Dedicated long-term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs - stratified random, weighted effort, indicator unit, rotating panel, and split panel - to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially-varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of U.S. National Parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10 to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales, and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred, and offer guidance as to how managers can balance common tradeoffs in large-scale and long-term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad-scaled environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2621","usgsCitation":"Wright, A., Campbell Grant, E.H., and Zipkin, E., 2022, A comparison of monitoring designs to assess wildlife community parameters across spatial scales: Ecological Applications, v. 32, no. 6, e2621, 13 p., https://doi.org/10.1002/eap.2621.","productDescription":"e2621, 13 p.","ipdsId":"IP-131418","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":399081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Alexander","contributorId":238924,"corporation":false,"usgs":false,"family":"Wright","given":"Alexander","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":840983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":840984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zipkin, Elise F.","contributorId":70528,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise F.","affiliations":[],"preferred":false,"id":840985,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228937,"text":"ofr20221001 - 2022 - Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download Analysis","interactions":[],"lastModifiedDate":"2022-04-07T16:36:11.779624","indexId":"ofr20221001","displayToPublicDate":"2022-04-07T08:28:05","publicationYear":"2022","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":"2022-1001","displayTitle":"Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products—Download Analysis","title":"Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download Analysis","docAbstract":"The global food-security-support-analysis data at 30-meter resolution (GFSAD30) cropland-extent product is a project to provide high-resolution global cropland-extent data relating to water use. It is the first global-land-cover map focusing exclusively on agriculture with a 30-meter spatial resolution. The overarching goal of the GFSAD30 project is to produce consistent and unbiased estimates of global agricultural cropland products such as cropland extent; cropland types; irrigated versus rainfed cropland; cropping intensities; and spatial and temporal (from 2000 to 2017) changes in cropland extent.
The goal of this report is to assess and discuss the usage of the GFSAD30 project’s cropland-extent product. Since the public release of GFSAD30 in November 2017, the number of files downloaded has been tracked, as well as the total size of files downloaded, the country from which the GFSAD30 data were downloaded, and the user’s field of study. This report presents a monthly assessment of the usage of GFSAD30 from November 2017 through December 2019. During this period, about 1,900 gigabytes of data and about 225,000 files were downloaded by users in more than 100 countries. This report also includes how GFSAD30 has been cited in media, scientific journals, and other data products. The release of data was widely covered by the national and international press, and GFSAD30 products have been cited more than 200 times in scientific journals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221001","usgsCitation":"Oliphant, A., Thenkabail, P., and Teluguntla, P., 2022, Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download analysis: U.S. Geological Survey Open-File Report 2022–1001, 20 p., https://doi.org/10.3133/ofr20221001.","productDescription":"Report: vi, 20 p.; Data Release","ipdsId":"IP-119165","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":398273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1001/covrthb.jpg"},{"id":398276,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HOIB7S","text":"Download rates of the global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products","description":"Oliphant, A.J., Thenkabail, P.S., and Teluguntla, P., 2022, Download rates of the global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products: U.S. Geological Survey data release, https://doi.org/10.5066/P9HOIB7S."},{"id":398274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1001/ofr20221001.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Western Geographic Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, monitors stations designed for the Ambient Water-Quality Monitoring Network, a collection of stations that monitor streams and springs in Missouri. During water year 2020 (October 1, 2019, through September 30, 2020), the U.S. Geological Survey collected water-quality data at 72 stations: 70 Ambient Water-Quality Monitoring stations and 2 U.S. Geological Survey National Water Quality Network stations. Among the stations in this report, four stations have data from additional sampling completed in cooperation with the U.S. Army Corps of Engineers. Water-quality analyses are provided for dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds. Monitoring stations have been classified based on the physiographic province or primary land use in the watershed or based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) monitored. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are provided for representative streamgages in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1153","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Buckley, C.E., 2022, Quality of surface water in Missouri, water year 2020: U.S. Geological Survey Data Report 1153, 24 p., https://doi.org/10.3133/dr1153.","productDescription":"Report: vii, 24 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129885","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":398244,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":501204,"rank":6,"type":{"id":36,"text":"NGMDB Index 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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Estimates of demographic rates for animal populations and individuals have many applications for ecological and conservation research. In many animals, survival is size-dependent, but estimating the form of the size–survival relationship presents challenges. For elusive species with low recapture rates, individuals’ size will be unknown at many points in time. Integrating growth and capture–mark–recapture models in a Bayesian framework empowers researchers to impute missing size data, with uncertainty, and include size as a covariate of survival, capture probability, and presence on-site. If there is no theoretical expectation for the shape of the size–survival relationship, spline functions can allow for fitting flexible, data-driven estimates. We use long-term capture–mark–recapture data from the endangered San Francisco gartersnake (Thamnophis sirtalis tetrataenia) to fit an integrated growth–survival model. Growth models showed that females reach longer asymptotic lengths than males and that the magnitude of sexual size dimorphism differed among populations. The capture probability and availability of San Francisco gartersnakes for capture increased with snout–vent length. The survival rate of female snakes exhibits a nonlinear relationship with snout–vent length (SVL), with survival flat between 300 mm and 550 mm SVL before decreasing for females between 550 mm and 700 mm SVL. For male snakes, survival decreased for adult males >550 mm SVL. The survival rates of the smallest and largest San Francisco gartersnakes were highly uncertain because recapture rates were very low for these sizes. By integrating growth and survival models and using penalized splines, we found support for size-dependent survival in San Francisco gartersnakes. Our results have applications for devising management activities for this endangered subspecies, and our methods could be applied broadly to the study of size-dependent demography among animals.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8799","usgsCitation":"Rose, J.P., Kim, R., Schoenig, E.J., Lien, P.C., and Halstead, B., 2022, Integrating growth and survival models for flexible estimation of size-dependent survival in a cryptic, endangered snake: Ecology and Evolution, v. 12, no. 4, e8799, 15 p., https://doi.org/10.1002/ece3.8799.","productDescription":"e8799, 15 p.","ipdsId":"IP-132803","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448199,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8799","text":"Publisher Index Page"},{"id":435891,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SC36I8","text":"USGS data release","linkHelpText":"Growth and Capture-Mark-Recapture Data for San Francisco Gartersnakes, Thamnophis sirtalis tetrataenia, in San Mateo County, California from 2007 to 2020"},{"id":435890,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9700BBK","text":"USGS data release","linkHelpText":"Code to analyze Capture-Mark-Recapture data of San Francisco gartersnakes (Thamnophis sirtalis tetrataenia)"},{"id":403070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"San Mateo County, Santa Cruz County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51129150390625,\n 37.59573590243413\n ],\n [\n -122.3712158203125,\n 37.59573590243413\n ],\n [\n -122.3712158203125,\n 37.6968609874419\n ],\n [\n -122.51129150390625,\n 37.6968609874419\n ],\n [\n -122.51129150390625,\n 37.59573590243413\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4151611328125,\n 37.106669860700045\n ],\n [\n -122.16659545898438,\n 37.106669860700045\n ],\n [\n -122.16659545898438,\n 37.38979975341983\n ],\n [\n -122.4151611328125,\n 37.38979975341983\n ],\n [\n -122.4151611328125,\n 37.106669860700045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Richard 0000-0001-5891-0582 rkim@usgs.gov","orcid":"https://orcid.org/0000-0001-5891-0582","contributorId":204478,"corporation":false,"usgs":true,"family":"Kim","given":"Richard","email":"rkim@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":845709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schoenig, Elliot James 0000-0002-7217-315X eschoenig@usgs.gov","orcid":"https://orcid.org/0000-0002-7217-315X","contributorId":291497,"corporation":false,"usgs":true,"family":"Schoenig","given":"Elliot","email":"eschoenig@usgs.gov","middleInitial":"James","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lien, Patrick C. 0000-0001-7183-0878","orcid":"https://orcid.org/0000-0001-7183-0878","contributorId":291498,"corporation":false,"usgs":true,"family":"Lien","given":"Patrick","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":845712,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230253,"text":"70230253 - 2022 - Importance of local weather and environmental gradients on demography of a broadly distributed temperate frog","interactions":[],"lastModifiedDate":"2022-04-06T14:30:50.755385","indexId":"70230253","displayToPublicDate":"2022-04-06T09:20:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Importance of local weather and environmental gradients on demography of a broadly distributed temperate frog","docAbstract":"Amphibian populations are sensitive to environmental temperatures and moisture, which vary with local weather conditions and may reach new norms and extremes as contemporary climate change progresses. Using long-term (11–16 years) mark-recapture data from 10 populations of the Columbia spotted frog (Rana luteiventris) from across its U.S. range, we addressed hypotheses about how demographic relationships to weather depend upon a population’s position along climate gradients. We estimated the effect of seasonal weather on annual survival probability and recruitment rates both within populations and across the species’ range from subalpine forests to semi-arid deserts. We calculated population-specific weather variables that captured seasonal temperature and precipitation between summer sampling events, both for periods when frogs were active (spring to fall) and inactive (winter). Across all populations, we marked 15,885 adult frogs, with 33% of frogs recaptured at least once. Population demography varied with seasonal weather across the species’ range. Annual adult survival probability and recruitment rates of each population were influenced by a unique set of seasonal temperature and precipitation variables, particularly in winter and spring. Hence, adult survival varied with local conditions but, when analyzed across all populations, was predictable along a species-environment response curve associated with the timing of snowmelt and spring moisture. In contrast, recruitment rates for each population peaked at different values along an environmental gradient associated with the amount of snow during winter, and fall temperature and moisture levels, suggesting that recruitment may be responding to local conditions independently within each population. These findings highlight that sampling across the environmental (i.e., elevational and meteorological) gradients within a species range is necessary to predict species-level responses to regional climate change. This study also provides evidence of the importance of winter conditions on the demography of temperate amphibians, conditions that are already responding to climate change. Finally, this study further emphasizes that local context and spatiotemporal scale of inquiry remain paramount to understanding and potentially managing for climate effects on populations of amphibian species with broad geographic ranges.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108648","usgsCitation":"Pilliod, D., McCaffery, R.M., Arkle, R., Scherer, R.D., Cupples, J.B., Eby, L.A., Hossack, B., Lingo, H., Lohr, K.N., Maxell, B.A., McGuire, M.J., Mellison, C., Meyer, M.K., Munger, J.C., Slatauski, T., and Van Horne, R., 2022, Importance of local weather and environmental gradients on demography of a broadly distributed temperate frog: Ecological Indicators, v. 136, 108648, 12 p., https://doi.org/10.1016/j.ecolind.2022.108648.","productDescription":"108648, 12 p.","ipdsId":"IP-064164","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":448202,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108648","text":"Publisher Index 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The field of volcanic gas geochemistry therefore plays a key role in understanding volcanism. In this article, we summarize the most important findings of the past few decades and how these shape today’s understanding of volcanic degassing. We argue that the recent advent of automated, continuous geochemical monitoring at volcanoes now allows us to track activity from unrest to eruption, thus providing valuable insights into the behavior of volatiles throughout the entire sequence. In the next 10 years, the research community stands to benefit from the expansion of geochemical monitoring networks to many more active volcanoes. This, along with technical advances in instrumentation, and in particular the increasing role that unoccupied aircraft systems (UAS) and satellite-based observations are likely to play in collecting volcanic gas measurements, will provide a rich dataset for testing hypotheses and developing diagnostic tools for eruption forecasts. The use of consistent, well-documented analytical methods and ensuring free, public access to the collected data with few restrictions will be most beneficial to the advancement of volcanic gas science.","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01556-6","usgsCitation":"Kern, C., Aiuppa, A., and de Moor, J.M., 2022, A golden era for volcanic gas geochemistry?: Bulletin of Volcanology, v. 84, 43, 11 p., https://doi.org/10.1007/s00445-022-01556-6.","productDescription":"43, 11 p.","ipdsId":"IP-137886","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467187,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10447/576295","text":"External Repository"},{"id":398212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","noUsgsAuthors":false,"publicationDate":"2022-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":839750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aiuppa, Alessandro","contributorId":173677,"corporation":false,"usgs":false,"family":"Aiuppa","given":"Alessandro","affiliations":[{"id":27272,"text":"Dipartimento DiSTeM, Università di Palermo, Palermo, Italy","active":true,"usgs":false}],"preferred":false,"id":839751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Moor, J. 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Within high-priority, multi-jurisdictional landscapes like the Madrean sky islands of the United States and México, efforts to identify and manage climate refugia are hindered by the lack of high-quality and consistent transboundary datasets. To fill these data gaps, we assembled a bi-national field dataset (n = 1416) for five pine species (Pinus spp.) and used generalized boosted regression to model pine habitats in relation to topographic variability as a basis for identifying potential microrefugia at local scales in the context of current species’ distribution patterns. We developed additional models to quantify climatic refugial attributes using coarse scale bioclimatic variables and finer scale seasonal remote sensing indices. Terrain metrics including ruggedness, slope position, and aspect defined microrefugia for pines within elevation ranges preferred by each species. Response to bioclimatic variables indicated that small shifts in climate were important to some species (e.g., P. chihuahuana, P. strobiformis), but others exhibited a broader tolerance (e.g., P. arizonica). Response to seasonal climate was particularly important in modeling microrefugia for species with open canopy structure and where regular fires occur (e.g., P. engelmannii and P. chihuahuana). Hotspots of microrefugia differed among species and were either limited to northern islands or occurred across central or southern latitudes. Mapping and validation of refugia and their ecological functions are necessary steps in developing regional conservation strategies that cross jurisdictional boundaries. A salient application will be incorporation of climate refugia in management of fire to restore and maintain pine ecology.
","language":"English","doi":"10.1007/s11258-022-01233-w","usgsCitation":"Haire, S.L., Villarreal, M.L., Cortes Montano, C., Flesch, A.D., Iniguez, J.M., Romo-Leon, J.R., and Sanderlin, J.S., 2022, Climate refugia for Pinus spp. in topographic and bioclimatic environments of the Madrean sky islands of México and the United States: Plant Ecology, v. 223, p. 577-598, https://doi.org/10.1007/s11258-022-01233-w.","productDescription":"22 p.","startPage":"577","endPage":"598","ipdsId":"IP-121574","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448208,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11258-022-01233-w","text":"Publisher Index Page"},{"id":435893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CLBAF7","text":"USGS data release","linkHelpText":"Pine species distribution maps of the Madrean Sky Islands, United States and Mexico"},{"id":398205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"México, United States","state":"Arizona, Chihuahua, New Mexico, Sonora","otherGeospatial":"Madrean Archipelago Ecoregion, Madrean Sky Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.19238281249999,\n 28.013801376380712\n ],\n [\n -107.677001953125,\n 28.013801376380712\n ],\n [\n -107.677001953125,\n 33.169743600216165\n ],\n [\n -112.19238281249999,\n 33.169743600216165\n ],\n [\n -112.19238281249999,\n 28.013801376380712\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"223","noUsgsAuthors":false,"publicationDate":"2022-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Haire, Sandra L. 0000-0002-5356-7567","orcid":"https://orcid.org/0000-0002-5356-7567","contributorId":213971,"corporation":false,"usgs":false,"family":"Haire","given":"Sandra","email":"","middleInitial":"L.","affiliations":[{"id":32362,"text":"Haire Laboratory for Landscape Ecology","active":true,"usgs":false}],"preferred":false,"id":839753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cortes Montano, Citlali 0000-0002-1916-1985","orcid":"https://orcid.org/0000-0002-1916-1985","contributorId":213973,"corporation":false,"usgs":false,"family":"Cortes Montano","given":"Citlali","email":"","affiliations":[{"id":38945,"text":"Universidad Juárez del Estado de Durango","active":true,"usgs":false}],"preferred":false,"id":839755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flesch, Aaron D. 0000-0003-3434-0778","orcid":"https://orcid.org/0000-0003-3434-0778","contributorId":245372,"corporation":false,"usgs":false,"family":"Flesch","given":"Aaron","email":"","middleInitial":"D.","affiliations":[{"id":49169,"text":"School of Natural Resources and the Environment and The Desert Laboratory on Tumamoc Hill, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":839756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iniguez, Jose M. 0000-0002-4566-1297","orcid":"https://orcid.org/0000-0002-4566-1297","contributorId":213972,"corporation":false,"usgs":false,"family":"Iniguez","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":839757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Romo-Leon, Jose Raul 0000-0001-8946-9005","orcid":"https://orcid.org/0000-0001-8946-9005","contributorId":289774,"corporation":false,"usgs":false,"family":"Romo-Leon","given":"Jose","email":"","middleInitial":"Raul","affiliations":[{"id":40545,"text":"Universidad de Sonora","active":true,"usgs":false}],"preferred":false,"id":839758,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sanderlin, Jamie S. 0000-0001-8651-9804","orcid":"https://orcid.org/0000-0001-8651-9804","contributorId":245373,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":49171,"text":"US Forest Service, Rocky Mountain Research Station, Flagstaff, Arizona","active":true,"usgs":false}],"preferred":false,"id":839759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230928,"text":"70230928 - 2022 - Reassessing perennial cover as a driver of duck nest survival in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-07-07T16:51:56.644528","indexId":"70230928","displayToPublicDate":"2022-04-06T08:23:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Reassessing perennial cover as a driver of duck nest survival in the Prairie Pothole Region","docAbstract":"Conservation plans designed to sustain North American duck populations prominently feature a key hypothesis stating that the amount of the landscape in perennial cover surrounding upland duck nests positively influences nest survival rates. Recent conflicting research testing this hypothesis creates ambiguity regarding which management actions to pursue and where to prioritize conservation delivery. We compared existing models and new formulations of existing models explaining spatiotemporal variation in nest survival using independent data documenting the fate of >20,000 duck nests within the Drift Prairie, Missouri Coteau, and Prairie Coteau physiographic regions of the United States Prairie Pothole Region during 2002–2018. Our results suggest an inconsistent relationship between perennial cover and survival of upland duck nests, which depended upon physiographic region and current and time-lagged landscape and environmental conditions. The magnitude and direction of how perennial cover correlated with daily nest survival depended on its dominance as a landcover type. A positive relationship existed when perennial cover was a minor component of landcover in all physiographic regions (<30% of a 10.4-km2 area) and, in the Drift Prairie and Prairie Coteau, when perennial cover was the dominant landcover type (>60%). A constant or negative relationship was predicted at locations of about 30–60% perennial cover. Additionally, environmental conditions (i.e., density of wetlands and estimated gross primary productivity in the previous year) moderated or enhanced the effect of perennial cover on nest survival, depending on physiographic region. Our finding of inconsistency in the relationship between perennial cover and nest survival contradicts the conservation premise that nest survival universally increases linearly when uplands are converted to perennial cover. Promoting policies and management actions designed to increase perennial cover can be expected to be situationally but not consistently associated with higher survival of upland duck nests.
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2022 - Phenological variation in spring migration timing of adult alewife (Alosa pseudoharengus) in coastal Massachusetts","interactions":[],"lastModifiedDate":"2022-04-05T15:38:17.139919","indexId":"70230202","displayToPublicDate":"2022-04-05T10:28:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Phenological variation in spring migration timing of adult alewife (Alosa pseudoharengus) in coastal Massachusetts","title":"Phenological variation in spring migration timing of adult alewife (Alosa pseudoharengus) in coastal Massachusetts","docAbstract":"The timing of biological events in plants and animals, such as migration and reproduction, is shifting due to climate change. Anadromous fishes are particularly susceptible to these shifts as they are subject to strong seasonal cycles when transitioning between marine and freshwater habitats to spawn. We used linear models to determine the extent of phenological shifts in adult Alewife Alosa pseudoharengus as they migrated from ocean to freshwater environments during spring to spawn at 12 sites along the northeastern USA. We also evaluated broadscale oceanic and atmospheric drivers that trigger their movements from offshore to inland habitats, including sea surface temperature, North Atlantic Oscillation index, and Gulf Stream index. Run timing metrics of initiation, median (an indicator of peak run timing), end, and duration were found to vary among sites. Although most sites showed negligible shifts towards earlier timing, statistically significant changes were detected in three systems. Overall, winter sea surface temperature, spring and fall transition dates, and annual run size were the strongest predictors of run initiation and median dates, while a combination of within-season and seasonal-lag effects influenced run end and duration timing. Disparate results observed across the 12 spawning runs suggest that regional environmental processes were not consistent drivers of phenology and local environmental and ecological conditions may be more important. Additional years of data to extend time series and monitoring of Alewife timing and movements in nearshore habitats may provide important information about staging behaviors just before adults transition between ocean and freshwater habitats.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10198","usgsCitation":"Dalton, R.M., Sheppard, J.J., Finn, J., Jordaan, A., and Staudinger, M., 2022, Phenological variation in spring migration timing of adult alewife (Alosa pseudoharengus) in coastal Massachusetts: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 14, no. 2, e10198, 17 p., https://doi.org/10.1002/mcf2.10198.","productDescription":"e10198, 17 p.","ipdsId":"IP-112539","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":448215,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10198","text":"Publisher Index Page"},{"id":398120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Dalton, Rebecca M.","contributorId":289643,"corporation":false,"usgs":false,"family":"Dalton","given":"Rebecca","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":839541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheppard, John J.","contributorId":200171,"corporation":false,"usgs":false,"family":"Sheppard","given":"John","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":839542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, John T.","contributorId":270782,"corporation":false,"usgs":false,"family":"Finn","given":"John T.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":839543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jordaan, Adrian","contributorId":240665,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":839544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staudinger, Michelle 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":206655,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":839545,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230200,"text":"ofr20221035 - 2022 - California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3 — Benthic habitat characterization offshore Morro Bay, California","interactions":[],"lastModifiedDate":"2022-08-23T19:18:44.059405","indexId":"ofr20221035","displayToPublicDate":"2022-04-05T09:14:35","publicationYear":"2022","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":"2022-1035","displayTitle":"California Deepwater Investigations and Groundtruthing (Cal DIG) I, Volume 3—Benthic Habitat Characterization Offshore Morro Bay, California","title":"California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3 — Benthic habitat characterization offshore Morro Bay, California","docAbstract":"Coastal and Marine Ecological Classification Standard (CMECS) geoform, substrate, and biotic component geographic information system (GIS) products were developed for the U.S. Exclusive Economic Zone (U.S. EEZ) of south-central California in the region of Santa Lucia Bank motivated by interest in development of offshore wind-energy capacity and infrastructure. The Bureau of Ocean Energy Management (BOEM), in coordination with the State of California and many other members of the California Task Force, issued calls for information in 2018 for the study area offshore of Morro Bay, California. The study area is in depths of 500 to 1,200 meters (m) and adjacent to a decommissioned nuclear power plant with a developed electric grid connection, and in an area of high wind resource. BOEM is the lead agency responsible for planning and leasing in the U.S. EEZ and funded this project to assess baseline conditions of, and the potential effects on, the seafloor environment. This project, carried out by the U.S. Geological Survey (USGS), resulted in three reports: one on biological analysis of seafloor video data, one on analysis of the geologic framework and hazards, and this report on seafloor habitat. The study area consists of 8,424 square kilometers (km2) of multibeam echo sounder (MBES) data acquired during five surveys from 2016 to 2019. Remotely operated vehicle (ROV) video was acquired in 2019 to supervise the classification of the MBES data into habitats. Derivatives of the MBES data were classified into 16 unique biotopes, 6 substrate types, 28 modifier groups, and 22 geoforms. The study area substrate is predominantly soft sediment (mud and fine sand) covering 7,804 km2 (92.7 percent) of the area. Mixed substrate areas on rocky banks, channel scarps, and the shelf break comprise 404 km2 (4.8 percent) of the study area. Hard substrate areas are found predominantly on the tops and flanks of banks and on bank ridges that separate canyons incising the banks. Hard substrates comprise 211 km2 of the study area (2.5 percent). After the bathymetry and backscatter raster images (rasters) were classified, manual editing was also done to remove noise artifacts. This effort was not completely successful and there are numerous erroneous small areas in the rasters that have been passed on to the CMECS polygon product. Nearly 120,000 annotations of organisms and their habitat were made from 25 video transects selected from 185 hours of ROV video. In total, 2,714 km2 of seafloor were successfully assigned to biotopes. Some biotopes were assigned to separate areas spatially distant from the transects that define the biotope. Expected relations between physical habitat and biota such as the number of species and the substrate induration and rugosity were verified. Slope is typically a predictive variable and was used in the classification of habitat, but the ground truth used for biotic component analysis included very little steeply sloping area. Ground-truth ROV operations were reduced by the sea state; additional ground truth could improve the biotic results and increase confidence in the spatial distribution of classifications reported here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221035","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management, National Oceanic and Atmospheric Administration, and Monterey Bay Aquarium Research Institute","programNote":"Bureau of Ocean Energy Management OCS Study BOEM 2021–045","usgsCitation":"Cochrane, G.R., Kuhnz, L.A. Gilbane, L., Dartnell, P., Walton, M.A.L., and Paull, C.K., 2022, California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3—Benthic habitat characterization offshore Morro Bay, California: U.S. Geological Survey Open-File Report 2022–1035 [also released as Bureau of Ocean Energy Management OCS Study BOEM 2021–045], 18 p., https://doi.org/10.3133/ofr20221035.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-129519","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":398057,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QQZ27U","text":"Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project","description":"Cochrane, G.R., Kuhnz, L.A., Gilbane, L., Dartnell, P., and Walton, M.A., 2022, Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project: U.S. Geological Survey data release, https://doi.org/10.5066/P9QQZ27U."},{"id":398055,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1035/coverthb.jpg"},{"id":398056,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1035/ofr20221035.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","city":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.48431396484375,\n 34.863397850419524\n ],\n [\n -120.9210205078125,\n 35.574682600980914\n ],\n [\n -121.31103515625,\n 35.94243575255426\n ],\n [\n -121.45660400390624,\n 36.04465753921525\n ],\n [\n -122.36846923828125,\n 35.628279555648845\n ],\n [\n -122.09930419921876,\n 34.66032236481892\n ],\n [\n -120.48431396484375,\n 34.863397850419524\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
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Santa Cruz, CA 95060
Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS, is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS. We applied results of the simulation analysis to evaluate NˆS for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that NS is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2602","usgsCitation":"White, S.L., Sard, N.M., Brundage III, H., Johnson, R.L., Lubinski, B.A., Eackles, M.S., Park, I.A., Fox, D.A., and Kazyak, D., 2022, Evaluating sources of bias in pedigree-based estimates of breeding population size: Ecological Applications, v. 32, no. 5, e2602, 13 p., https://doi.org/10.1002/eap.2602.","productDescription":"e2602, 13 p.","ipdsId":"IP-114638","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2602","text":"Publisher Index Page"},{"id":398310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.794677734375,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 39.614152077002664\n ],\n [\n -74.542236328125,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 41.40153558289846\n ],\n [\n -75.794677734375,\n 39.614152077002664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sard, Nicholas M","contributorId":289872,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas","email":"","middleInitial":"M","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":839958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brundage III, Harold M","contributorId":289873,"corporation":false,"usgs":false,"family":"Brundage III","given":"Harold M","affiliations":[{"id":62274,"text":"Environmental Research and Consulting Inc","active":true,"usgs":false}],"preferred":false,"id":839959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839961,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839962,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian A","contributorId":289876,"corporation":false,"usgs":false,"family":"Park","given":"Ian","email":"","middleInitial":"A","affiliations":[{"id":62277,"text":"DNREC","active":true,"usgs":false}],"preferred":false,"id":839963,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":839964,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":839965,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230434,"text":"70230434 - 2022 - Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach","interactions":[],"lastModifiedDate":"2022-07-07T16:48:08.451905","indexId":"70230434","displayToPublicDate":"2022-04-05T07:07:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach","docAbstract":"1. Large-scale, long-term biodiversity monitoring is essential to conservation, land management, and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including `unknown', morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic ratess, occurrence, and species richness.
2. Here we present a joint classification-occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain `morphospecies' designations, and makes use of individual specimens with known species identities in a semi-supervised setting. We compare the performance of our model to a classification-only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed.
3. The model supported imperfect classifier accuracy and favored certain true species classifications strongly for some morphospecies. The model outperformed (e.g., precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species.
4. Spatial and temporal dynamics from modeled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data, and non-species classifications.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13858","usgsCitation":"Spiers, A., Royle, A., Torrens, C., and Joseph, M., 2022, Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach: Methods in Ecology and Evolution, v. 13, no. 7, p. 1528-1539, https://doi.org/10.1111/2041-210X.13858.","productDescription":"12 p.","startPage":"1528","endPage":"1539","ipdsId":"IP-127556","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":448235,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13858","text":"Publisher Index Page"},{"id":398632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398615,"type":{"id":15,"text":"Index Page"},"url":"https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13858"}],"volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Spiers, Anna","contributorId":290178,"corporation":false,"usgs":false,"family":"Spiers","given":"Anna","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":840413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":840414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torrens, Christa","contributorId":290179,"corporation":false,"usgs":false,"family":"Torrens","given":"Christa","email":"","affiliations":[{"id":62371,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":840415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joseph, Maxwell","contributorId":290181,"corporation":false,"usgs":false,"family":"Joseph","given":"Maxwell","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":840416,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230418,"text":"70230418 - 2022 - Microbially induced anaerobic oxidation of magnetite to maghemite in a hydrocarbon-contaminated aquifer","interactions":[],"lastModifiedDate":"2022-04-12T11:37:08.934453","indexId":"70230418","displayToPublicDate":"2022-04-05T06:33:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Microbially induced anaerobic oxidation of magnetite to maghemite in a hydrocarbon-contaminated aquifer","docAbstract":"Iron mineral transformations occurring in hydrocarbon-contaminated sites are linked to the biodegradation of the hydrocarbons. At a hydrocarbon-contaminated site near Bemidji, Minnesota, USA, measurements of magnetic susceptibility (MS) are useful for monitoring the natural attenuation of hydrocarbons related to iron cycling. However, a transient MS, previously observed at the site, remains poorly understood and the iron mineral phases acting as reactants and products associated with this MS perturbation remain largely unknown. To address these unknowns, we acquired mineral magnetism measurements, including hysteresis loops, backfield curves, and isothermal remanent magnetizations on sediment core samples retrieved from the site and magnetite-filled mineral packets installed within the aquifer. Our data show that the core samples and magnetite packs display decreasing magnetization with time and that this loss in magnetization is accompanied by increasing bulk coercivity consistent with decreased average grain size and/or partial oxidation. Low-temperature magnetometry on all samples displayed behavior consistent with magnetite, but samples within the plume also show evidence of maghemitization. This interpretation is supported by the occurrence of shrinkage cracks on the surface of the grains imaged via scanning electron microscopy. Magnetite transformation to maghemite typically occurs under oxic conditions, here, we propose that maghemitization occurs within the anoxic portions of the plume via microbially mediated anaerobic oxidation. Mineral dissolution also occurs within the plume. Microorganisms capable of such anaerobic oxidation have been identified within other areas at the Bemidji site, but additional microbiological studies are needed to link specific anaerobic iron oxidizers with this loss of magnetization.
The Aransas-Wood Buffalo population (the only non-reintroduced, migratory population) of endangered whooping cranes (Grus americana) overwinters along the Texas Gulf Coast, USA. Understanding whooping crane space use on the wintering grounds reveals essential aspects of this species' ecology, which subsequently assists with conservation. Using global positioning system telemetry data from marked whooping cranes during 2009–2017, we fit continuous-time stochastic process models to describe movement and home range using autocorrelated kernel density estimation (AKDE) and explored variation in home range size in relation to age, sex, reproductive status, and drought conditions. We used the Bhattacharyya coefficient of overlap and distance between home range centroids to quantify site fidelity. We examined the effects of time between winter home ranges and the sex of the crane on site fidelity using Bayesian mixed-effects beta regression. Winter whooping crane 95% AKDE home range size averaged 30.1 ± 45.2 (SD) km2 (median = 14.3, range = 1.1–308.6). Home ranges of sub-adult females were approximately 2 times larger than those of sub-adult males or families. As drought worsened, home ranges typically expanded. Between consecutive years, the home ranges of an adult crane exhibited 68 ± 31% overlap (site fidelity), but fidelity to winter sites declined in subsequent winters. The overlap of adult home ranges with the nearest unrelated family averaged 33 ± 28%. As a whooping crane aged, overlap with its winter home range as a juvenile declined, regardless of sex. By 4 years of age, a whooping crane had approximately 14 ± 28% overlap with its juvenile winter home range. Limited evidence suggested male whooping cranes return to within 2 km of their juvenile home range by their fifth winter. Previous data obtained from aerial surveys led ecologists to assume that whooping crane families normally used small areas (~2 km2) and expressed persistent site fidelity. Our analyses showed <8% of families had home ranges ≤2 km2, with the average area 15 times greater, and waning site fidelity over time. Our work represents an analysis of whooping crane home ranges for this population, identifying past misconceptions of winter space use and resulting in better estimates of space requirements for future conservation efforts.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22226","usgsCitation":"Butler, M.J., Stewart, D., Harris, G.M., Bidwell, M., and Pearse, A.T., 2022, Space use and site fidelity of wintering whooping cranes on the Texas Gulf Coast: Journal of Wildlife Management, v. 86, no. 5, e22226, 17 p., https://doi.org/10.1002/jwmg.22226.","productDescription":"e22226, 17 p.","ipdsId":"IP-132895","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":399808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Aransas National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.327880859375,\n 27.858503954841247\n ],\n [\n -96.3006591796875,\n 27.858503954841247\n ],\n [\n -96.3006591796875,\n 28.420391085674304\n ],\n [\n -97.327880859375,\n 28.420391085674304\n ],\n [\n -97.327880859375,\n 27.858503954841247\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Butler, Matthew J","contributorId":239688,"corporation":false,"usgs":false,"family":"Butler","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":841649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":841650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Grant M","contributorId":290710,"corporation":false,"usgs":false,"family":"Harris","given":"Grant","email":"","middleInitial":"M","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":841651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":139204,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark T.","affiliations":[{"id":12696,"text":"Environmental Canada","active":true,"usgs":false}],"preferred":false,"id":841652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":841653,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239266,"text":"70239266 - 2022 - Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study","interactions":[],"lastModifiedDate":"2023-01-06T14:28:10.804018","indexId":"70239266","displayToPublicDate":"2022-04-03T08:16:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study","docAbstract":"Spatial capture–recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture–recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km2. Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture–recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2618","usgsCitation":"Schmidt, G.M., Graves, T., Pederson, J.C., and Carroll, S.L., 2022, Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study: Ecological Applications, v. 32, no. 5, e2618, 19 p., https://doi.org/10.1002/eap.2618.","productDescription":"e2618, 19 p.","ipdsId":"IP-126268","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":448272,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2618","text":"Publisher Index Page"},{"id":411485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.1113551477145,\n 40.75776766966564\n ],\n [\n -112.1113551477145,\n 37.522762898285166\n ],\n [\n -109.09220756712764,\n 37.522762898285166\n ],\n [\n -109.09220756712764,\n 40.75776766966564\n ],\n [\n -112.1113551477145,\n 40.75776766966564\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Greta M","contributorId":300615,"corporation":false,"usgs":false,"family":"Schmidt","given":"Greta","email":"","middleInitial":"M","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":860956,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":860957,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Jordan C","contributorId":300616,"corporation":false,"usgs":false,"family":"Pederson","given":"Jordan","email":"","middleInitial":"C","affiliations":[{"id":65213,"text":"Utah Department of Natural Resources, retired","active":true,"usgs":false}],"preferred":false,"id":860958,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Sarah L","contributorId":300618,"corporation":false,"usgs":false,"family":"Carroll","given":"Sarah","email":"","middleInitial":"L","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":860959,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230357,"text":"70230357 - 2022 - Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","interactions":[],"lastModifiedDate":"2022-04-08T12:04:03.799162","indexId":"70230357","displayToPublicDate":"2022-04-03T07:00:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","docAbstract":"For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow-water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel subsites in the Florida Bay (application site) under a different time period (application period). We used foraging little blue herons (Egretta caerulea) as the ecological unit with which to constrain the model's “water depth window,” that is, range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability, which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by subsite and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow water has implications for monitoring habitat conditions for tidally influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.
The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.
Thermal response of the surface to solar insolation is a function of the topography and the thermal physical characteristics of the landscape, which include bulk density, heat capacity, thermal conductivity and surface albedo and emissivity. Thermal imaging is routinely used to constrain thermal physical properties by characterizing or modeling changes in the diurnal temperature profiles. Images need to be acquired throughout the diurnal cycle – typically this is done twice during a diurnal cycle, but we suggest multiple times. Comparison of images acquired over 24 hours requires that either the data be calibrated to surface temperature, or the response of the thermal camera is linear and stable over the image acquisition period. Depending on the type and age of the thermal instrument, imagery may be self-calibrated in radiance, corrected for atmospheric effects, and pixels converted to surface temperature. We used an experimental instrumentation where the calibration should be stable, but calibration coefficients are unknown. Cases may occur where one wishes to validate the camera's calibration. We present a method to validate and calibrate the instrument and characterize the thermal physical properties for areas of interest. Finally, in situ high-temporal-resolution oblique thermal imaging can be invaluable in preparation for conducting overflight missions. We present the following:
The use of oblique thermal high temporal resolution thermal imaging over diurnal or multiday periods for the characterization of landscapes has not been widespread but poses great potential.
A method of collecting and analyzing thermal data that can be used to either determine or validate thermal camera calibration coefficients.
An approach to characterize thermophysical properties of the landscape using oblique temporally high-resolution thermal imaging, combined with in situ ground measurements.
The Yosemite toad (Anaxyrus [Bufo] canorus) is a federally threatened species of meadow-specializing amphibian endemic to the high-elevation Sierra Nevada Mountains of California. The species is one of the first amphibians to undergo a large demographic collapse that was well-documented, and is reputed to remain in low abundance throughout its range. Recent phylogeographic work has demonstrated that Pleistocene toad lineages diverged and then admixed to differing extents across an elevational gradient. Although lineage divisions may have significant effects on evolutionary trajectories over large spatial and temporal scales, present-day population dynamics must be delineated in order to manage and conserve the species effectively. In this study, we used a double-digest RADseq dataset to address three primary questions: (1) Are single meadows or neighborhoods of nearby meadows most correlated with population boundaries? (2) Does asymmetrical migration occur among neighborhoods of nearby meadows? (3) What topographic or hydrological variables predict such asymmetrical migration in these meadow neighborhoods? Hierarchical STRUCTURE and AMOVA analyses suggested that populations are typically circumscribed by a single meadow, although 84% of meadows exist in neighborhoods of at least two meadows connected by low levels of migration, and over half (53%) of neighborhoods examined display strong asymmetrical migration. Meadow neighborhoods often contain one or more large and flat “hub” meadows that experience net immigration, surrounded by smaller and topographically rugged “satellite” meadows with net emigration. Hubs tend to contain more genetic diversity and could be prioritized for conservation and habitat management and as potential sources for reestablishment efforts.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2022.851676","usgsCitation":"Maier, P., Vandergast, A.G., Ostoja, S.M., Aguilar, A., and Bohonak, A.J., 2022, Gene pool boundaries for the Yosemite toad (Anaxyrus canorus) reveal asymmetrical migration within meadow neighborhoods: Frontiers in Conservation Science, v. 3, 851676, 14 p., https://doi.org/10.3389/fcosc.2022.851676.","productDescription":"851676, 14 p.","ipdsId":"IP-112825","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448280,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2022.851676","text":"Publisher Index Page"},{"id":403072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.531494140625,\n 34.903952965590065\n ],\n [\n -117.98217773437499,\n 35.02099970111467\n ],\n [\n -117.6416015625,\n 35.42486791930558\n ],\n [\n -117.87231445312499,\n 36.474306755095235\n ],\n [\n -118.19091796875,\n 36.74768773190056\n ],\n [\n -118.24584960937499,\n 37.34395908944491\n ],\n [\n -118.927001953125,\n 37.71859032558816\n ],\n [\n -119.25659179687499,\n 38.46219172306828\n ],\n [\n -119.99267578124999,\n 39.01064750994083\n ],\n [\n -119.99267578124999,\n 39.985538414809746\n ],\n [\n -120.52001953124999,\n 40.421860362045194\n ],\n [\n -120.750732421875,\n 40.91351257612758\n ],\n [\n -121.36596679687499,\n 40.95501133048621\n ],\n [\n -121.78344726562499,\n 40.863679665481676\n ],\n [\n -122.06909179687501,\n 40.30466538259176\n ],\n [\n -121.26708984374999,\n 39.257778150283364\n ],\n [\n -120.73974609374999,\n 38.7283759182398\n ],\n [\n -120.11352539062499,\n 37.56199695314352\n ],\n [\n -119.21264648437499,\n 36.54494944148322\n ],\n [\n -118.80615234374999,\n 35.23664622093195\n ],\n [\n -118.531494140625,\n 34.903952965590065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2022-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Maier, Paul A. 0000-0003-0851-8827","orcid":"https://orcid.org/0000-0003-0851-8827","contributorId":221033,"corporation":false,"usgs":false,"family":"Maier","given":"Paul A.","affiliations":[{"id":40313,"text":"Department of Biology, San Diego State","active":true,"usgs":false}],"preferred":false,"id":845776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M. sostoja@usgs.gov","contributorId":3039,"corporation":false,"usgs":true,"family":"Ostoja","given":"Steven","email":"sostoja@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":33665,"text":"USDA California Climate Hub, UC Davis","active":true,"usgs":false}],"preferred":false,"id":845778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aguilar, Andres","contributorId":195155,"corporation":false,"usgs":false,"family":"Aguilar","given":"Andres","email":"","affiliations":[],"preferred":false,"id":845779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohonak, Andrew J.","contributorId":195156,"corporation":false,"usgs":false,"family":"Bohonak","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":845780,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236571,"text":"70236571 - 2022 - On the potential for remote observations of coastal morphodynamics from surf-cameras","interactions":[],"lastModifiedDate":"2022-09-12T14:27:37.087379","indexId":"70236571","displayToPublicDate":"2022-04-01T09:14:37","publicationYear":"2022","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":"On the potential for remote observations of coastal morphodynamics from surf-cameras","docAbstract":"Recreational surf-cameras (surfcams) are ubiquitous along many coastlines, and yet are a largely untapped source of coastal morphodynamic observations. Surfcams offer broad spatial coverage and flexibility in data collection, but a method to remotely acquire ground control points (GCPs) and initial camera parameter approximations is necessary to better leverage this existing infrastructure to make quantitative measurements. This study examines the efficacy of remotely monitoring coastal morphodynamics from surfcams at two sites on the Atlantic coast of Florida, U.S.A., by leveraging freely available airborne lidar observations to acquire remote-GCPs and open-source web tools for camera parameter approximations, ignoring lens distortion. Intrinsic and extrinsic camera parameters are determined using a modified space resection procedure, wherein parameters are determined using iterative adjustment while fitting to remote-GCPs and initial camera parameter approximations derived from justified assumptions and Google Earth. This procedure is completed using the open-source Surf-Camera Remote Calibration Tool (SurfRCaT). The results indicate root mean squared horizontal reprojection errors at the two cameras of 3.43 m and 6.48 m. Only immobile hard structures such as piers, jetties, and boulders are suitable as remote-GCPs, and the spatial distribution of available points is a likely reason for the higher accuracy at one camera relative to the other. Additionally, lens distortion is not considered in this work. This is another important source of error and including it in the methodology is highlighted as a useful avenue for future work. Additional factors, such as initial camera parameter approximation accuracy, likely play a role as well. This work illustrates that, provided there is sufficient remote-GCP availability and small lens distortion, remote video monitoring of coastal areas with existing surfcams could provide a usable source of coastal morphodynamic observations. This is further explored with a shoreline change analysis from the higher-accuracy camera. It was found that only the largest (>6 m) magnitude shoreline changes exceed the observational uncertainty driven by shoreline mapping error and reprojection error, indicating that remotely calibrated surfcams can provide observations of seasonal or storm-driven signals.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14071706","usgsCitation":"Conlin, M.P., Adams, P., and Palmsten, M.L., 2022, On the potential for remote observations of coastal morphodynamics from surf-cameras: Remote Sensing, v. 14, no. 7, 1706, 18 p., https://doi.org/10.3390/rs14071706.","productDescription":"1706, 18 p.","ipdsId":"IP-124730","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14071706","text":"Publisher Index Page"},{"id":406533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Jupiter Island, St. Lucie Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.07522583007812,\n 26.943495898597618\n ],\n [\n -80.06492614746094,\n 26.943495898597618\n ],\n [\n -80.12535095214844,\n 27.108033801463115\n ],\n [\n -80.1397705078125,\n 27.118424003999095\n ],\n [\n -80.14389038085938,\n 27.109867436716698\n ],\n [\n -80.1123046875,\n 27.034052154839163\n ],\n [\n -80.08895874023438,\n 26.97164956771795\n ],\n [\n -80.08209228515625,\n 26.944108009688595\n ],\n [\n -80.07522583007812,\n 26.943495898597618\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Conlin, Matthew P.","contributorId":239947,"corporation":false,"usgs":false,"family":"Conlin","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":851411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Peter N.","contributorId":264783,"corporation":false,"usgs":false,"family":"Adams","given":"Peter N.","affiliations":[{"id":34924,"text":"U. Florida","active":true,"usgs":false}],"preferred":false,"id":851412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":851413,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234160,"text":"70234160 - 2022 - Updates to and applications of the USGS National Crustal Model for seismic hazard studies","interactions":[],"lastModifiedDate":"2022-08-02T13:59:32.828858","indexId":"70234160","displayToPublicDate":"2022-04-01T08:58:59","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Updates to and applications of the USGS National Crustal Model for seismic hazard studies","docAbstract":"The U.S. Geological Survey (USGS) National Crustal Model (NCM) is being developed to assist in the modeling of seismic hazards across the conterminous United States. The NCM is composed of a grid of geophysical profiles, extending from the Earth’s surface into the upper mantle. It is constructed from a 3D geologic framework and geophysical rules defined by: (1) a petrologic and mineral physics database; (2) a 3D temperature model; and (3) a calibrated rock type- and age-dependent porosity model. Parameters needed to estimate site response for existing ground motion models (GMMs), including the time-averaged velocity in the upper 30 meters (VS30) and the depths to 1.0 and 2.5 km/s shear-wave velocity (Z1.0 and Z2.5), can be extracted from the NCM. As GMMs develop, other metrics could also be extracted or derived from the NCM such as sediment thickness and travel times, fundamental frequency, a fully frequency-dependent site response function, or 3D geophysical volumes for wavefield simulations. Application of the NCM may also benefit other aspects of seismic hazard analysis including better accounting for path-dependent attenuation and geometric spreading, more accurate estimation of earthquake source properties such as hypocentral location and stress drop, and calculation of crustal strength profiles that inform estimates of the base of seismicity.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"OFR22-02, Geologic Mapping Forum 21/22 abstracts","largerWorkSubtype":{"id":3,"text":"Organization Series"},"conferenceTitle":"Geologic Mapping Forum","conferenceDate":"September 2021-April 2022","conferenceLocation":"Virtual","language":"English","publisher":"Minnesota Geological Survey","usgsCitation":"Boyd, O.S., 2022, Updates to and applications of the USGS National Crustal Model for seismic hazard studies, in OFR22-02, Geologic Mapping Forum 21/22 abstracts, Virtual, September 2021-April 2022, p. 51-52.","productDescription":"2 p.","startPage":"51","endPage":"52","ipdsId":"IP-139374","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":404655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404638,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/11299/228213"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Thorleifson, L. Harvey","contributorId":103430,"corporation":false,"usgs":true,"family":"Thorleifson","given":"L.","email":"","middleInitial":"Harvey","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":848067,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848049,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230431,"text":"70230431 - 2022 - Post-Early Miocene silicic volcanism in the northern Mojave Desert, California","interactions":[],"lastModifiedDate":"2022-04-13T13:21:26.717806","indexId":"70230431","displayToPublicDate":"2022-04-01T08:14:16","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Post-Early Miocene silicic volcanism in the northern Mojave Desert, California","docAbstract":"Silicic volcanism that postdates widespread early Miocene volcanism in the Mojave Desert is underappreciated. We compiled age, petrographic, and geochemical data for volcanic rocks in a wide swath of the desert south of the Garlock fault using an age threshold of post-18.8 Ma, approximately the limit of the earlier Miocene volcanism as marked by the eruption of the widespread Peach Spring Tuff. In addition to the well-known young basaltic volcanic centers not considered in this paper, several dozen silicic volcanic edifices are known or likely to be younger than 18.8 Ma. Several examples of rhyolite tuffs and basalt lava in middle Miocene basin occur in sequences of the Barstow Formation and its correlatives. Dacite domes are common in the Calico Mountains, dated at ~17 Ma, and similar, but mostly undated, domes are scattered nearby in the Barstow area and east of the Calico Mountains. North of Barstow, chains of rhyolite domes and scattered dacite domes are known. A few of these domes and flows are dated in the range of 13-7 Ma. Farther north, the Lava Mountains have several volcanic sequences from 12 to 7 Ma and ranging in composition from basalt to rhyolite. Farther east and west are more rhyolite and dacite domes, in general undated, as well as the extensive ~17.8 Ma Woods Mountains rhyolite center. Sparse geochemical data for the silicic rocks indicate distinct rhyolite and dacite groups, and rare andesite. Understanding of these potentially young silicic volcanic rocks is hampered by poor age control and geochemical data, but more study holds promise for better understanding the origins of volcanism in the Mojave Desert.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Symposium Inc","usgsCitation":"Miller, D., Gans, P.B., Felger, T.J., and Vazquez, J.A., 2022, Post-Early Miocene silicic volcanism in the northern Mojave Desert, California, in Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings, p. 124-141.","productDescription":"18 p.","startPage":"124","endPage":"141","ipdsId":"IP-137943","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":398642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398614,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org"}],"country":"United States","state":"California, Nevada","otherGeospatial":"northern Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 34\n ],\n [\n -114,\n 34\n ],\n [\n -114,\n 37\n ],\n [\n -119,\n 37\n ],\n [\n -119,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, David M. 0000-0003-3711-0441","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":238721,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gans, Phillip B 0000-0003-0373-9639","orcid":"https://orcid.org/0000-0003-0373-9639","contributorId":204410,"corporation":false,"usgs":false,"family":"Gans","given":"Phillip","email":"","middleInitial":"B","affiliations":[{"id":36937,"text":"Dept of Earth Science, Univ of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":840410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":290175,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":840411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":840412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230395,"text":"70230395 - 2022 - In situ recording of Mars soundscape","interactions":[],"lastModifiedDate":"2022-06-01T15:15:58.077816","indexId":"70230395","displayToPublicDate":"2022-04-01T06:33:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"In situ recording of Mars soundscape","docAbstract":"Prior to the Perseverance rover landing, the acoustic environment of Mars was unknown. Models predicted that: (i) atmospheric turbulence changes at centimeter scales or smaller at the point where molecular viscosity converts kinetic energy into heat1, (ii) the speed of sound varies at the surface with frequency2,3, and (iii) high frequency waves are strongly attenuated with distance in CO22–4. However, theoretical models were uncertain because of a lack of experimental data at low pressure, and the difficulty to characterize turbulence or attenuation in a closed environment. Here using Perseverance microphone recordings, we present the first characterization of Mars’ acoustic environment and pressure fluctuations in the audible range and beyond, from 20 Hz to 50 kHz. We find that atmospheric sounds extend measurements of pressure variations down to 1,000 times smaller scales than ever observed before, revealing a dissipative regime extending over 5 orders of magnitude in energy. Using point sources of sound (Ingenuity rotorcraft, laser-induced sparks), we highlight two distinct values for the speed of sound that are ~10 m/s apart below and above 240 Hz, a unique characteristic of low-pressure CO2-dominated atmosphere. We also provide the acoustic attenuation with distance above 2 kHz, allowing us to elucidate the large contribution of the CO2 vibrational relaxation in the audible range. These results establish a ground truth for modelling of acoustic processes, which is critical for studies in atmospheres like Mars and Venus ones.
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