A Comprehensive Evaluation of System Response
Achieving Water Quality Goals in the Chesapeake Bay: A Comprehensive Evaluation of System Response (CESR) includes an evaluation of why progress toward meeting the TMDL and water quality standards has been slower than expected and offers options for how progress can be accelerated. This report is a summation of a three year investigation into the 40 year effort to reduce nutrient loads to Chesapeake Bay.
History
The effort began as a STAC independent initiative in March 2019, after Kurt Stephenson, Zach Easton, and Brian Benham proposed the idea of a report that would identify gaps and uncertainties in system response—physical, chemical, biological, and socioeconomic—that impact efforts designed to attain water quality standards in Chesapeake Bay. As STAC Chair at the time, Benham facilitated the development of a collaborative process that would engage the entire committee. As a first step in approaching the long causal chain that links management actions to their eventual impact on water quality and living resources, workgroups were formed around the subsystems of this chain: nutrient and sediment reductions (watershed), water quality response to nutrient and sediment reductions (estuary) and living resource response to water quality (living resources). Each of these workgroups generated an independent document with a self-determined scope (i.e., workgroups were afforded flexibility to address issues beyond the original objectives). Because the content of each document was both unique and substantial, STAC chose to publish them as stand-alone documents with authorship attribution.
In the second step, a steering committee developed a series of framing questions to guide the preparation of this report that would meet the objective of identifying gaps and uncertainties in achieving the Bay TMDL and water quality standards. Coeditors Stephenson and Wardrop, supported to great extent by a subgroup of the Steering Committee (Leonard Shabman, Zach Easton, Jeremy Testa, William Dennison, Kenny Rose, and Mark Monaco) were tasked with assembling ideas and contributions to write a single draft text, drawing material from the aforementioned resource documents, STAC and Chesapeake Bay Program reports, the scientific literature, and a limited amount of additional analyses performed in collaboration with Bay Program scientists. The resulting report was then submitted for several reviews by both steering committee members and the membership at-large to produce a consensus report.
The Colorado River is often referred to as “the lifeblood of the west.” The basin supplies municipal water to nearly 40 million people and irrigates approximately 22,000 km2 of agricultural lands. Twenty-two major rivers converge with the Colorado after it begins its descent from the Rocky Mountains and winds through the plateaus of Colorado, Utah, and Arizona, onto the deserts of southwestern Arizona, and finally into the Gulf of California, where inflows from the Río Hardy and Río Sonoyta in Mexico complete the drainage. The mainstem Colorado, Green, Yampa, Little Colorado, and Yampa Rivers are described in further detail in the 2005 edition (Blinn and Poff, 2005) of this book. In this edition, we discuss seven other major tributaries in the Colorado River basin: the Gunnison, San Juan, Virgin, Bill Williams, Verde, Black, and Salt Rivers. The water quality and quantity, flora and fauna, and sediment and organic loads of each of these tributaries uniquely alter the mainstem Colorado River and the habitat it provides. Thus, understanding the hydrology, ecology, and human use of these tributaries is critical toward understanding both the complex history and present-day management of the Colorado River Basin as a whole.
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Michael D.","contributorId":305312,"corporation":false,"usgs":false,"family":"Delong","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":872297,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Jardine, Timothy D.","contributorId":305313,"corporation":false,"usgs":false,"family":"Jardine","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":872298,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Benke, Arthur C.","contributorId":296917,"corporation":false,"usgs":false,"family":"Benke","given":"Arthur","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":872299,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Cushing, Colbert E.","contributorId":296918,"corporation":false,"usgs":false,"family":"Cushing","given":"Colbert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":872300,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Metcalfe, Anya 0000-0002-6286-4889","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":221738,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ford, Morgan 0000-0001-5104-9566","orcid":"https://orcid.org/0000-0001-5104-9566","contributorId":221740,"corporation":false,"usgs":true,"family":"Ford","given":"Morgan","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872248,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70245602,"text":"70245602 - 2023 - Prevalence of Ophidiomyces ophidiicola and epizootiology of snake fungal disease in free-ranging Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in New Jersey","interactions":[],"lastModifiedDate":"2024-08-06T10:47:41.764556","indexId":"70245602","displayToPublicDate":"2023-05-11T06:58:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Prevalence of Ophidiomyces ophidiicola and epizootiology of snake fungal disease in free-ranging Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in New Jersey","title":"Prevalence of Ophidiomyces ophidiicola and epizootiology of snake fungal disease in free-ranging Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in New Jersey","docAbstract":"Snake fungal disease, caused by Ophidiomyces ophidiicola, is recognized as a potential concern for North American snakes. We tested skin swabs from Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in the New Jersey pinelands for the presence of O. ophidiicola before emergence from hibernation. We used qPCR to test the collected swabs for the presence of O. ophidiicola, then determined pathogen prevalence as a function of sampling year, sampling location (skin lesion, healthy ventral skin, healthy head skin) sex, and age. There were no temporal trends in O. ophidiicola detection percentages on snakes, which varied from 58 to 83% in different years. Ophidiomyces ophidiicola detection on snakes was highest in swabs of skin lesions (71%) and lowest in head swabs (29%). Males had higher prevalence than females (82% versus 62%). The fungus was not detected in hatchling snakes (age 0) in the fall, but 75% of juveniles tested positive at the end of hibernation (age 1 year). We also screened hibernacula soil samples for the presence of O. ophidiicola. Where snakes hibernated, 69% of soil samples were positive for O. ophidiicola, and 85% of snakes lying on positive soil samples also tested positive for the pathogen. Although a high proportion of snakes (73%) tested positive for O. ophidiicola during our 4-year study, the snakes appeared healthy except for small skin lesions. We conclude that O. ophidiicola prevalence is high on hibernating Northern Pine Snakes and in the hibernacula soil, with a strong association between snakes and positive adjacent soil. This is the first demonstration that snakes likely become infected during hibernation.
Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.
This paper describes the results of a study that was done by the USGS to assess recent (2017) water availability, forecast long-term trends in water availability, assess changes in water availability, and forecast future water availability in the Trinity River Basin in Texas. The Trinity River Basin surface water model and Trinity River alluvium aquifer (TRAA) groundwater model were created to evaluate future conditions under different global climate models (GCM). The results of this study show minimal overall changes in water availability for both surface water and groundwater. Trend analyses using historical data (1900–2017) indicated an increase of annual precipitation on the watersheds that drain into the reservoirs in Regional Water Planning Group C. However, the Trinity River Basin surface water model GCM ensemble mean annual precipitation indicates a downward trend, resulting in a downward trend in surface runoff. Additionally, the GCM ensemble mean for the Trinity River Basin surface water model and the TRAA groundwater model both indicate a downward trend in recharge while the TRAA model GCM ensemble mean indicates an upward trend in the amount of groundwater leaving the aquifer to rivers and streams resulting in an upward trend of cumulative storage change.
","language":"English","publisher":"Texas Water Journal","doi":"10.21423/twj.v14i1.7146","usgsCitation":"Milmo, M.J., McDowell, J., Yesildirek, M.V., and Harwell, G.R., 2023, The use of historical data and global climate models to assess historical and future surface water and groundwater availability in the Trinity River Basin in Texas: Texas Water Journal, v. 14, p. 34-61, https://doi.org/10.21423/twj.v14i1.7146.","productDescription":"28 p.","startPage":"34","endPage":"61","ipdsId":"IP-126619","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":443587,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.21423/twj.v14i1.7146","text":"Publisher Index Page"},{"id":435342,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BVOEJ3","text":"USGS data release","linkHelpText":"Hydrologic simulations using projected climate data as input to the Precipitation-Runoff Modeling System (PRMS) for the Trinity River Basin Integrated Water Availability Assessment, Texas, 2023"},{"id":435341,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XO5F9G","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to assess historical and future trends in groundwater availability in the Trinity River alluvium aquifer, Texas"},{"id":416955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Trinity River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.0568823566713,\n 29.605130308347057\n ],\n [\n -94.82111695509118,\n 29.47268831477325\n ],\n [\n -94.56341988824927,\n 29.61102156169673\n ],\n [\n -94.98560444456466,\n 31.578939128932277\n ],\n [\n -95.40230608456515,\n 32.304435780613815\n ],\n [\n -95.72579814719658,\n 32.762064472265905\n ],\n [\n -95.93963188351171,\n 33.33195284298357\n ],\n [\n -96.42212851930111,\n 33.574406176811095\n ],\n [\n -97.90799884087924,\n 33.77061473287188\n ],\n [\n -98.1218325771938,\n 33.49213956579713\n ],\n [\n -97.53516053140494,\n 32.558961831908476\n ],\n [\n -96.68530850245952,\n 32.141316395990245\n ],\n [\n -96.30698727667026,\n 31.217473563861276\n ],\n [\n -95.89576855298657,\n 30.48316869632812\n ],\n [\n -95.23781859509266,\n 29.709952489208263\n ],\n [\n -95.0568823566713,\n 29.605130308347057\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","edition":"1","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Milmo, Molly J. 0000-0001-9074-0982","orcid":"https://orcid.org/0000-0001-9074-0982","contributorId":245854,"corporation":false,"usgs":true,"family":"Milmo","given":"Molly","email":"","middleInitial":"J.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDowell, Jeremy 0000-0002-8132-9806","orcid":"https://orcid.org/0000-0002-8132-9806","contributorId":221296,"corporation":false,"usgs":true,"family":"McDowell","given":"Jeremy","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872224,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yesildirek, Monica Veale 0000-0002-0320-8531","orcid":"https://orcid.org/0000-0002-0320-8531","contributorId":228880,"corporation":false,"usgs":true,"family":"Yesildirek","given":"Monica","email":"","middleInitial":"Veale","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harwell, Glenn R. 0000-0003-4265-2296","orcid":"https://orcid.org/0000-0003-4265-2296","contributorId":205197,"corporation":false,"usgs":true,"family":"Harwell","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872226,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243530,"text":"70243530 - 2023 - Spatiotemporal segregation by migratory phenotype indicates potential for assortative mating in lake sturgeon","interactions":[],"lastModifiedDate":"2023-05-11T11:51:53.294068","indexId":"70243530","displayToPublicDate":"2023-05-11T06:40:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal segregation by migratory phenotype indicates potential for assortative mating in lake sturgeon","docAbstract":"Migratory diversity can promote population differentiation if sympatric phenotypes become temporally, spatially, or behaviorally segregated during breeding. In this study, the potential for spatiotemporal segregation was tested among three migratory phenotypes of lake sturgeon (Acipenser fulvescens) that spawn in the St. Clair River of North America’s Laurentian Great Lakes but differ in how often they migrate into the river and in which direction they move after spawning. Acoustic telemetry over 9 years monitored use of two major spawning sites by lake sturgeon that moved north to overwinter in Lake Huron or south to overwinter in Lake St. Clair. Lake St. Clair migrants were further distinguished by whether they migrated into the St. Clair River each year (annual migrants) or intermittently (intermittent migrants). Social network analyses indicated lake sturgeon generally co-occurred with individuals of the same migratory phenotype more often than with different migratory phenotypes. A direct test for differences in space use revealed one site was almost exclusively visited by Lake St. Clair migrants whereas the other site was visited by Lake Huron migrants, intermittent Lake St. Clair migrants, and, to a lesser extent, annual Lake St. Clair migrants. Analysis of arrival and departure dates indicated opportunity for co-occurrence at the site visited by all phenotypes but showed Lake Huron migrants arrived approximately 2 weeks before Lake St. Clair migrants. Taken together, our results indicated partial spatiotemporal segregation of migratory phenotypes that may generate assortative mating and promote population differentiation.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-022-05280-y","usgsCitation":"Buchinger, T.J., Hondorp, D.W., and Krueger, C.C., 2023, Spatiotemporal segregation by migratory phenotype indicates potential for assortative mating in lake sturgeon: Oecologia, v. 201, p. 953-964, https://doi.org/10.1007/s00442-022-05280-y.","productDescription":"12 p.","startPage":"953","endPage":"964","ipdsId":"IP-145544","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":416953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Lake Huron, Lake St. Clair, St. Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.44106634783627,\n 43.02142044090863\n ],\n [\n -82.43493230856738,\n 43.00572237651323\n ],\n [\n -82.43493230856738,\n 42.98665504673153\n ],\n [\n -82.4272647594809,\n 42.97543620673554\n ],\n [\n -82.46406899509569,\n 42.93166317141717\n ],\n [\n -82.48707164235513,\n 42.89010612827542\n ],\n [\n -82.48400462272066,\n 42.85076962407413\n ],\n [\n -82.49473919144158,\n 42.83165427664838\n ],\n [\n -82.49320568162476,\n 42.802407600536924\n ],\n [\n -82.48400462272066,\n 42.782151798798424\n ],\n [\n -82.48247111290384,\n 42.76414107641085\n ],\n [\n -82.49320568162476,\n 42.74612511637403\n ],\n [\n -82.49780621107678,\n 42.72472436138173\n ],\n [\n -82.51160779943214,\n 42.710077484389984\n ],\n [\n -82.52080885833621,\n 42.67400890494051\n ],\n [\n -82.52387587797065,\n 42.641303662813186\n ],\n [\n -82.5392109761436,\n 42.62212362728596\n ],\n [\n -82.60208487865226,\n 42.63340671694476\n ],\n [\n -82.63735560445019,\n 42.650327516652254\n ],\n [\n -82.67415984006571,\n 42.63340671694476\n ],\n [\n -82.68182738915219,\n 42.57584157849038\n ],\n [\n -82.67415984006571,\n 42.54308473958321\n ],\n [\n -82.6511571928063,\n 42.54082501338064\n ],\n [\n -82.60975242773874,\n 42.51822325322155\n ],\n [\n -82.6005513688354,\n 42.48543617491288\n ],\n [\n -82.55454607431655,\n 42.499005327087275\n ],\n [\n -82.51774183870103,\n 42.484305279350025\n ],\n [\n -82.5085407797977,\n 42.52048379725801\n ],\n [\n -82.54227799577804,\n 42.580358413596514\n ],\n [\n -82.5085407797977,\n 42.60970986652924\n ],\n [\n -82.49780621107678,\n 42.6379193801549\n ],\n [\n -82.49933972089363,\n 42.66386084303551\n ],\n [\n -82.47020303436533,\n 42.713457839779466\n ],\n [\n -82.47020303436533,\n 42.732609709294906\n ],\n [\n -82.45333442637477,\n 42.76188936783501\n ],\n [\n -82.46713601473087,\n 42.80915806122647\n ],\n [\n -82.45486793619236,\n 42.86425924971999\n ],\n [\n -82.44720038710591,\n 42.89010612827542\n ],\n [\n -82.45026740674035,\n 42.90358715831874\n ],\n [\n -82.44720038710591,\n 42.92604899219708\n ],\n [\n -82.40886264167352,\n 42.95635948072143\n ],\n [\n -82.39812807295186,\n 42.981045882622254\n ],\n [\n -82.40426211222149,\n 42.998993406281045\n ],\n [\n -82.37819244532761,\n 43.00908658525387\n ],\n [\n -82.39659456313501,\n 43.03823534271183\n ],\n [\n -82.44106634783627,\n 43.02142044090863\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationDate":"2023-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchinger, Tyler J. 0000-0002-4590-341X","orcid":"https://orcid.org/0000-0002-4590-341X","contributorId":290501,"corporation":false,"usgs":false,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":872235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":872236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krueger, Charles C. 0000-0002-6735-5012","orcid":"https://orcid.org/0000-0002-6735-5012","contributorId":274493,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":872237,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243878,"text":"70243878 - 2023 - Assembling the right pieces: Developing an interdisciplinary team to study disease, decline, and recovery of a world-class Smallmouth Bass fishery","interactions":[],"lastModifiedDate":"2023-07-24T16:52:34.573388","indexId":"70243878","displayToPublicDate":"2023-05-10T11:17:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Assembling the right pieces: Developing an interdisciplinary team to study disease, decline, and recovery of a world-class Smallmouth Bass fishery","docAbstract":"Managing and understanding fisheries dynamics are becoming more complex as new and seemingly more complicated environmental factors are identified. Often management requires resources beyond that of any one entity and calls for collaboration among partners with differing priorities and backgrounds to account for the complexity of factors influencing fisheries. We present a collaborative case study from the Susquehanna River basin, Pennsylvania, where Smallmouth Bass Micropterus dolomieu have faced population declines, mortality events, and notable signs of disease in recent years. Collaboration was required to study many facets of the fishery and the environment simultaneously to better understand risk factors and underlying relationships influencing Smallmouth Bass health. The outcomes from this interdisciplinary collaboration allowed for identification of contributing risk factors, led to the development of products and analytical techniques that were mutually beneficial to all partners involved, and provided knowledge that was integrated into fish health and fisheries management.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10922","usgsCitation":"Schall, M., Smith, G., Blazer, V., Walsh, H.L., Wertz, T., Shull, D.R., and Wagner, T., 2023, Assembling the right pieces: Developing an interdisciplinary team to study disease, decline, and recovery of a world-class Smallmouth Bass fishery: Fisheries Magazine, v. 48, no. 2, p. 287-294, https://doi.org/10.1002/fsh.10922.","productDescription":"8 p.","startPage":"287","endPage":"294","ipdsId":"IP-142756","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443590,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.10922","text":"Publisher Index Page"},{"id":417401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New York, Pennsylvania","otherGeospatial":"Susquehanna River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.5,\n 39.528797988906234\n ],\n [\n -75.5,\n 42.26954597404389\n ],\n [\n -77.80771178110639,\n 42.26954597404389\n ],\n [\n -77.80771178110639,\n 39.528797988906234\n ],\n [\n -75.5,\n 39.528797988906234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schall, Megan K.","contributorId":264767,"corporation":false,"usgs":false,"family":"Schall","given":"Megan K.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":873593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Geoffrey","contributorId":199064,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":873594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":873592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Heather L. 0000-0001-6392-4604 hwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":4696,"corporation":false,"usgs":true,"family":"Walsh","given":"Heather","email":"hwalsh@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":873595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wertz, Timothy","contributorId":274363,"corporation":false,"usgs":false,"family":"Wertz","given":"Timothy","affiliations":[{"id":56607,"text":"Pennsylvania Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shull, Dustin R.","contributorId":147947,"corporation":false,"usgs":false,"family":"Shull","given":"Dustin","email":"","middleInitial":"R.","affiliations":[{"id":16963,"text":"PA DEP","active":true,"usgs":false}],"preferred":false,"id":873597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":873598,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243602,"text":"70243602 - 2023 - Plant migration due to winter climate change: Range expansion of tropical invasive plants in response to warming winters","interactions":[],"lastModifiedDate":"2023-07-24T16:51:21.074256","indexId":"70243602","displayToPublicDate":"2023-05-10T09:46:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Plant migration due to winter climate change: Range expansion of tropical invasive plants in response to warming winters","docAbstract":"Warming winters due to climate change can facilitate the range expansion of invasive non-native species. In the southeastern United States, the frequency and intensity of extreme winter temperatures determines the northern range limits of many tropical organisms including many species of invasive non-native plants. However, the effects of winter climate change on invasive species’ range limits have been understudied. Here, we used temperature and species occurrence data to examine the sensitivity of invasive tropical plant species to freezing temperatures. We also examined the potential for northward range expansion of these species due to winter climate change. From an initial group of 81 invasive plant species selected due to their ability to transform native plant communities, our analyses identify and quantify species-specific temperature thresholds for 40 tropical, cold sensitive species. Future winter warming scenarios indicate that these tropical invasive plant species have the potential for northward range expansion across the southeastern United States in response to small changes in the severity of winter cold temperature extremes. The potential for range expansion is greatest in coastal areas, which typically have warmer temperatures than inland counterparts. Thus, coastal regions are likely to serve as biological invasion hotspots from which invasive species expand into inland areas. The state of Florida has become a global hotspot for biological invasions, with tens of millions of dollars (US) spent annually to control the ecological and societal impacts of invasive plants on publicly held conservation lands. Collectively, our results underscore the need to better anticipate and prepare for the northward range expansion of invasive plants from Florida into the southeastern United States in response to winter climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-023-03075-7","usgsCitation":"Osland, M., Chivoiu, B., Feher, L., Dale, L., Lieurance, D., Daniel, W., and Spencer, J.E., 2023, Plant migration due to winter climate change: Range expansion of tropical invasive plants in response to warming winters: Biological Invasions, v. 25, p. 2813-2830, https://doi.org/10.1007/s10530-023-03075-7.","productDescription":"18 p.","startPage":"2813","endPage":"2830","ipdsId":"IP-145305","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":417030,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, South 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data","interactions":[],"lastModifiedDate":"2023-05-17T13:49:27.788978","indexId":"70243688","displayToPublicDate":"2023-05-10T08:48:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data","docAbstract":"Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023EA002845","usgsCitation":"Maxwell, A.E., Odom, W.E., Shobe, C.M., Doctor, D.H., Bester, M.S., and Ore, T., 2023, Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data: Earth and Space Science, v. 10, no. 5, e2023EA002845, 25 p., https://doi.org/10.1029/2023EA002845.","productDescription":"e2023EA002845, 25 p.","ipdsId":"IP-150908","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443593,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023ea002845","text":"Publisher Index Page"},{"id":417130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Maxwell, Aaron E.","contributorId":305483,"corporation":false,"usgs":false,"family":"Maxwell","given":"Aaron","email":"","middleInitial":"E.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shobe, Charles M.","contributorId":305484,"corporation":false,"usgs":false,"family":"Shobe","given":"Charles","email":"","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":872918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bester, Michelle S.","contributorId":305485,"corporation":false,"usgs":false,"family":"Bester","given":"Michelle","email":"","middleInitial":"S.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ore, Tobi","contributorId":305487,"corporation":false,"usgs":false,"family":"Ore","given":"Tobi","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872921,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243953,"text":"70243953 - 2023 - Evaluation of Copernicus DEM and comparison to the DEM used for Landsat collection-2 processing","interactions":[],"lastModifiedDate":"2023-06-12T21:50:00.086818","indexId":"70243953","displayToPublicDate":"2023-05-10T07:04:02","publicationYear":"2023","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 Copernicus DEM and comparison to the DEM used for Landsat collection-2 processing","docAbstract":"High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within-season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950–2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19-year period analysis (2000–2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass-Cast). This method can be made available in a user-friendly automated system that can be used by livestock producers and rangeland managers to inform within-season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.
We compared a bead RNA extraction method with a one-tube method that required only a heat block and ice. RNA was first extracted from liver samples from nine rabbits dying from rabbit hemorrhagic disease virus 2 (RHDV2) using magnetic beads, and RT-PCR was used to detect RHDV2 sequence. Following freezing, RNA was extracted a second time using the SwiftX™ Swabs Viral RNA Extraction Reagent. RHDV2 was detected in all nine samples. Cycle threshold values were higher in the RT-PCR following SwiftX extraction (mean: 3.79), indicating that the second extraction method resulted in approximately a 1 log10 reduction in sensitivity. A second freeze–thaw for the samples and less tissue extracted using SwiftX may have contributed additionally to the loss in sensitivity.
Sex ratio, and the extent to which it varies over time, is an important factor in the demography, management, and conservation of wildlife populations. Greater sage-grouse Centrocercus urophasianus populations in western North America are monitored using counts of males at leks in spring. Population estimates derived from lek-count data typically assume a constant, female-biased sex ratio, yet few rigorous, empirically derived estimates of sex ratio are available to test that assumption. We estimated pre-breeding sex ratio of greater sage-grouse in a peripheral, geographically isolated population in northwestern Colorado during two consecutive winters using closed-population, robust-design, multi-state, genetic mark–recapture models in program MARK. Sex ratio varied markedly between years, with estimates of 3.29 (95% CI: 2.36–4.59) females per male in winter 2012–2013 and 1.54 (95% CI: 1.22–1.95) females per male in winter 2013–2014. Rather than assuming a constant sex ratio, biologists should consider the potential for large annual variation in sex ratio of greater sage-grouse populations when estimating population size or trend from male lek-count data.
The idea of mining the Moon, once purely science-fiction, is now on the verge of becoming reality. Taking advantage of the resources on the Moon is part of the plans of many nations and some enterprising commercial entities; demonstrating in-situ (in place) resource utilization near the lunar south pole is an explicit goal of the United States’ Artemis program. Economic extraction and sustainable management of these resources require understanding the nature, quantity, and quality of each resource. This publication aims to provide a relatively simple, but technically rigorous, assessment of the status of lunar resource exploration in 2022.
Building on the experience of the U.S. Geological Survey in conducting resource assessments for Earth, we propose a general methodology for quantitative lunar resources assessments. Lunar resources can be categorized as energy, mineral, and water and classified with respect to their certainty and their recoverability. The portion of the technically recoverable resource that can be converted to a commodity within budgetary and other mission constraints can be classified as a “reserve.”
For energy resources, solar energy is known to be especially abundant along some high ridges near the lunar poles and the technology to exploit it is mature. Mineral resources, largely in the form of loose rock powder that covers the surface of the Moon, are also widely accessible in large quantities. Many different technologies to convert this material into useful commodities (such as landing pads and oxygen) are currently being developed and are likely to be available for industrial-scale application within 30 years. Water ice almost certainly exists in the polar regions of the Moon but there are fundamental unanswered questions about when and how the ice formed—leaving us without knowledge of the form, quantity, quality, and distribution of lunar ice. Until rover missions bring new ground truth data, lunar ice will remain a highly speculative resource that may be both limited and non-renewable.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1507","usgsCitation":"Keszthelyi, L.P., Coyan, J.A., Bennett, K.A., Ostrach, L.R., Gaddis, L.R., Gabriel, T.S.J., and Hagerty, J., 2023, Assessment of lunar resource exploration in 2022: U.S. Geological Survey Circular 1507, 23 p., https://doi.org/10.3133/cir1507.","productDescription":"iv, 23 p.","numberOfPages":"23","onlineOnly":"N","ipdsId":"IP-132347","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":416826,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1507/cir1507.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416825,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1507/covrthb.jpg"}],"contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The recent development of animal-borne sensors coupled with location data can provide insights into how individuals modify their behaviour with respect to specific habitat features. Animals can express a diverse array of behaviours as they navigate heterogenous landscapes, yet few studies have specifically evaluated the interaction of behaviours with habitat characteristics. We used a novel broadcast acoustic transmitter to investigate the interaction between vocal behaviours of an endemic Hawaiian thrush, the ʻōmaʻo, Myadestes obscurus, and habitat features across a naturally fragmented forest landscape. Through the development of behavioural landscape models that link specific vocalizations with space use, we found that the use of different vocalization types (calls, songs, whisper songs) were highly variable across the landscape but were associated with distinct habitat features. The likelihood of calls increased in an open lava matrix between forest patches, while whisper songs were more strongly associated with the dense interior areas of forest fragments. In contrast, the rate of ʻōmaʻo vocalizations overall decreased in the open lava matrix, suggesting that ʻōmaʻo may shift behaviours from territory defence to foraging as they transition through different habitats. Our study revealed context-specific changes in behaviour across ʻōmaʻo home ranges, including courtship, aggression and social interactions between individuals. Combining the use of a novel acoustic tool with automated radiotelemetry allowed us to overcome challenges associated with detection and analysis of variation in behaviour and resource selection across a highly heterogeneous landscape that would have been otherwise difficult to impossible.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2023.04.006","usgsCitation":"Netoskie, E.C., Paxton, K.L., Paxton, E.H., Asner, G.P., and Hart, P.J., 2023, Linking vocal behaviours to habitat structure to create behavioural landscapes: Animal Behaviour, v. 201, p. 1-11 p., https://doi.org/10.1016/j.anbehav.2023.04.006.","productDescription":"11 p.","startPage":"1","endPage":"11 p.","ipdsId":"IP-136322","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.anbehav.2023.04.006","text":"Publisher Index Page"},{"id":420907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii, Mauna Loa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.58292548105314,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.5421499770886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Netoskie, Erin C","contributorId":329790,"corporation":false,"usgs":false,"family":"Netoskie","given":"Erin","email":"","middleInitial":"C","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":883317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":883318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":883319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":883320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":883321,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246240,"text":"70246240 - 2023 - Geology along the Yuba Pass and Highway 70 corridors: A complex history of tectonics and magmatism in the northern Sierra Nevada","interactions":[],"lastModifiedDate":"2023-06-28T15:02:00.624756","indexId":"70246240","displayToPublicDate":"2023-05-09T10:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geology along the Yuba Pass and Highway 70 corridors: A complex history of tectonics and magmatism in the northern Sierra Nevada","docAbstract":"This field trip traverses a cross section of northern Sierra Nevada geology and landscape along two major corridors, Highway 49 (Yuba Pass) and Highway 70. These highways, and adjacent roadways, offer roadcuts, outcrops, and overviews through diverse pre-Cenozoic metamorphic rocks along the Laurentian margin, Mesozoic batholithic rocks, and Miocene volcanic rocks. Observing this array of rocks on a single trip provides an opportunity to examine the progression of tectonic forces in this region since the Paleozoic Era. Inspiration for this trip is a 1:100,000-scale geologic map and geophysical maps of the Portola 30′ × 60′ quadrangle that integrate decades of published and unpublished mapping with new geophysical data. The quadrangle map will seamlessly depict a geologically complex region along the boundary between the Sierra Nevada and Basin and Range provinces, dominated by transtensional tectonics of the Walker Lane. This field trip highlights many of the major units of the geologic map and will also feature new geochronological data on plutonic rocks.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field excursions to the northern Sierra Nevada of California, the mining districts of the Sierra Nevada, and Cretaceous and Paleocene sediments in Maryland, USA","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2023.0065(02)","usgsCitation":"Roberts, M., Langenheim, V., Schweickert, R.A., and Hanson, R., 2023, Geology along the Yuba Pass and Highway 70 corridors: A complex history of tectonics and magmatism in the northern Sierra Nevada, chap. of Field excursions to the northern Sierra Nevada of California, the mining districts of the Sierra Nevada, and Cretaceous and Paleocene sediments in Maryland, USA, v. 65, p. 21-35, https://doi.org/10.1130/2023.0065(02).","productDescription":"15 p.","startPage":"21","endPage":"35","ipdsId":"IP-148535","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":418588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"northern Sierra Nevada, Yuba Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121,\n 40\n ],\n [\n -121,\n 39.5\n ],\n [\n -120,\n 39.5\n ],\n [\n -120,\n 40\n ],\n [\n -121,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"65","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Michelle 0000-0003-4387-6574","orcid":"https://orcid.org/0000-0003-4387-6574","contributorId":216218,"corporation":false,"usgs":true,"family":"Roberts","given":"Michelle","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":876374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":221236,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":876375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schweickert, Richard A.","contributorId":310423,"corporation":false,"usgs":false,"family":"Schweickert","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":876376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Richard E.","contributorId":315377,"corporation":false,"usgs":false,"family":"Hanson","given":"Richard E.","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":876377,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256482,"text":"70256482 - 2023 - Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","interactions":[],"lastModifiedDate":"2024-08-07T15:11:02.429549","indexId":"70256482","displayToPublicDate":"2023-05-09T09:49:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","docAbstract":"Effective fishery management in large systems relies on understanding how individual stocks contribute to a fishery over spatial and temporal scales. The current conceptual model for management of Walleye Sander vitreus in Green Bay designates Walleye in the northern and southern parts of the bay as distinct stocks, with little mixing between the northern and southern fisheries, and assumes that Walleye in both northern and southern Green Bay primarily spawn in tributaries as opposed to shoreline or offshore reef areas. We used acoustic telemetry to test this conceptual model for Walleye management in Green Bay. Telemetry indicated that the majority of Green Bay Walleye use tributaries for spawning. However, many individuals were assigned to open-water spawning locations during consecutive years in both northern (26%) and southern (21%) Green Bay, suggesting that open-water spawners may represent a larger proportion of the Walleye stocks than previously thought. Differential movement was observed between northern and southern portions of Green Bay, with 56% of Walleye tagged in northern Green Bay crossing receiver lines to move south compared to only 19% of Walleye tagged in southern Green Bay crossing receiver lines to move north. Walleye typically transitioned across these boundaries in summer and fall, suggesting that stock contributions to the fishery in each zone may differ seasonally. Differential movements of northern Green Bay Walleye may be influenced by broad-scale differences in habitat and prey availability, which are likely related to the differential effects of dreissenid mussel invasion in Green Bay. Our results suggest that adjustment of monitoring efforts to account for open-water spawners may provide a more complete picture of stock status. Additionally, more research examining potential food web effects of northern Green Bay Walleye moving into southern Green Bay may be needed to determine how these movements might influence other important species.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10883","usgsCitation":"Izzo, L., Dembkowski, D., Hayden, T., Binder, T., Christopher Vandergoot, Hogler, S., Donofrio, M., Zorn, T., Krueger, C., and Isermann, D.A., 2023, Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 43, no. 3, p. 695-714, https://doi.org/10.1002/nafm.10883.","productDescription":"20 p.","startPage":"695","endPage":"714","ipdsId":"IP-145656","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10883","text":"Publisher Index Page"},{"id":432339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.32040853136192,\n 44.37812529340982\n ],\n [\n -87.91846002557772,\n 44.3585534932721\n ],\n [\n -86.60288078784984,\n 45.624769896015266\n ],\n [\n -86.99575779919539,\n 45.917890769552486\n ],\n [\n -87.69003998623847,\n 45.15642614432045\n ],\n [\n -88.32040853136192,\n 44.37812529340982\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":340807,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel","contributorId":340808,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Todd","contributorId":340810,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Binder, Tom","contributorId":340812,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christopher Vandergoot","contributorId":340814,"corporation":false,"usgs":false,"family":"Christopher Vandergoot","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogler, Steven","contributorId":340817,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907579,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael","contributorId":340818,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907580,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zorn, Troy","contributorId":340819,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907581,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles","contributorId":340820,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907582,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907583,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243674,"text":"70243674 - 2023 - Phenotypic trait differences between Iris pseudacorus in native and introduced ranges support greater capacity of invasive populations to withstand sea level rise","interactions":[],"lastModifiedDate":"2023-06-27T16:57:12.215808","indexId":"70243674","displayToPublicDate":"2023-05-09T08:49:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Phenotypic trait differences between Iris pseudacorus in native and introduced ranges support greater capacity of invasive populations to withstand sea level rise","title":"Phenotypic trait differences between Iris pseudacorus in native and introduced ranges support greater capacity of invasive populations to withstand sea level rise","docAbstract":"Tidal wetlands are greatly impacted by climate change, and by the invasion of alien plant species that are being exposed to salinity changes and longer inundation periods resulting from sea level rise. To explore the capacity for the invasion of Iris pseudacorus to persist with sea level rise, we initiated an intercontinental study along estuarine gradients in the invaded North American range and the native European range.
San Francisco Bay-Delta Estuary; California, USA and Guadalquivir River Estuary; Andalusia, Spain.
We compared 15 morphological, biochemical, and reproductive plant traits within populations in both ranges to determine if specific functional traits can predict invasion success and if environmental factors explain observed phenotypic differences.
Alien I. pseudacorus plants in the introduced range had more robust growth than plants in the native range. The vigour of the alien plants was reflected by expression of higher leaf water content, fewer senescent leaves per leaf fan, and more carbohydrate storage reserves in rhizomes than plants in the native range. Moreover, alien plants tended to show higher specific leaf area and seed production than native plants. I. pseudacorus plants in the introduced range were less affected by increasing salinity and were exposed to deeper inundation water along the estuarine gradient than those in the native range.
Functional trait differences suggest mature populations of I. pseudacorus in the introduced range have greater adapted capacity to adjust to environmental stresses induced by rising sea level than those in the native range. Knowledge of these trait responses can be applied to improve risk assessments in invaded estuaries and to achieve climate-adapted conservation goals for conservation of the species in its native range.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13694","usgsCitation":"Grewell, B.J., Gallego-Tevar, B., Barcenas-Moreno, G., Whitcraft, C.R., Thorne, K., Buffington, K., and Castillo, J.M., 2023, Phenotypic trait differences between Iris pseudacorus in native and introduced ranges support greater capacity of invasive populations to withstand sea level rise: Diversity and Distributions, v. 29, no. 7, p. 834-848, https://doi.org/10.1111/ddi.13694.","productDescription":"15 p.","startPage":"834","endPage":"848","ipdsId":"IP-151016","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443610,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13694","text":"Publisher Index Page"},{"id":417131,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain, United States","state":"Andalusia, California","otherGeospatial":"Guadalquivir River Estuary, San Francisco Bay-Delta Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.17511083170808,\n 38.31712828438441\n ],\n [\n -123.17511083170808,\n 37.205650879413625\n ],\n [\n -120.22951725697519,\n 37.205650879413625\n ],\n [\n -120.22951725697519,\n 38.31712828438441\n ],\n [\n -123.17511083170808,\n 38.31712828438441\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -7.015998637877459,\n 37.34832618116711\n ],\n [\n -7.015998637877459,\n 36.72067723528794\n ],\n [\n -5.810620224229581,\n 36.72067723528794\n ],\n [\n -5.810620224229581,\n 37.34832618116711\n ],\n [\n -7.015998637877459,\n 37.34832618116711\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Grewell, Brenda J.","contributorId":305471,"corporation":false,"usgs":false,"family":"Grewell","given":"Brenda","email":"","middleInitial":"J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":872885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallego-Tevar, Blanca","contributorId":305472,"corporation":false,"usgs":false,"family":"Gallego-Tevar","given":"Blanca","email":"","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barcenas-Moreno, Gael","contributorId":305474,"corporation":false,"usgs":false,"family":"Barcenas-Moreno","given":"Gael","email":"","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitcraft, Christine R.","contributorId":305476,"corporation":false,"usgs":false,"family":"Whitcraft","given":"Christine","email":"","middleInitial":"R.","affiliations":[{"id":66229,"text":"CSU Long Beach","active":true,"usgs":false}],"preferred":false,"id":872888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872889,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872890,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Castillo, Jesus M.","contributorId":305477,"corporation":false,"usgs":false,"family":"Castillo","given":"Jesus","email":"","middleInitial":"M.","affiliations":[{"id":66227,"text":"Universidad de Sevilla","active":true,"usgs":false}],"preferred":false,"id":872891,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255245,"text":"70255245 - 2023 - Diverse migratory portfolios drive inter-annual switching behavior of elk across the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2024-06-13T12:25:42.960467","indexId":"70255245","displayToPublicDate":"2023-05-09T07:15:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Diverse migratory portfolios drive inter-annual switching behavior of elk across the Greater Yellowstone Ecosystem","docAbstract":"A growing body of evidence shows that some ungulates alternate between migratory and nonmigratory behaviors over time. Yet it remains unclear whether such short-term behavioral changes can help explain reported declines in ungulate migration worldwide, as opposed to long-term demographic changes. Furthermore, advances in tracking technology reveal that a simple distinction between migration and nonmigration may not sufficiently describe all individual behaviors. To better understand the dynamics and drivers of ungulate switching behavior, we investigated 14 years of movement data from 361 elk in 20 herds across the Greater Yellowstone Ecosystem (GYE). First, we categorized yearly individual behaviors using a clustering algorithm that identified similar migratory tactics across a continuum of behaviors. Then, we tested seven hypotheses to explain why some ungulates switch behaviors, and we evaluated how behavioral changes affected the proportions of different behaviors across the system. We identified four distinct behavioral tactics: residents (4.8% of elk-years), short-distance migrants (53.7%), elevational migrants (21.9%) and long-distance migrants (19.6%). Of the 20 herds, 18 were partially migratory, and 5 had all four movement tactics present. We observed switches between migratory tactics in all sets of consecutive years during our study period, with an average of 22.5% of individual elk changing movement tactics from one year to the next. Elk in herds with higher movement tactic diversity were significantly more likely to switch tactics and often responded more effectively to adverse environmental conditions, compared to those in herds with low movement tactic diversity. During our study period, switching increased the prevalence of both short- and long-distance migrants, decreased the prevalence of elevational migrants, and had no effect on the prevalence of residents. Our findings suggest that rather than contributing to the declining migratory behavior found in the GYE, switching behavior may enable greater resiliency to continuously changing environmental and anthropogenic conditions.
Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.
Lakes provide important habitat for salmonids that may use them as a primary feeding area between periods of reproduction. The seasonal changes in vertical thermal structure in lakes can affect the distribution of salmonids on seasonal and diel time scales as they search for, consume, and digest prey that also exploits the water column's distribution of food, temperature and light. Our goal was to analyse the vertical distribution of wild, native coastal cutthroat trout (Oncorhynchus clarkii clarkii) in Lake Washington on daily and seasonal time scales. This lake is stratified in the summer and isothermal in winter, allowing us to compare vertical movements between periods with and without thermal structure in water 50 m deep. We predicted that trout would be deeper in the water column during stratified months and shallower during isothermal months, and shallower at night than in the day. Overall, the trout showed these patterns in the depths and temperatures they occupied, tending to be within or below the thermocline in the summer but not in the coolest water available, and closer to the surface when the lake was isothermal. The trout were also closer to the surface at night and deeper during the day. The vertical range of these diel movements shifted with the seasons–deepest in October, as the thermocline deepened and weakened, and shallowest in January when the lake was isothermal. These seasonal and diel vertical distribution patterns by the trout optimise metabolism for growth, and facilitate feeding on planktivorous fishes that also show seasonal and diel vertical distribution changes.
Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.
The parasitic copepod Salmincola californiensis infects Pacific salmon and trout (Oncorhynchus spp.) and often reaches high prevalence and intensity in reservoirs compared to stream systems. Recent research indicates that temperature plays a fundamental role in copepod development and fish susceptibility. Here, we expand a linked foraging and bioenergetics model to simulate infection risk. Based on juvenile salmon vertical migration patterns, we add estimates of copepod generations produced and thermal strata metrics that appear associated with copepodid aggregations and increased infection. Severe damage on hosts may be caused by the infectious copepodid, a life-stage not readily visible and thus not detectable using traditional fish screenings. We discuss model limitations, opportunities for future research, and the potential for inclusion of copepod expansion equations to existing linked bioenergetics models or observed behaviors of salmonids in other lentic systems. We demonstrate that using a temperature sensitive model framework that includes copepod infection dynamics is useful in interpreting other lines of evidence, such as fish mortality estimates. Collectively, our work provides a testable framework for future comparisons of infection potential and demonstrates how bioenergetics models may be useful in understanding host–parasite interactions.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-023-01420-2","usgsCitation":"Murphy, C.A., Pollock, A., Johnson, S.L., and Arismendi, I., 2023, Linked foraging and bioenergetics modeling may inform fish parasite infection dynamics: Environmental Biology of Fishes, v. 106, p. 1345-1356, https://doi.org/10.1007/s10641-023-01420-2.","productDescription":"12 p.","startPage":"1345","endPage":"1356","ipdsId":"IP-147898","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","noUsgsAuthors":false,"publicationDate":"2023-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Christina Amy 0000-0002-3467-6610","orcid":"https://orcid.org/0000-0002-3467-6610","contributorId":335232,"corporation":false,"usgs":true,"family":"Murphy","given":"Christina","email":"","middleInitial":"Amy","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Amanda","contributorId":150244,"corporation":false,"usgs":false,"family":"Pollock","given":"Amanda","email":"","affiliations":[{"id":17945,"text":"U.S. Fish and Wildlife Service, Pacific Islands Refuge and Monuments","active":true,"usgs":false}],"preferred":false,"id":910079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Sherri L 0000-0002-4223-3465","orcid":"https://orcid.org/0000-0002-4223-3465","contributorId":192210,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","email":"","middleInitial":"L","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":910080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arismendi, Ivan 0000-0002-8774-9350","orcid":"https://orcid.org/0000-0002-8774-9350","contributorId":202207,"corporation":false,"usgs":false,"family":"Arismendi","given":"Ivan","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":910081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243176,"text":"sir20235028 - 2023 - Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","interactions":[],"lastModifiedDate":"2026-03-06T20:53:37.591262","indexId":"sir20235028","displayToPublicDate":"2023-05-08T11:03:58","publicationYear":"2023","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":"2023-5028","displayTitle":"Development of an Integrated Hydrologic Flow Model of the Rio San Jose Basin and Surrounding Areas, New Mexico","title":"Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","docAbstract":"The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
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Discussed in this chapter are seven significant tributaries of the Lower Mississippi River and its major distributary. As a group, these eight rivers and their basins encompass substantial variation in physical form, hydrology, biota, ecology, and human impacts. The Current River, Ouachita River, and Saline River, flow to the Mississippi out of the U.S. Interior Highlands. The Cache River basin, centered in Arkansas, contains a vast expanse of bottomland hardwood forest and is famous for wintering waterfowl. The Hatchie River, flowing west out of Tennessee, is the longest free-flowing tributary of the Lower Mississippi River and famous for its rich diversity of mussels and fishes. The Wolf River of Tennessee supports a magnificent bald cypress-tupelo swamp and is notable as a protected urban river that flows through Memphis. The Big Sunflower River begins and ends in the alluvial floodplain of the Mississippi River and flows through a basin of intense agriculture and historical human conflict. The Atchafalaya River, the primary distributary of the Mississippi River, supports the nation's largest expanse of bottomland hardwood forest and swamp wetlands. In this chapter, we review the physiography, geomorphology, hydrology, water chemistry, land use, biological diversity, ecological processes, human impacts, and areas of need for research and management of each of these eight rivers and their basins.
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