The U.S. Geological Survey (USGS) assesses potential for undiscovered, technically recoverable oil and gas resources in priority geologic provinces and quantifies resource volume estimates within subdivisions called assessment units (AUs). AU boundaries are defined by USGS geologists using quantitative and qualitative geologic information. Variables contained in IHS Markit’s well and production databases can quantify and/or function as proxies for many of the qualitative, boundary-defining variables. This research explores a new approach to determine AU boundaries and the potential to automate their definition, using data analytics and machine learning algorithms on key, qualitative variables within the IHS Markit databases. Well and production data from the U.S. onshore Gulf Coast region for the Upper Cretaceous Eagle Ford Group and Austin Chalk are used in this analysis because each is relatively geologically uniform in Texas and both have recently been assessed by the USGS. The Eagle Ford is an example of an in situ continuous oil and gas accumulation, and the overlying Austin Chalk is an example of a combined conventional and continuous resource, sourced from the underlying Eagle Ford. Wellspecific values were extracted or calculated from data in IHS Markit’s well and production databases for depth to top and base of the formations, formation thickness, bottom-hole temperature, temperature gradient, temperature at base of formation, cumulative oil and gas production values, barrels of oil equivalent, oil and gas gravities, mud weights from initial well test, depth pressure ratio, and excess pressure. A raster for each variable was interpolated using the natural neighbor technique from the spatial analyst toolbox in ArcGIS. Rasters were then transformed using minimum-maximum scaling, which rescales the distribution to the range of 0–1. Clustering was completed using the iso cluster unsupervised classification tool on the normalized rasters. Raster cell groupings from two to ten were explored, with initial results demonstrating that four to six classes return the most differentiable groups, with depth to formation, oil gravity, pressure, and temperature variables containing the greatest between-group differences. Modeled clusters have spatial similarities to the geologically defined AUs, with indication that temperature and pressure are the most fundamental to AU definition. Input from geologists will remain crucial for further dividing clusters and defining final AUs, since AUs are defined by both qualitative and quantitative information; however, this research documents promising cluster modeling results for the automation of initial AU definitions.
","conferenceTitle":"SEG-AAPG International Meeting for Applied Geoscience & Energy (IMAGE) 2021","conferenceDate":"September 26-October 1, 2021","conferenceLocation":"Denver, CO","language":"English","publisher":"Society of Exploration Geophysicists and the American Association of Petroleum Geologists","usgsCitation":"Shorten, C., Kinney, S.A., and Whidden, K.J., 2021, Exploring basin-scale relations and unsupervised classification to quantify and automate the definition of assessment units in USGS continuous oil and gas resource assessments, SEG-AAPG International Meeting for Applied Geoscience & Energy (IMAGE) 2021, Denver, CO, September 26-October 1, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-131669","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":490854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490853,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://archives.datapages.com/data/international-meeting-for-applied-geoscience-and-energy/data/2021/7321.htm?q=%2BauthorStrip%3Ashorten","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana, Mississippi, Texas","otherGeospatial":"Upper Cretaceous Eagle Ford Group and Austin Chalk","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.46300363364126,\n 29.853524579639256\n ],\n [\n -98.79174292446497,\n 26.39154767945162\n ],\n [\n -97.15568801261291,\n 25.692988402953162\n ],\n [\n -96.30476798511874,\n 27.907758438495677\n ],\n [\n -93.26076686296953,\n 29.3771372231364\n ],\n [\n -88.088627576136,\n 28.580800903730534\n ],\n [\n -88.5714936485433,\n 32.46814742644388\n ],\n [\n -94.5985927100297,\n 32.52158667154865\n ],\n [\n -101.46300363364126,\n 29.853524579639256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shorten, Chilisa Marie 0000-0003-1828-2002","orcid":"https://orcid.org/0000-0003-1828-2002","contributorId":267256,"corporation":false,"usgs":true,"family":"Shorten","given":"Chilisa Marie","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824843,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinney, Scott A. 0000-0001-5008-5813 skinney@usgs.gov","orcid":"https://orcid.org/0000-0001-5008-5813","contributorId":1395,"corporation":false,"usgs":true,"family":"Kinney","given":"Scott","email":"skinney@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824844,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whidden, Katherine J. 0000-0002-7841-2553 kwhidden@usgs.gov","orcid":"https://orcid.org/0000-0002-7841-2553","contributorId":3960,"corporation":false,"usgs":true,"family":"Whidden","given":"Katherine","email":"kwhidden@usgs.gov","middleInitial":"J.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824845,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229714,"text":"70229714 - 2021 - Effects of sample gear on estuarine nekton assemblage assessments and food web model simulations","interactions":[],"lastModifiedDate":"2022-03-17T13:23:05.511118","indexId":"70229714","displayToPublicDate":"2021-12-01T10:32:51","publicationYear":"2021","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":"Effects of sample gear on estuarine nekton assemblage assessments and food web model simulations","docAbstract":"Long-term fisheries-independent sampling data inform population status and trends of species-specific biomass and are often used to drive biomass-based food web models such as the Comprehensive Aquatic Systems Model (CASM). Indicators such as total biomass and mean trophic level derived from these data and from CASM outputs inform management and facilitate assessments of on-going and predicted coastal change and restoration activities on fisheries, but rely on consistent sampling to enable comparisons across space and time. Changes in coastal estuarine gradients, combined with the availability of new sampling technologies, highlight a need to assess the potential consequences of changing sampling technologies on fisheries data and the cascading impact on model outputs. In Louisiana, USA, CASM models are used to inform coastal restoration projects, relying on 40 years of fisheries-independent data derived from 50′ seine sampling. However, alternative use of electrofishers as a sampling method has been proposed to replace the seine sampling. In this study, we examine data from concurrent seine and electrofisher sampling in Barataria Basin, Louisiana, and compare biomass, assemblage data and CASM outputs related to species biomass, food web structure and energy cycling. In a paired comparison of data in 2018–2019, the electrofisher captured higher total catch and diversity compared to the seine. The electrofisher samples were dominated by shrimp (grass, white, brown) and larger bodied fish, while seine samples were dominated by smaller-bodied fish (i.e., anchovy, menhaden). Ecosystem indicators derived from running the CASM using biomass data from seine and electrofisher sampling separately in two different simulation exercises provide contrasting results. In Simulation Exercise 1, the use of different datasets (long-term CASM calibration, 2018–2019 seine, 2018–2019 electrofisher) to initialize the CASM biomasses did not result in large or long-running changes in the simulated biomasses over time. In contrast, in Simulation Exercise 2, CASM model outputs using adjusted gear ratios indicated changes in biomass structure when using electrofisher data, with a doubling of total food web biomass due to the increased shrimp count, and a 13% increase in total energy flow through the food web. Conversions based on area and gear efficiency for overall catch may be useful in maintaining the continuity of historical data. However, differences in species-specific catch due to gear selectivity could have large consequences for constructing and calibrating fish and ecosystem models and remain difficult to reconcile. These differences in assemblages, and estimated biomasses for key food web species, suggest careful consideration in changing gears.
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M.","contributorId":220867,"corporation":false,"usgs":false,"family":"Taylor","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":838076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Watkins, Katherine S.","contributorId":288553,"corporation":false,"usgs":false,"family":"Watkins","given":"Katherine","email":"","middleInitial":"S.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":838077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kiskaddon, E.","contributorId":276292,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"E.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":838079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baustian, M.","contributorId":288555,"corporation":false,"usgs":false,"family":"Baustian","given":"M.","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":838080,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242771,"text":"70242771 - 2021 - Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","interactions":[],"lastModifiedDate":"2024-03-05T16:44:23.727968","indexId":"70242771","displayToPublicDate":"2021-12-01T10:23:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7577,"text":"Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","docAbstract":"The Annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains (NGP) working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model’s parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating of the model is called “learning.”
The original ABAM model has little information about the effects of the herbicide indaziflam on target invasive annual grasses and other components of the vegetation in conditions like those that frequently occur in ABAM parks (i.e., ungrazed). The purpose of this study is to provide some of that information and therefore accelerate the rate of learning accomplished in the adaptive management cycle.
","language":"English","publisher":"National Park Service","usgsCitation":"Symstad, A., Richardson, T., and Swanson, D., 2021, Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model: Annual Report, 5 p.","productDescription":"5 p.","ipdsId":"IP-152073","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":415838,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/RPRS/IAR/Profile/573320","linkFileType":{"id":5,"text":"html"}},{"id":426325,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Little Bighorn Battlefield National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.42780937203189,\n 45.55613907753829\n ],\n [\n -107.41424157476726,\n 45.56297486244455\n ],\n [\n -107.42644541357662,\n 45.57478473659421\n ],\n [\n -107.44274112775177,\n 45.56674424099478\n ],\n [\n -107.44575619381031,\n 45.566543213856875\n ],\n [\n -107.44259755317754,\n 45.56327642203598\n ],\n [\n -107.43943891254428,\n 45.56151730159996\n ],\n [\n -107.4385056778117,\n 45.560763375980855\n ],\n [\n -107.44123359472226,\n 45.55814968884059\n ],\n [\n -107.43814674137612,\n 45.55679253410301\n ],\n [\n -107.43584954818878,\n 45.55789836636245\n ],\n [\n -107.43412665329791,\n 45.56016022820128\n ],\n [\n -107.43218839654568,\n 45.55855180246806\n ],\n [\n -107.43419844058504,\n 45.55593801245129\n ],\n [\n -107.43075265080329,\n 45.555586146818285\n ],\n [\n -107.42780937203189,\n 45.55613907753829\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Timm","contributorId":334581,"corporation":false,"usgs":false,"family":"Richardson","given":"Timm","email":"","affiliations":[],"preferred":false,"id":895969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, Dan","contributorId":334582,"corporation":false,"usgs":false,"family":"Swanson","given":"Dan","email":"","affiliations":[],"preferred":false,"id":895970,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230205,"text":"70230205 - 2021 - Genomics reveals identity, phenology and population demographics of larval ciscoes (Coregonus artedi, C. hoyi, and C. kiyi) in the Apostle Islands, Lake Superior","interactions":[],"lastModifiedDate":"2022-04-05T15:27:13.158382","indexId":"70230205","displayToPublicDate":"2021-12-01T10:14:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Genomics reveals identity, phenology and population demographics of larval ciscoes (Coregonus artedi, C. hoyi, and C. kiyi) in the Apostle Islands, Lake Superior","title":"Genomics reveals identity, phenology and population demographics of larval ciscoes (Coregonus artedi, C. hoyi, and C. kiyi) in the Apostle Islands, Lake Superior","docAbstract":"We demonstrate, for the first time, the ability to reliably assign an assemblage of larval coregonines [Salmonidae Coregoninae] to shallow and multiple deepwater species. Larval coregonines from the Apostle Islands, Lake Superior, were genotyped using restriction site-associated DNA sequencing (RADseq) and were assigned to species using reference genotypes from adult corgonines from the same region. Of the 193 genotyped larvae, 101 were assigned as Coregonus artedi (average assignment probability = 97.6%), 57 were assigned as C. kiyi (average assignment probability = 95.5%), and 28 were assigned as C. hoyi (average assignment probability = 89.0%). Coregonus artedi were collected earliest in the season, followed by C. kiyi and then C. hoyi. Estimates of genetic diversity within each species provide a baseline for future monitoring in the Apostle Islands. Our success with species assignment indicates the promise of leveraging genomic data for larval coregonine identification, which could enable assessing and evaluating early life history dynamics and recruitment processes at the species level to the benefit of ongoing coregonine restoration and management efforts.
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USA","interactions":[],"lastModifiedDate":"2023-03-08T15:17:43.127372","indexId":"70241051","displayToPublicDate":"2021-12-01T09:10:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Inter- and intra-annual effects of lethal removal on common raven abundance in Nevada and California, USA","docAbstract":"Populations of common ravens (Corvus corax; ravens) have increased rapidly within sagebrush (Artemisia spp.) ecosystems between 1960 and 2020. Although ravens are native to North America, their population densities have expanded to levels that negatively influence the population dynamics of other wildlife species of conservation concern, such as greater sage-grouse (Centrocercus urophasianus) and desert tortoises (Gopherus agassizii). For this reason, lethal removal, such as the application of the avicide DRC-1339, has been used to manage raven numbers at local scales and under certain circumstances. Because the relative effectiveness of DRC-1339 in reducing raven populations densities is not thoroughly understood, we completed 2 case studies using a before-after-control-impact experimental design of density estimates generated from point count data within a Bayesian hierarchical distance sampling framework. Specifically, we analyzed >16,000 point count surveys collected during 2009–2019 and split into 2 study designs covering multiple field sites within the Great Basin region. The first experiment evaluated intra-annual changes in density by comparing before and after treatment time periods within a single breeding season for multiple treatment regions compared to 2 control regions. The other experiment focused on inter-annual differences by comparing time periods across years before and after the onset of annual avicide application for a single treatment region compared to multiple control regions. Our models estimated a 100% probability of decline in density relative to control sites for both the intra- and inter-annual model designs. At treatment sites, expected densities of ravens varied but were reduced by 43% (95% CRI: 33–49%) and 54% (95% CRI: 24–71%) according to intra- and inter-annual analyses, respectively, whereas densities increased by 42% (95% CRI: 27–60%) and 15% (95% CRI: -17 to 58%) at control sites. Although population densities were reduced with treatments, trends indicated that sustained effort would likely be needed to maintain densities at acceptable levels within regions of interest. Effectively reducing the adverse effects of raven populations on other native species likely will depend on a variety of targeted management actions such as improving habitat quality for prey species, possibly reducing ravens’ population density, and treating the cause of increased raven abundance to reduce future carrying capacity and prevent rebounds.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/p79d-en84","usgsCitation":"O’Neil, S.T., Coates, P.S., Brockman, J.C., Jackson, P.J., Spencer, J.O., and Williams, P.J., 2021, Inter- and intra-annual effects of lethal removal on common raven abundance in Nevada and California, USA: Human–Wildlife Interactions, v. 15, no. 3, 20, 16 p., https://doi.org/10.26077/p79d-en84.","productDescription":"20, 16 p.","ipdsId":"IP-130888","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":413856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.06172324180406,\n 41.791122396069284\n ],\n [\n -120.49545088606604,\n 41.791122396069284\n ],\n [\n -120.49545088606604,\n 37.428574642347996\n ],\n [\n -114.06172324180406,\n 37.428574642347996\n ],\n [\n -114.06172324180406,\n 41.791122396069284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brockman, Julia C.","contributorId":302928,"corporation":false,"usgs":false,"family":"Brockman","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":865867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":865868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, Jack O. 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Movement patterns based on life history phases, particularly territoriality of breeding birds and transiency of nonbreeding birds, are thought to influence the frequency and efficacy of nest predation. As such, predicting where on the landscape territorial resident and non-territorial transient birds may be found in relation to the distribution of sensitive prey is of increasing importance to managers and conservationists. From 2007 to 2019, we conducted raven point count surveys between mid-March and mid-September across 43 different field sites representing typical sagebrush (Artemisia spp.) ecosystems of the Great Basin, USA. The surveys conducted during 2007–2016 were used in previously published maps of raven occurrence and density. Here, we examined the relationship between occurrence and density of ravens using spatially explicit predictions from 2 previously published studies and differentiate areas occupied by higher concentrations of resident ravens as opposed to transients. Surveys conducted during 2017–2019 were subsequently used to evaluate the predicted trends from our analytical approach. Specifically, we used residuals from a generalized linear regression to establish the relationship between occurrence and density, which ultimately resulted in a spatially explicit categorical map that identifies areas of resident versus transient ravens. We evaluated mapped categories using independently collected observed raven group sizes from the 2017–2019 survey data, as well as an independent dataset of global positioning system locations of resident and transient individuals monitored during 2019–2020. We observed moderate agreement between the mapped categories and independent datasets for both evaluation approaches. Our map provides broad inference about spatial variation in potential predation risk from ravens for species such as greater sage-grouse (Centrocercus urophasianus) and can be used as a valuable spatial layer for decision support tools aimed at guiding raven management decisions and, ultimately, improving survival and reproduction of sensitive prey within the Great Basin.
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Clustering supported classification of ChemCam data from Gale crater, Mars","interactions":[],"lastModifiedDate":"2022-04-06T13:33:12.390775","indexId":"70230276","displayToPublicDate":"2021-12-01T08:28:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Clustering supported classification of ChemCam data from Gale crater, Mars","docAbstract":"The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method non-negative matrix factorization (NMF) and the repetition of k-means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.","language":"English","publisher":"John Wiley & Sons, Inc.","doi":"10.1029/2021EA001903","usgsCitation":"Rammelkamp, K., Gasnault, O., Forni, O., Bedford, C.C., Dehouck, E., Cousin, A., Lasue, J., David, G., Gabriel, T.S., Maurice, S., and Wiens, R.C., 2021, Clustering supported classification of ChemCam data from Gale crater, Mars: Earth and Space Science, v. 8, no. 12, e2021EA001903, 27 p., https://doi.org/10.1029/2021EA001903.","productDescription":"e2021EA001903, 27 p.","ipdsId":"IP-130563","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":450112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021ea001903","text":"Publisher Index Page"},{"id":398202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale Crater, Mars","volume":"8","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-11-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Rammelkamp, Kristin","contributorId":289781,"corporation":false,"usgs":false,"family":"Rammelkamp","given":"Kristin","affiliations":[{"id":62247,"text":"Institut de Recherche en Astrophysique et Planetologie","active":true,"usgs":false}],"preferred":false,"id":839781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gasnault, Olivier","contributorId":181501,"corporation":false,"usgs":false,"family":"Gasnault","given":"Olivier","email":"","affiliations":[],"preferred":false,"id":839782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forni, Olivier","contributorId":72690,"corporation":false,"usgs":false,"family":"Forni","given":"Olivier","email":"","affiliations":[],"preferred":false,"id":839783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedford, Candice C.","contributorId":229499,"corporation":false,"usgs":false,"family":"Bedford","given":"Candice","email":"","middleInitial":"C.","affiliations":[{"id":12445,"text":"Lunar and Planetary Institute","active":true,"usgs":false}],"preferred":false,"id":839784,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dehouck, Erwin","contributorId":270386,"corporation":false,"usgs":false,"family":"Dehouck","given":"Erwin","email":"","affiliations":[{"id":56160,"text":"Université de Lyon","active":true,"usgs":false}],"preferred":false,"id":839785,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cousin, Agnes","contributorId":40139,"corporation":false,"usgs":false,"family":"Cousin","given":"Agnes","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":839786,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lasue, Jeremie","contributorId":181504,"corporation":false,"usgs":false,"family":"Lasue","given":"Jeremie","email":"","affiliations":[],"preferred":false,"id":839787,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"David, Gael","contributorId":289782,"corporation":false,"usgs":false,"family":"David","given":"Gael","email":"","affiliations":[{"id":62247,"text":"Institut de Recherche en Astrophysique et Planetologie","active":true,"usgs":false}],"preferred":false,"id":839788,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gabriel, Travis S.J. 0000-0002-9767-4153","orcid":"https://orcid.org/0000-0002-9767-4153","contributorId":267903,"corporation":false,"usgs":true,"family":"Gabriel","given":"Travis","middleInitial":"S.J.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":839789,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Maurice, Sylvestre","contributorId":82626,"corporation":false,"usgs":false,"family":"Maurice","given":"Sylvestre","email":"","affiliations":[],"preferred":false,"id":839790,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":839791,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70226858,"text":"70226858 - 2021 - Alaska natural gas hydrate production testing: Test site selection, characterization and testing operations","interactions":[],"lastModifiedDate":"2021-12-16T13:07:55.071125","indexId":"70226858","displayToPublicDate":"2021-12-01T07:06:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Alaska natural gas hydrate production testing: Test site selection, characterization and testing operations","docAbstract":"This Interagency Agreement supports the U.S. Department of Energy (DOE) and its research partners in understanding, predicting, and testing the recoverability and potential production characteristics of onshore natural gas hydrate in the Greater Prudhoe Bay area on the Alaska North Slope (ANS: Prudhoe Bay, Kuparuk River, and Milne Point areas) or other areas deemed suitable by DOE and USGS for potential long-term production testing of gas hydrate. Researchers will accomplish these tasks by evaluating the occurrence and resource potential of the known gas hydrate accumulations in the Eileen trend. Geologic, geochemical, and geophysical (2-D and 3-D seismic surveys) data from northern Alaska and other data sources, including wireline and mud log surveys of wells of opportunity, will be used to assess the occurrence and nature of the known gas hydrate accumulations. The project involves two primary areas of effort: the geologic and engineering assessment of the Eileen gas-hydrate accumulation and support of DOE and its industry partners in evaluating, planning, and preparing for drilling and testing gas hydrate research wells in northern Alaska.
","language":"English","publisher":"U.S. Department of Energy National Energy Technology Laboratory","collaboration":"Department of Energy (DOE), Alaska Department of Natural Resources, the Japan Oil Gas and Metals National Corporation (JOGMEC), and Petrotechnical Resources Alaska (PRA)","usgsCitation":"Collett, T., 2021, Alaska natural gas hydrate production testing: Test site selection, characterization and testing operations, 161 p.","productDescription":"161 p.","ipdsId":"IP-128429","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":393009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":392993,"type":{"id":15,"text":"Index Page"},"url":"https://netl.doe.gov/project-information?p=FE0022898"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.81640625,\n 68.2042121888185\n ],\n [\n -141.064453125,\n 68.2042121888185\n ],\n [\n -141.064453125,\n 71.7739410364347\n ],\n [\n -166.81640625,\n 71.7739410364347\n ],\n [\n -166.81640625,\n 68.2042121888185\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220812,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":828519,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229489,"text":"70229489 - 2021 - The Southeastern U.S. as a complex of use sites for nonbreeding rufa Red Knots: Fifteen years of band-encounter data","interactions":[],"lastModifiedDate":"2022-03-09T13:03:23.084612","indexId":"70229489","displayToPublicDate":"2021-12-01T07:01:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"title":"The Southeastern U.S. as a complex of use sites for nonbreeding rufa Red Knots: Fifteen years of band-encounter data","docAbstract":"Shorebirds have been banded for decades and monitoring programs have helped to accumulate large band-encounter datasets from across the globe; however, many of these datasets are left largely unused, particularly those collected by citizen scientists. These datasets can provide valuable insight into the migration and movement strategies of shorebirds and the threats they face throughout their migratory cycle. We used long-term (2003–2018) band-encounter data of Red Knots Calidris canutus rufa in North America to determine: (1) the spatiotemporal distribution during the nonbreeding season, (2) site fidelity to nonbreeding sites, and (3) migratory connectivity of knots using the southeastern United States (Southeast), an important overwintering and stopover area for this subspecies. Annual mean site fidelity ranged from 0% to 86% across 24 sites. We found movement between sites across the Southeast during migratory and wintering periods, indicating that knots are using the region as interconnected sites, as opposed to relying on a single site or a cluster of adjacent sites. We identified ‘hop migration’ as a common strategy for knots in the region, and showed regular within-year movement between sites in South Carolina, Georgia, and Florida. The Southeast is an understudied part of the rufa range; our results show the importance of the region to the subspecies both as a stopover and wintering area. Despite the inherent biases in the data and imperfect detection due to inconsistent survey effort, the data showed large-scale movements and confirmed the region as a complex of sites connected by knots.
This pamphlet presents a series of general reference maps showing relevant geospatial features of the U.S. Arctic boundary as defined by the U.S. Congress since 1984. The first generation of the U.S. Arctic Research and Policy Act (ARPA) boundary maps was originally formatted and published in 2009 by a private firm contracted with the National Science Foundation and the U.S. Arctic Research Commission. Recognizing the steadily increasing relevance of Arctic issues to national and global affairs that requires more functional projections and online tools, the U.S. Geological Survey (USGS) Alaska Regional Office and the National Geospatial Technical Operations Center developed this updated series of ARPA boundary maps. Map sheet 1 shows the ARPA boundary as it relates to Alaska and marine features of the Bering Sea. Map sheet 2 shows the ARPA boundary from a circumpolar perspective. Map sheet 3 shows the national boundary of the U.S. 200-nautical-mile Exclusive Economic Zone through the Bering, Chukchi, and Beaufort Seas, facilitating Arctic domain awareness and more consistent territorial assessments of the U.S. Arctic. Map sheet 4 shows, in poster-size detail, the ARPA boundary as it relates to terrestrial features of Arctic Alaska north of the Yukon and Kuskokwim Rivers. Map sheet 5 shows, in poster-size detail, the ARPA boundary as it relates to marine and terrestrial features of the Aleutian Islands. These new maps collectively illustrate several value-added attributes, including updated bathymetry and shoreline refinements, demographic information, international borders and offshore territorial claims, Alaska conservation areas, Alaska land cover, Alaska terrestrial shaded relief, annual sea ice maximum extent, annual circumpolar 10-degree-Celsius isotherm, location of active volcanoes, and updated geospatial information. The static PDF-file maps offer value as standalone products but are intended for use with a potential interactive website that can be sourced by annual data updates, allowing users to access the various map layers in a dynamic up-to-date environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3484","usgsCitation":"Williams, D.M., and Richmond, C.L., 2021, Maps of the Arctic Alaska boundary area as defined by the U.S. Arctic Research and Policy Act—Including geospatial characteristics of select marine and terrestrial features: U.S. Geological Survey Scientific Investigations Map 3484, 7 p., 5 sheets, https://doi.org/10.3133/sim3484.","productDescription":"Pamphlet: vi, 7 p.; 5 Sheets: 47.50 × 33.50 inches or smaller","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125844","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":392220,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet5.pdf","text":"Map sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 5","linkHelpText":"— The Arctic Research and Policy Act Region—Aleutian Islands"},{"id":392219,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet4.pdf","text":"Map sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 4","linkHelpText":"— The Arctic Research and Policy Act Region—Mainland Alaska"},{"id":392218,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet3.pdf","text":"Map sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 3","linkHelpText":"— The Arctic Research and Policy Act Region—U.S. Territorial Limits"},{"id":392217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet2.pdf","text":"Map sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 2","linkHelpText":"— The Arctic Research and Policy Act Region—Circumpolar Perspective"},{"id":392216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet1.pdf","text":"Map sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 1","linkHelpText":"— The Arctic Research and Policy Act Region—Bering Sea"},{"id":392249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Pamphlet"},{"id":392268,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3484/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.50390625,\n 69.83962194067463\n ],\n [\n -149.94140625,\n 70.55417853776078\n ],\n [\n -155.91796874999997,\n 71.41317683396566\n ],\n [\n -162.421875,\n 70.19999407534661\n ],\n [\n -166.11328125000003,\n 68.52823492039876\n ],\n [\n -166.640625,\n 67.20403234340081\n ],\n [\n -165.9375,\n 66.93006025862448\n ],\n [\n -168.22265625,\n 65.58572002329473\n ],\n [\n -166.11328125,\n 61.270232790000634\n ],\n [\n -165.58593749999997,\n 60.06484046010452\n ],\n [\n -164.35546875,\n 59.265880628258095\n ],\n [\n -161.3671875,\n 58.81374171570782\n ],\n [\n -147.83203125,\n 65.2198939361321\n ],\n [\n -140.625,\n 65.94647177615738\n ],\n [\n -141.50390625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Regional Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
Aquatic systems in and around the Long Island Sound (LIS) provide a variety of ecological and economic benefits, but in some areas of the LIS, aquatic ecosystems have become degraded by excess nitrogen. A substantial fraction of the nitrogen inputs to the LIS are transported through the groundwater-flow system. Because groundwater travel times in surficial aquifers can exceed 100 years, multiyear lags are introduced between inputs at the water table in recharge areas and discharge to inland or coastal receiving waters. The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection and the U.S. Environmental Protection Agency’s Long Island Sound Study, developed a steady-state groundwater model of the watersheds draining from the northern shore of the LIS for the purpose of calculating groundwater budgets and travel times to coastal waters.
The model was developed by using the MODFLOW–NWT software and existing spatial data on aquifers, river networks, land-surface altitudes, land cover, groundwater recharge, and water use. Coastal waters were delineated on the basis of the National Wetland Inventory; all non-coastal waters were collectively termed “inland waters.” A coarse-resolution model was calibrated by using the PEST++ software, long-term records of water levels in 65 wells, stream altitudes from 477 streams, base-flow records for 14 streamgages that are relatively unaffected by withdrawals, and error metrics based on incorrectly simulated flooding and incorrectly simulated dry streams. The calibrated values were used in a fine-resolution model in which the mean absolute residuals were 4.5 meters for groundwater levels, 1.3 meters for stream altitudes, and 7,200 cubic meters per day (2.9 cubic feet per second) for base flow. About 89 percent of the terrestrial cells were correctly simulated with the water table below land surface, and nearly 90 percent of the cells representing streams were correctly simulated as having the water table above the stream bottom. Together, these metrics suggest that this model is robust for simulating regional-scale groundwater patterns.
Simulated groundwater budgets were compiled for the entire study area, for each HUC12 (Hydrologic Unit Code no. 12) watershed and its adjacent coastal waters, if applicable, within the study area, and for 14 coastal-embayment watersheds. Most groundwater (90.6 percent of inflows) discharged to inland waters, with smaller fractions to coastal waters (7.0 percent) and well withdrawals (2.4 percent). When computed for HUC12 watersheds with coastal discharge, the portions of groundwater discharging to coastal waters ranged from 0.02 to 66 percent of groundwater outflows, with a median of 13 percent. Within priority-embayment watersheds, the portions of groundwater discharging to coastal waters ranged from 2 to 56 percent, with a median of 15 percent.
Groundwater travel times also were simulated for the entire study area, for each HUC12 watershed and its adjacent coastal waters, if applicable, within the study area and for 14 priority coastal embayments. Within the entire study area, the median groundwater travel time was 1.9 years, with an interquartile range of 0.1 to 5.9 years. Sensitivity analysis of groundwater travel times within a subbasin in the study area indicates that the travel times are a function of the grid resolution, with coarser grids resulting in shorter median travel times. Travel times for groundwater discharging to coastal waters were similar to travel times for groundwater discharging to inland waters, with a median of 1.9 years. Median travel times for the HUC12 watersheds ranged from 0.9 to 53.5 years, with a median of 1.8 years. Among HUC12 watersheds that include coastal areas, travel times for groundwater discharging to coastal waters ranged from less than 1 to 61.6 years, with a median of 2.8 years. The HUC12 watersheds with the longest simulated travel times were in the urban area near New York City where the model performance is less accurate. Median travel times for groundwater discharging to coastal waters within the priority-embayment watersheds ranged from less than 1 to 18.6 years, with a median of 2.3 years.
A more focused analysis was conducted for the Niantic River watershed to demonstrate the applicability of the regional model to local-scale nitrogen-transport analyses by using nitrogen-input and -attenuation rates from literature sources. Nitrogen inputs were estimated by using land-cover-based loading factors, and attenuation was estimated by using attenuation factors based on geologic zones and soil properties. Based on this analysis, groundwater transports an estimated 22,000 kilograms of nitrogen per year (2.9 kilograms of nitrogen per hectare per year) to streams, rivers, and coastal waters within the Niantic River watershed. Approximately 36 percent of discharging nitrogen is from atmospheric-deposition sources, 38 percent is from fertilizers, and 26 percent is from septic systems. Most of the groundwater-transported nitrogen (88 percent) discharges first to streams and rivers, with only 12 percent discharging directly to coastal waters. Travel times for groundwater-transported nitrogen ranged from less than 1 day to more than 100 years, with a median of 1.6 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215116","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2021, Simulation of groundwater budgets and travel times for watersheds on the north shore of Long Island Sound, with implications for nitrogen-transport studies: U.S. Geological Survey Scientific Investigations Report 2021–5116, 84 p., https://doi.org/10.3133/sir20215116.","productDescription":"Report: x, 84 p.; 2 Data Releases","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117840","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391933,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91TQ895","text":"USGS data release","linkHelpText":"Summary data on groundwater budgets and travel times for watersheds on the north shore of Long Island Sound"},{"id":391932,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLHPIT","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH groundwater flow models of steady-state conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, as well as a nitrogen transport model of the Niantic River watershed"},{"id":391931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5116/sir20215116.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5116"},{"id":391930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5116/coverthb.jpg"}],"country":"United States","state":"Connecticut, New York, Rhode Island","otherGeospatial":"Long island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.9324951171875,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 40.826280356677124\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Managers of our Nation’s resources face unprecedented challenges driven by the convergence of increasing, competing societal demands and a changing climate that affects the stability, vulnerability, and predictability of those resources. To help meet these challenges, the scientific community must take advantage of all available technologies, data, and integrative Earth systems modeling capacity to better inform resource and risk management decisions. This is the overarching goal of the U.S. Geological Survey (USGS) Earth Monitoring, Analysis, and Prediction (EarthMAP) vision: “By 2030, the USGS will deliver well integrated observations and predictions of the future state of natural systems—water, ecosystems, energy, minerals, hazards—at regional and national scales, working primarily with federal, state, and academic partners to develop and operate the capability” (U.S. Geological Survey, 2021).
Providing more integrated Earth systems science and actionable information to decision makers, stakeholders, and the public requires a better understanding of the depth and distribution of existing capacity (capabilities, tools, and techniques) across the Bureau. Identifying existing capacity is also a critical first step toward gap analysis and targeted investments to increase capacity over time. The USGS formed a Capacity Assessment Team (CAT) and charged it with (1) conducting a Request for Information (RFI) to identify existing USGS expertise and activities supportive of integrated and predictive science to inform decision making, (2) developing a strategy and proof-of-concept for a continuously updated capacity assessment capability, and (3) identifying lessons learned to inform development of best practices for future capacity assessment efforts.
The RFI took the form of a survey, with content guided by the science and technology needs identified in a USGS report titled “Grand Challenges for Integrated U.S. Geological Survey Science—A Workshop Report” (Jenni and others, 2017). The 44-question survey provided respondents the ability to rate their level of experience with a suite of priority disciplines, analysis and modeling approaches, technologies, and stakeholder engagement strategies and to enter optional narrative text for supporting context. An introductory portion focused on general science capacity assessment, followed by three sections targeting capabilities related to the foundational components of EarthMAP: (1) data and information integration, (2) integrated predictive science, and (3) actionable information.
The survey results provided a high-level snapshot of USGS capacity in the targeted areas. Respondents (1,035 individuals) represented approximately 13 percent of the USGS across all mission areas and regions. Seventy-four percent of the respondents held a science-focused position title and the remainder had position titles in information technology, computer science, management, administrative, or other (contractors, volunteers, emeritus, and unknown). To provide greater insight into respondent capabilities and activities, information from the U.S. Department of the Interior and USGS enterprise information systems were used to further characterize topical expertise and organizational associations of survey respondents. To address the ongoing need to assess the Bureau’s capacity to address integrated predictive science priorities, the CAT developed a software-based proof-of-concept called the Integrated Science Assessment Information Database (iSAID) for assembling various information sources together toward making the full extent of USGS capabilities and scientific assets available for routine capacity assessment. This proof-of-concept is intended to serve as a catalyst for further development. The process of implementing the EarthMAP capacity assessment survey, analyzing survey responses, and developing the proof-of-concept resulted in lessons learned, findings, and recommendations. Example scenarios throughout the report demonstrate how capacity assessment data can inform science planning. Three overarching findings and recommendations are:
(1) Finding: Capacity is limited in some critical disciplines, skills, and technology applications, but “sufficient” depends on the question and the need relative to availability at a given point in time.
Recommendation: Develop an on-demand capacity assessment framework that enables rapid identification and evaluation of existing and available expertise to support decision needs as they arise.
(2) Finding: Institutional barriers and lack of awareness constrain the ability of USGS staff to adopt new technologies, collaborate across administrative boundaries, and deliver actionable information to stakeholders in a timely manner. However, these barriers are not universally experienced.
Recommendation: Pursue more targeted inquiries to clarify which institutional barriers are obstructing the adoption of new technologies and approaches or the sharing of expertise and equipment across organizational and regional boundaries. These inquiries should inform USGS leadership, mission areas, and regions whether policies can be revised or whether a lack of understanding is creating perceived obstacles. Highlight cases when staff have successfully adopted new technologies and approaches to advance EarthMAP priorities and provide actionable information in a timely manner to spread awareness of how perceived obstacles can be navigated and overcome when appropriate.
(3) Finding: Examples of people and projects integrating across disciplines and scales and applying advanced approaches to meet complex stakeholder needs exist. Such examples provide transfer value across the spectrum from approach to decision making. Many projects, already underway, appear to meet elements of the EarthMAP vision, and the USGS has people who can provide leadership in multiple types of specific integrated science efforts.
Recommendation: Use these findings as a starting point for near-term strategic planning for integrated science. Highlight, incentivize, and build on existing interdisciplinary predictive science and information delivery activities across the USGS to advance toward further realization of an EarthMAP capacity.
The CAT efforts to develop and assess existing USGS capacity to advance the EarthMAP vision revealed a fundamental challenge for not only this effort but any effort to assess existing capacity: A considerable amount of thought, time, and effort is required to survey and assess capabilities and tools available to support a given need, yet best results are still likely to provide an incomplete assessment. To better meet the frequent need to assess capabilities, tools, products, and projects that address an expressed strategic priority, the CAT proposes the concept of an on-demand capacity assessment framework supported by a software package that dynamically pulls and integrates information from existing USGS information systems and public domain registries. Although existing USGS enterprise information systems currently lack the structure, cross-system consistency, interoperability, and stability to support a continuously updated capacity assessment capability, we identify reasonable near-term steps to improve the utility of information gathered on expertise and project capacity and to improve the consistency and completeness of information and the ability of USGS systems to share that information. The ability to search and characterize this information will make future assessments of capacity faster, more complete, more efficient, and more targeted. This approach would grow the Bureau’s capacity knowledge over time, iteratively improving the ability to access, leverage, and synthesize existing capabilities and assets as well as identify and fill critical gaps. The greatest promise for developing integrated science could lie in linking across existing projects and expertise to create a multi-project capacity for addressing large, complex environmental issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211102","usgsCitation":"Keisman, J.L., Bristol, S., Brown, D.S., Flickinger, A.K., Gunther, G., Murdoch, P.S., Musgrove, M., Nelson, J.C., Steyer, G.D., Thomas, K.A., and Waite, I.R., 2021, Capacity assessment for Earth Monitoring, Analysis, and Prediction (EarthMAP) and future integrated monitoring and predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021-1102, 110 p., https://doi.org/10.3133/ofr20211102.","productDescription":"Report: v, 110 p.; Data Release","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-129970","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":392008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BB5NMZ","linkHelpText":"USGS Earthmap Capacity Assessment Dataset"},{"id":392006,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1102/images"},{"id":392005,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.xml"},{"id":392004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":392003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1102/covrthb.jpg"}],"contact":"Director,
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Identifying patterns of organismal distribution can provide valuable insights for basic and applied marine and coastal ecology because understanding where animals are located is foundational to both research and science-based conservation. Understanding variation in distributional patterns can lead to a better assessment of ecological drivers and an improved ability to predict consequences of natural and altered relationships. Here, our purpose is to explore if quantifying coexisting groups of individual fish predators advances our understanding of field distribution patterns. Toward this end, we quantified locations of 59 acoustically tagged striped bass (Morone saxatilis) within a 26-stationary unit telemetry receiver array in Plum Island Estuary (PIE), MA, United States. We then used cluster analyses on spatial and temporal-spatial metrics from this dataset to (1) assess if distinct groups of individuals coexisted, (2) quantify group characteristics, and (3) test associations between groups and distribution (e.g., physical site type and region). Based on multiple lines of evidence, we identified four groups of striped bass with different space use patterns that persisted across seasons (summer and fall). Similar-sized striped bass clustered at spatial and temporal scales at which individuals within distinct groups could, and did, physically overlap. In addition, distributional groups were linked to components of physical site type and region suggesting that discrete groups of individuals can interact differently with the environment within the same ecological system. The identification of these distinct groups of individuals creates a baseline from which to explore further ecological implications of grouping behavior for research and conservation in geographically large, temporally dynamic, and spatially heterogeneous marine and coastal environments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.723025","usgsCitation":"Taylor, R.B., Mather, M.E., Smith, J., and Boles, K., 2021, Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns?: Frontiers in Marine Science, v. 8, 712025, 12 p., https://doi.org/10.3389/fmars.2021.723025.","productDescription":"712025, 12 p.","ipdsId":"IP-129993","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450128,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.723025","text":"Publisher Index Page"},{"id":397177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.89237213134766,\n 42.68344492725023\n ],\n [\n -70.74199676513672,\n 42.68344492725023\n ],\n [\n -70.74199676513672,\n 42.79313328756228\n ],\n [\n -70.89237213134766,\n 42.79313328756228\n ],\n [\n -70.89237213134766,\n 42.68344492725023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryland B.","contributorId":288583,"corporation":false,"usgs":false,"family":"Taylor","given":"Ryland","email":"","middleInitial":"B.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":838118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":288584,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph M.","affiliations":[{"id":61805,"text":"Northwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":838119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boles, Kayla M.","contributorId":288585,"corporation":false,"usgs":false,"family":"Boles","given":"Kayla M.","affiliations":[{"id":13409,"text":"Kentucky Department of Fish & Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":838120,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227476,"text":"70227476 - 2021 - Assessing the migratory histories, trophic positions, and conditions of lake sturgeon in the St. Croix and Mississippi Rivers using fin ray microchemistry, stable isotopes, and fatty acid profiles","interactions":[],"lastModifiedDate":"2022-01-19T13:07:03.401035","indexId":"70227476","displayToPublicDate":"2021-11-25T07:04:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1460,"text":"Ecological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the migratory histories, trophic positions, and conditions of lake sturgeon in the St. Croix and Mississippi Rivers using fin ray microchemistry, stable isotopes, and fatty acid profiles","docAbstract":"Reproducing populations of invasive carps (Hypophthalmichthys spp.) could alter aquatic food webs and negatively affect native fishes in the Mississippi National River and Recreation Area (MISS) and the St. Croix National Scenic Riverway (SACN). However, proposed invasive carp barriers may also threaten populations of native migratory fishes by preventing movements of fish between rivers that are necessary for life history requirements. In this study, nonlethal chemical techniques were used to provide baseline data related to the condition, trophic position, and migratory histories of lake sturgeon (Acipenser fulvescens) captured in the Mississippi and St. Croix Rivers.
Fish length and weight measurements and age estimates determined from pectoral fin rays demonstrated that lake sturgeon from the Mississippi River had greater lengths-at-age compared to sturgeon from the St. Croix River. However, length–weight relations were similar for sturgeon from the Mississippi and St. Croix Rivers. Lake sturgeon captured from different locations had distinguishable fatty acid signatures, and stable isotope analyses demonstrated that lake sturgeon from the Mississippi River generally feed at a higher trophic level than those in the St. Croix River. Strontium-to-calcium ratios (Sr:Ca) from fin ray cross sections indicated that sturgeon captured from the Mississippi River had higher Sr:Ca values than sturgeon captured from the St. Croix River, and natal origins and capture locations were not significantly different among sturgeon captured within individual rivers. Most sturgeon were captured in water with a similar Sr:Ca signature as their natal waters, indicating that there is some separation between populations of lake sturgeon in the St. Croix and Mississippi Rivers. However, Sr:Ca data indicated substantial variation in movement patterns among individual lake sturgeon, indicating that populations interact through migrations of individual fish between rivers.
Study results provide baseline condition and food web structure index data for assessing changes in lake sturgeon populations should invasive carps become established in these areas of the Mississippi and St. Croix Rivers. Controlled-exposure and telemetry studies would help verify and enhance the relations between Sr:Ca signatures in water and lake sturgeon pectoral fin rays to further assess mixing of sturgeons between rivers.
","language":"English","publisher":"Springer","doi":"10.1186/s13717-021-00344-y","usgsCitation":"Ziegeweid, J.R., Bartsch, M., Bartsch, L., Zigler, S., Kennedy, R., and Love, S.A., 2021, Assessing the migratory histories, trophic positions, and conditions of lake sturgeon in the St. Croix and Mississippi Rivers using fin ray microchemistry, stable isotopes, and fatty acid profiles: Ecological Processes, v. 10, 72, 22 p., https://doi.org/10.1186/s13717-021-00344-y.","productDescription":"72, 22 p.","ipdsId":"IP-131342","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450133,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13717-021-00344-y","text":"Publisher Index Page"},{"id":394505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Mississippi River, St. Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.22998046875,\n 44.64911632343077\n ],\n [\n -92.51312255859375,\n 44.64911632343077\n ],\n [\n -92.51312255859375,\n 45.31159750379206\n ],\n [\n -93.22998046875,\n 45.31159750379206\n ],\n [\n -93.22998046875,\n 44.64911632343077\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Michelle 0000-0002-9571-5564 mbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-9571-5564","contributorId":3165,"corporation":false,"usgs":true,"family":"Bartsch","given":"Michelle","email":"mbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zigler, Steven J. 0000-0002-4153-0652","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":244025,"corporation":false,"usgs":false,"family":"Zigler","given":"Steven J.","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":831109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Robert J 0000-0003-2135-5022","orcid":"https://orcid.org/0000-0003-2135-5022","contributorId":215686,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert J","affiliations":[{"id":39305,"text":"Former UMESC employee - retired","active":true,"usgs":false}],"preferred":false,"id":831110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Love, Seth A.","contributorId":209950,"corporation":false,"usgs":false,"family":"Love","given":"Seth","email":"","middleInitial":"A.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":831111,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226743,"text":"70226743 - 2021 - Long-term Pseudogymnoascus destructans surveillance data reveal factors contributing to pathogen presence","interactions":[],"lastModifiedDate":"2023-06-23T13:15:26.338878","indexId":"70226743","displayToPublicDate":"2021-11-25T06:49:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Long-term Pseudogymnoascus destructans surveillance data reveal factors contributing to pathogen presence","docAbstract":"The disease white-nose syndrome (WNS) was first recognized in upstate New York in 2006 and has since spread across much of the United States (U.S.), causing severe mortality in several North American bat species. To aid in the identification and monitoring of at-risk bat populations, we evaluate factors associated with the presence of the causative fungal agent of WNS, Pseudogymnoascus destructans (Pd), in the continental United States. We obtained Pd samples through hibernaculum surveys conducted from 2013 to 2020, with all samples analyzed at the U.S. Geological Survey National Wildlife Health Center. Using generalized additive models, we estimated the likelihood of Pd presence under three different hypotheses: human-mediated, species-mediated, and hibernaculum type. In addition to hypothesis-related predictor variables, a subset of models included a smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since study onset to account for spatial and temporal autocorrelation. Under all hypotheses, models indicated probability of Pd detection is best described by the smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since onset of this study. After accounting for spatial and temporal autocorrelations, only hibernaculum type significantly affected Pd presence, with mines and culverts/tunnels less likely to contain Pd compared with caves. Reduced likelihood of Pd presence in mines and culverts/tunnels bodes well for bats of the western and southern United States, where use of these hibernaculum types is more common. While our findings can help guide monitoring and management efforts, the potential for long-distance dispersal combined with variation in community composition and hibernation ecology between the western and eastern United States necessitates the continued monitoring of Pd presence.
Indicator species are species which can be monitored as an index to measure the overall health of an ecosystem. Crocodylians have been shown to be good indicators of wetland condition as they respond to changes in hydrology, can be efficiently monitored, and are a key part of ecosystem trophic relationships. Eye shine surveys at night are a standard method used to sample alligators, but because some individuals that are present in a study area may go undetected and the proportion of individuals counted is not constant over time, appropriate modeling is required to convert counts to estimates of abundance. We analyzed 13 years of American alligator (Alligator mississippiensis) survey count data from South Florida using an N-mixture model. Alligator abundance estimates were assigned to quartiles that were then represented as color coded categories of red, yellow, or green to provide a straightforward rating of Everglades restoration based on familiar stoplight coloring. These results were then compared to a previously used method in which unadjusted counts of these same data were assigned to color coded quartile categories. Water depth played a major role in the detection probability of alligators and the stoplight colors between the two methods matched 76% of the time. This suggests that the original stoplight score method provided a good overall snapshot of the trends in alligator abundance in the Everglades; however, the hierarchical models estimate abundance and trends of alligator abundance by incorporating detection probability thus providing unbiased estimates of abundance.
Effective conservation for listed migratory species requires an understanding of how drivers of population decline vary spatially and temporally, as well as knowledge of range-wide connectivity between breeding and nonbreeding areas. Environmental conditions distant from breeding areas can have lasting effects on the demography of migratory species, yet these consequences are often the least understood. Our objectives were to 1) evaluate associations between survival and extreme environmental disturbances at nonbreeding areas, including hurricanes, harmful algal blooms, and oil spills, and 2) estimate migratory connectivity between breeding and nonbreeding areas of midcontinental piping plovers (Charadrius melodus). We used capture and resighting data from 5067 individuals collected between 2002 and 2019 from breeding areas across the midcontinent, and nonbreeding areas throughout the Gulf of Mexico and southern Atlantic coasts of North America. We developed a hidden Markov multistate model to estimate seasonal survival and account for unobservable geographic locations. Hurricanes and harmful algal blooms were negatively associated with nonbreeding season survival, but we did not detect a similarly negative relationship with oil spills. Our results indicated that individuals from separate breeding areas mixed across nonbreeding areas with low migratory connectivity. Mixing among individuals in the nonbreeding season may provide a buffering effect against impacts of extreme events on any one breeding region. Our results suggest that understanding migratory connectivity and linking seasonal threats to population dynamics can better inform conservation strategies for migratory shorebirds.
Crayfish serve critical roles in aquatic ecosystems as engineers, omnivores, and prey. It is unclear how increasingly frequent extreme-flow events and warming air temperatures will affect crayfish populations, partly because there are few long-term crayfish monitoring datasets. Using a unique 10-y dataset, we asked 1) whether recruitment of crayfishes in summer responded to extreme-flow events and air temperature during spring brooding and summer growing periods and 2) whether responses were similar among 3 co-occurring crayfish species. Golden (Faxonius luteus [Creaser, 1933]), Ozark (Faxonius ozarkae [Williams, 1952]), and Spothand (Faxonius punctimanus [Creaser, 1933]) crayfishes were sampled in quadrats at 2 sites each in the Big Piney (1993–2000) and Jacks Fork (1992–2001) rivers (Missouri, USA; n = 3355 1-m2 quadrats). We used zero-inflated generalized linear models to relate variability in quadrat-level age-0 counts to mean daily maximum air temperatures and flow metrics (variability, magnitude, and frequency of extreme high- and low-flow events). Species ranged from a small-bodied, abundant habitat generalist (Golden Crayfish) to large-bodied, uncommon habitat specialists (Ozark and Spothand crayfishes). Golden Crayfish occurred in higher-velocity habitats (riffles, runs) and had variable recruitment that increased during years with few spring and summer high-flow events and summers with lower flows and warmer temperatures. In contrast, annual recruitment variability of Ozark and Spothand crayfishes was low and explained by positive effects of cooler summers and by different flow metrics. Spothand Crayfish recruitment decreased in years with frequent spring and summer high-flow events, whereas lower summer minimum flow was the only flow metric that explained slight increases in Ozark Crayfish recruitment. Relationships with the preceding year’s recruitment were quadratic for Ozark and Spothand crayfishes, suggesting potential density dependence at higher recruitment levels. Species-specific responses suggest that closely related crayfishes could respond idiosyncratically to changes in temperature and flow. Temperature- and flow-related disturbances may be key mechanisms mediating competition and, thus, may help maintain crayfish diversity. However, warming air temperatures and increasingly frequent extreme-flow events could disadvantage some species, thereby altering future crayfish assemblages.
","language":"English","publisher":"Society for Freshwater Science","doi":"10.1086/717486","usgsCitation":"Dunn, C.G., Moore, M.J., Sievert, N., Paukert, C.P., and DiStefano, R., 2021, Co-occurring lotic crayfishes exhibit variable long-term responses to extreme-flow events and temperature: Freshwater Science, v. 40, no. 4, p. 626-643, https://doi.org/10.1086/717486.","productDescription":"18 p.","startPage":"626","endPage":"643","ipdsId":"IP-127694","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/717486","text":"Publisher Index Page"},{"id":397261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big Piney River, Jacks Forks River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.84673309326172,\n 37.07791492175793\n ],\n [\n -91.80965423583984,\n 37.07791492175793\n ],\n [\n -91.80965423583984,\n 37.0921568267209\n ],\n [\n -91.84673309326172,\n 37.0921568267209\n ],\n [\n -91.84673309326172,\n 37.07791492175793\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.05821990966797,\n 37.15128685950638\n ],\n [\n -92.00122833251953,\n 37.15128685950638\n ],\n [\n -92.00122833251953,\n 37.2125580936087\n ],\n [\n -92.05821990966797,\n 37.2125580936087\n ],\n [\n -92.05821990966797,\n 37.15128685950638\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Michael J.","contributorId":274823,"corporation":false,"usgs":false,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":838224,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sievert, Nicholas A. 0000-0003-3160-7596","orcid":"https://orcid.org/0000-0003-3160-7596","contributorId":177341,"corporation":false,"usgs":false,"family":"Sievert","given":"Nicholas A.","affiliations":[],"preferred":false,"id":838448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DiStefano, Robert J.","contributorId":213268,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":838226,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226550,"text":"70226550 - 2021 - Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","interactions":[],"lastModifiedDate":"2021-11-24T13:27:41.850893","indexId":"70226550","displayToPublicDate":"2021-11-21T07:23:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","docAbstract":"