98.0095,45.9355],[-98.164,45.9356],[-98.1849,45.9355],[-98.3472,45.9355],[-98.3537,45.9355],[-98.7267,45.9373],[-98.7273,45.9373],[-99.0021,45.9393],[-99.0054,45.9393],[-99.0073,46.0262],[-99.0061,46.1132],[-99.0054,46.2002],[-99.0049,46.2822],[-98.9154,46.2821],[-98.7878,46.2805],[-98.755,46.281],[-98.6622,46.2812],[-98.5359,46.2817],[-98.5024,46.2808],[-98.2859,46.2816],[-98.2524,46.2815],[-98.1616,46.2818],[-98.1314,46.2813],[-98.0366,46.2809],[-98.009,46.2814],[-97.9096,46.2823],[-97.8826,46.2827],[-97.5333,46.2819],[-97.4063,46.2823],[-97.2833,46.2822],[-97.2615,46.2822],[-97.2618,46.196],[-97.2603,45.9985],[-97.231,45.9951],[-97.2313,45.936],[-97.3576,45.936],[-97.3773,45.936],[-97.4826,45.9359],[-97.605,45.9356],[-97.755,45.9356],[-97.9775,45.9351],[-98.0017,45.9355],[-98.0095,45.9355]]]]},\"properties\":{\"name\":\"Barnes\",\"state\":\"ND\"}}]}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-15","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tatge, Wyatt S. 0000-0003-4414-2492","orcid":"https://orcid.org/0000-0003-4414-2492","contributorId":239544,"corporation":false,"usgs":true,"family":"Tatge","given":"Wyatt","email":"","middleInitial":"S.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wheeling, Spencer L. 0000-0003-4411-6526","orcid":"https://orcid.org/0000-0003-4411-6526","contributorId":221899,"corporation":false,"usgs":true,"family":"Wheeling","given":"Spencer","email":"","middleInitial":"L.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823301,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224581,"text":"70224581 - 2021 - Forest resistance to extended drought enhanced by prescribed fire in low elevation forests of the Sierra Nevada","interactions":[],"lastModifiedDate":"2021-09-29T13:34:40.402512","indexId":"70224581","displayToPublicDate":"2021-09-15T08:29:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Forest resistance to extended drought enhanced by prescribed fire in low elevation forests of the Sierra Nevada","docAbstract":"

Prescribed fire reduces fire hazards by removing dead and live fuels (small trees and shrubs). Reductions in forest density following prescribed fire treatments (often in concert with mechanical treatments) may also lessen competition so that residual trees might be more likely to survive when confronted with additional stressors, such as drought. The current evidence for these effects is mixed and additional study is needed. Previous work found increased tree survivorship in low elevation forests with a recent history of fire during the early years of an intense drought (2012 to 2014) in national parks in the southern Sierra Nevada. We extend these observations through additional years of intense drought and continuing elevated tree mortality through 2017 at Sequoia and Kings Canyon National Parks. Relative to unburned sites, we found that burned sites had lower stem density and had lower proportions of recently dead trees (for stems ≤47.5 cm dbh) that presumably died during the drought. Differences in recent tree mortality among burned and unburned sites held for both fir (white fir and red fir) and pine (sugar pine and ponderosa pine) species. Unlike earlier results, models of individual tree mortality probability supported an interaction between plot burn status and tree size, suggesting the effect of prescribed fire was limited to small trees. We consider differences with other recent results and discuss potential management implications including trade-offs between large tree mortality following prescribed fire and increased drought resistance. 

","language":"English","publisher":"MDPI","doi":"10.3390/f12091248","usgsCitation":"van Mantgem, P., Caprio, A., Stephenson, N.L., and Das, A., 2021, Forest resistance to extended drought enhanced by prescribed fire in low elevation forests of the Sierra Nevada: Forests, v. 12, no. 9, 1248, 11 p., https://doi.org/10.3390/f12091248.","productDescription":"1248, 11 p.","ipdsId":"IP-129596","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12091248","text":"Publisher Index Page"},{"id":436200,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W1CKTF","text":"USGS data release","linkHelpText":"Forest Structure Data for Burned and Unburned Sites at Sequoia and Kings Canyon National Parks"},{"id":389949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.30352783203125,\n 36.69485094156225\n ],\n [\n -118.41888427734374,\n 37.03325468997236\n ],\n [\n -118.69903564453124,\n 37.21939331752986\n ],\n [\n -118.828125,\n 37.23907530202184\n ],\n [\n -118.85559082031249,\n 37.19533058280065\n ],\n [\n -118.75946044921874,\n 37.07271048132943\n ],\n [\n -118.80615234374999,\n 36.95208671786997\n ],\n [\n -118.91601562499999,\n 36.91915611148194\n ],\n [\n -118.89953613281249,\n 36.84446074079564\n ],\n [\n -118.95996093749999,\n 36.78949107451841\n ],\n [\n -118.98193359375,\n 36.756490329505176\n ],\n [\n -118.970947265625,\n 36.619936625629215\n ],\n [\n -118.91601562499999,\n 36.59127365634205\n ],\n [\n -118.89404296875,\n 36.49859745028132\n ],\n [\n -118.75946044921874,\n 36.47209813242351\n ],\n [\n -118.76495361328125,\n 36.32176422120382\n ],\n [\n -118.53424072265625,\n 36.29741818650811\n ],\n [\n -118.57818603515625,\n 36.405810193726765\n ],\n [\n -118.46282958984374,\n 36.319551259461186\n ],\n [\n -118.2403564453125,\n 36.48314061639213\n ],\n [\n -118.30352783203125,\n 36.69485094156225\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"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":824161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caprio, Anthony C.","contributorId":35863,"corporation":false,"usgs":false,"family":"Caprio","given":"Anthony C.","affiliations":[],"preferred":false,"id":824162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824164,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232160,"text":"70232160 - 2021 - Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon","interactions":[],"lastModifiedDate":"2022-06-09T13:42:26.457253","indexId":"70232160","displayToPublicDate":"2021-09-15T08:25:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon","docAbstract":"

Research dating back to the 1950 s has documented negative effects from harvesting of primeval forests on stream ecosystems of the Pacific Northwest. By the early 1990 s, state and federal forest practice rules governing timber harvest were modified throughout North America to better protect aquatic habitats and biotic resources, principally salmonids. These rules inspired a generation of studies using a before-after-control-impact (BACI) design to document the capacity of contemporary timber harvest rules to protect salmonids in headwater streams of second-growth forests. One important unanswered question concerns the potential effects of successive clearcuts in second growth forests. Consequently, we used a paired watershed approach to evaluate the effects of two successive clearcut harvests in the Alsea Watershed, site of the seminal Alsea Watershed Study that was conducted from 1958 to 1973, on relative biomass, movement, survival, and distribution of coastal cutthroat trout (Oncorhynchus clarkii clarkii) and three physical habitat characteristics (pool area and depth, and water temperature). Although the total clearcut harvest encompassed 87% of the treatment catchment in six years, no negative effects of logging were detected for either age-1 + coastal cutthroat trout or habitat variables. Comparisons between the harvested and reference catchments suggested the survival of coastal cutthroat trout (>94 mm fork length) and total catchment relative biomass of age-1+ (i.e., > 80 mm) exhibited similar patterns, increasing from the pre-logging period (2006–2009) through the Phase I post-logging period (2009–2014), and decreasing to levels observed in the pre-logging period during the Phase II post-logging period (2014–2017). Additionally, there was no evidence for differences in movement of coastal cutthroat trout related to the harvesting treatment. In terms of habitat variables, there was a relative increase in annual total pool area in the harvested catchment during the Phase II post-logging period, but there was no evidence the 7-day moving mean maximum stream temperature changed after the Phase I and Phase II harvests. Moreover, stream water temperatures never exceeded the criterion designed to protect core coldwater habitat for salmonids (16 °C). As such, it is unlikely that cutthroat trout experienced thermal stress following either harvest. More generally, results from this and other recent studies suggest that forest practice rules developed in conjunction with current best management practices for logging in headwater catchments have substantially improved outcomes for stream biota relative to unregulated forest harvest, at least for short periods of time after logging (i.e., ≤ 8 years).

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119447","usgsCitation":"Bateman, D.S., Chelgren, N., Gresswell, R.E., Dunham, J.B., Hockman-Wert, D., Leer, D.W., and Bladon, K., 2021, Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon: Forest Ecology and Management, v. 496, 119447, 15 p., https://doi.org/10.1016/j.foreco.2021.119447.","productDescription":"119447, 15 p.","ipdsId":"IP-130611","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119447","text":"Publisher Index Page"},{"id":401979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Alsea River Watershed, Drift Creek, Flynn Creek, Needle Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.0143585205078,\n 44.38865427337759\n ],\n [\n -123.79737854003905,\n 44.38865427337759\n ],\n [\n -123.79737854003905,\n 44.524416083679924\n ],\n [\n -124.0143585205078,\n 44.524416083679924\n ],\n [\n -124.0143585205078,\n 44.38865427337759\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"496","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bateman, D. S.","contributorId":292361,"corporation":false,"usgs":false,"family":"Bateman","given":"D.","email":"","middleInitial":"S.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chelgren, Nathan 0000-0003-0944-9165 nchelgren@usgs.gov","orcid":"https://orcid.org/0000-0003-0944-9165","contributorId":3134,"corporation":false,"usgs":true,"family":"Chelgren","given":"Nathan","email":"nchelgren@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":844393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":844394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leer, D. W.","contributorId":292363,"corporation":false,"usgs":false,"family":"Leer","given":"D.","email":"","middleInitial":"W.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bladon, K. D.","contributorId":292364,"corporation":false,"usgs":false,"family":"Bladon","given":"K. D.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225168,"text":"70225168 - 2021 - A preliminary regional geomorphologic map in Utopia Planitia of the Tianwen-1 Zhurong Landing Region","interactions":[],"lastModifiedDate":"2021-10-15T13:02:36.681619","indexId":"70225168","displayToPublicDate":"2021-09-15T08:01:13","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":"A preliminary regional geomorphologic map in Utopia Planitia of the Tianwen-1 Zhurong Landing Region","docAbstract":"

A geomorphologic map is an important step to understanding the geologic context and history of a site; here, we present an initial geomorphologic map for an area spanning 22°–26°N, 108°–112°E in the Utopia Planitia (UP) region on Mars. This site is of special interest because it contains the May 2021 landing site of the Zhurong rover from Tianwen-1. Utopia Planitia exhibits many lobate features that have been proposed to be lava or mud flows. Lander and rover data should help solve the scientific question concerning the origin of UP flows. We use our map to generate an initial stratigraphic framework of geomorphological features in order to help place future Zhurong data into the regional geologic context. Our mapping effort has detailed the distribution of three geomorphologic units and 11 types of surface features.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL094629","usgsCitation":"Mills, M., McEwen, A.S., and Okubo, C., 2021, A preliminary regional geomorphologic map in Utopia Planitia of the Tianwen-1 Zhurong Landing Region: Geophysical Research Letters, v. 48, no. 18, e2021GL094629, 10 p., https://doi.org/10.1029/2021GL094629.","productDescription":"e2021GL094629, 10 p.","ipdsId":"IP-130143","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":489129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl094629","text":"Publisher Index Page"},{"id":390563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Mills, Mackenzie M","contributorId":267770,"corporation":false,"usgs":false,"family":"Mills","given":"Mackenzie M","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":825233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":825234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Okubo, Chris 0000-0001-9776-8128 cokubo@usgs.gov","orcid":"https://orcid.org/0000-0001-9776-8128","contributorId":174209,"corporation":false,"usgs":true,"family":"Okubo","given":"Chris","email":"cokubo@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825235,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224197,"text":"ofr20211088 - 2021 - Effect of the emergency drought barrier on the distribution, biomass, and grazing rate of the bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California","interactions":[],"lastModifiedDate":"2021-09-16T11:44:14.037287","indexId":"ofr20211088","displayToPublicDate":"2021-09-15T07:48:53","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1088","displayTitle":"Effect of the Emergency Drought Barrier on the Distribution, Biomass, and Grazing Rate of the Bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California","title":"Effect of the emergency drought barrier on the distribution, biomass, and grazing rate of the bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California","docAbstract":"

Executive Summary

Benthic samples were collected from the Sacramento–San Joaquin Delta of northern California to examine the effect of the changing hydrologic flow on the bivalves Potamocorbula and Corbicula before, during, and after the False River Barrier (hereafter, barrier) was in operation (May–November 2015). Potamocorbula moved upstream in the Sacramento River as the salinity intruded. Given the lower electrical conductivity of the San Joaquin River, Potamocorbula did not move as far upriver as it did in the Sacramento River. Potamocorbula recruits settled in the Sacramento and False Rivers, whereas Corbicula recruits were mostly found in the San Joaquin River. When the grazing rates for the two bivalves were combined, new populations of Potamocorbula plus existing Corbicula likely reduced the net growth rate of the phytoplankton in and just upstream from the Sacramento and San Joaquin River confluence region when the barrier was in place. Prior to the barrier installation, a very dry period assumably aided the success of Potamocorbula in the confluence region; nonetheless, they also responded to the increasing salinity in the Sacramento River and their population spatially expanded. Potamocorbula’s upriver incursion was stopped owing to the return of freshwater flow due to the removal of the barrier, but the adults of the species were still present at the upstream end of Decker Island in January 2016. Corbicula adults did not seem to respond to the increased salinity caused by the barrier and maintained their biomass at all locations compared to what was recorded before the barrier.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211088","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Parchaso, F., Zierdt Smith, E.L., and Thompson, J.K., 2021, Effect of the emergency drought barrier on the distribution, biomass, and grazing rate of the bivalves Corbicula fluminea and Potamocorbula amurensis, False River, California: U.S. Geological Survey Open-File Report 2021–1088, 22 p., https://doi.org/10.3133/ofr20211088.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-120260","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":389246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1088/coverthb.jpg"},{"id":389247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1088/ofr20211088.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1088"}],"country":"United States","state":"California","otherGeospatial":"False River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87957763671874,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.005902055387075\n ],\n [\n -121.44561767578124,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.232786699509965\n ],\n [\n -121.87957763671874,\n 38.005902055387075\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Water Resources, Earth System Processes Division
U.S. Geological Survey
345 Middlefield Road
Menlo Park, California, 94025

","tableOfContents":"","publishedDate":"2021-09-15","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Parchaso, Francis 0000-0002-9471-7787 parchaso@usgs.gov","orcid":"https://orcid.org/0000-0002-9471-7787","contributorId":150620,"corporation":false,"usgs":true,"family":"Parchaso","given":"Francis","email":"parchaso@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":823309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zierdt Smith, Emily L. 0000-0003-0787-1856 ezierdtsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0787-1856","contributorId":220320,"corporation":false,"usgs":true,"family":"Zierdt Smith","given":"Emily","email":"ezierdtsmith@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":823310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Janet K. 0000-0002-1528-8452 jthompso@usgs.gov","orcid":"https://orcid.org/0000-0002-1528-8452","contributorId":1009,"corporation":false,"usgs":true,"family":"Thompson","given":"Janet","email":"jthompso@usgs.gov","middleInitial":"K.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":823311,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226855,"text":"70226855 - 2021 - A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","interactions":[],"lastModifiedDate":"2023-11-08T16:32:06.862608","indexId":"70226855","displayToPublicDate":"2021-09-15T06:59:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","docAbstract":"

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112670","usgsCitation":"Li, C., Xian, G.Z., Zhou, Q., and Pengra, B., 2021, A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping: Remote Sensing of Environment, v. 266, 112670, 19 p., https://doi.org/10.1016/j.rse.2021.112670.","productDescription":"112670, 19 p.","ipdsId":"IP-123712","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450816,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112670","text":"Publisher Index Page"},{"id":393007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"266","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":828505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":828506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":828507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":264539,"corporation":false,"usgs":false,"family":"Pengra","given":"Bruce","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":828508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}