Multiple host introductions to the same non-native environment have the potential to complete life cycles of parasites incidentally transported with them. Our goal was to identify a recently detected parasitic flatworm in the invasive Brown Treesnake (Boiga irregularis) on the remote Pacific island of Guam. We considered possible factors influencing parasite transmission, and tested for correlations between infection status and potential indicators of host fitness. We used genetic data from the parasite and information about the native ranges of other possible non-native hosts to hypothesize how it arrived on Guam and how its life cycle may be currently supported.
\nWe identified the parasite by comparing larval morphology and mtDNA sequences with other Pseudophyllid tapeworms. We assessed probability of infection in individual snakes using logistic regression and examined different factors influencing presence of parasites in hosts.
\nWe identified the parasite as the pseudophyllid cestode Spirometra erinaceieuropaei, with all sampled worms from multiple snakes sharing a single mtDNA haplotype. Infection appears to be limited to the only freshwater watershed on the island, where infection prevalence was high (77.5%). Larger snakes had a higher probability of being infected, consistent with the chronic nature of such infections. While infection status was positively correlated with body condition, infected snakes tended to have lower intra-peritoneal fat body mass, potentially indicating a negative effect on energy stores.
\nWe discovered that B. irregularis inhabiting a small area of forested habitat in a freshwater watershed on Guam are often infected by a novel parasite of Asian origin. While further work is needed, this species of Spirometra, itself a non-native species, likely depends on a suite of recently introduced hosts from different parts of the world to complete the life cycle. This baseline study provides little evidence of any effects on host fitness, but additional data are needed to more thoroughly explore the consequences of infection in this invasive snake population.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0143718","usgsCitation":"Holldorf, E., Siers, S.R., Richmond, J.Q., Klug, P.E., and Reed, R., 2015, Invaded invaders: Infection of invasive Brown Treesnakes on Guam by an exotic larval cestode with a life cycle comprised of non-native hosts: PLoS ONE, v. 10, no. 12, e0143718: 16 p., https://doi.org/10.1371/journal.pone.0143718.","productDescription":"e0143718: 16 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063527","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0143718","text":"Publisher Index Page"},{"id":314453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.60411071777344,\n 13.233261466546951\n ],\n [\n 144.60411071777344,\n 13.655328309840225\n ],\n [\n 144.97283935546872,\n 13.655328309840225\n ],\n [\n 144.97283935546872,\n 13.233261466546951\n ],\n [\n 144.60411071777344,\n 13.233261466546951\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-23","publicationStatus":"PW","scienceBaseUri":"569f5e32e4b0961cf27fd169","contributors":{"authors":[{"text":"Holldorf, Elden T","contributorId":152311,"corporation":false,"usgs":false,"family":"Holldorf","given":"Elden T","affiliations":[{"id":12728,"text":"Cherokee Services Group","active":true,"usgs":false}],"preferred":false,"id":588885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":588886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":588887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klug, Page E. pklug@usgs.gov","contributorId":5545,"corporation":false,"usgs":true,"family":"Klug","given":"Page","email":"pklug@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588884,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162213,"text":"70162213 - 2015 - Caveats for correlative species distribution modeling","interactions":[],"lastModifiedDate":"2016-01-19T08:31:38","indexId":"70162213","displayToPublicDate":"2016-01-19T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Caveats for correlative species distribution modeling","docAbstract":"Correlative species distribution models are becoming commonplace in the scientific literature and public outreach products, displaying locations, abundance, or suitable environmental conditions for harmful invasive species, threatened and endangered species, or species of special concern. Accurate species distribution models are useful for efficient and adaptive management and conservation, research, and ecological forecasting. Yet, these models are often presented without fully examining or explaining the caveats for their proper use and interpretation and are often implemented without understanding the limitations and assumptions of the model being used. We describe common pitfalls, assumptions, and caveats of correlative species distribution models to help novice users and end users better interpret these models. Four primary caveats corresponding to different phases of the modeling process, each with supporting documentation and examples, include: (1) all sampling data are incomplete and potentially biased; (2) predictor variables must capture distribution constraints; (3) no single model works best for all species, in all areas, at all spatial scales, and over time; and (4) the results of species distribution models should be treated like a hypothesis to be tested and validated with additional sampling and modeling in an iterative process.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2015.06.007","usgsCitation":"Jarnevich, C.S., Stohlgren, T.J., Kumar, S., Morisette, J.T., and Holcombe, T.R., 2015, Caveats for correlative species distribution modeling: Ecological Informatics, v. 29, no. 1, p. 6-15, https://doi.org/10.1016/j.ecoinf.2015.06.007.","productDescription":"10 p.","startPage":"6","endPage":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066207","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":314454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"569f5e30e4b0961cf27fd165","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Sunil","contributorId":84992,"corporation":false,"usgs":true,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":588881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":588882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holcombe, Tracy R. holcombet@usgs.gov","contributorId":3694,"corporation":false,"usgs":true,"family":"Holcombe","given":"Tracy","email":"holcombet@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588883,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162212,"text":"70162212 - 2015 - Stability of detectability over 17 years at a single site and other lizard detection comparisons from Guam","interactions":[],"lastModifiedDate":"2016-01-19T08:38:15","indexId":"70162212","displayToPublicDate":"2016-01-19T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Stability of detectability over 17 years at a single site and other lizard detection comparisons from Guam","docAbstract":"To obtain quantitative information about population dynamics from counts of animals, the per capita detectabilities of each species must remain constant over the course of monitoring. We characterized lizard detection constancy for four species over 17 yr from a single site in northern Guam, a relatively benign situation because detection was relatively easy and we were able to hold constant the site, habitat type, species, season, and sampling method. We monitored two species of diurnal terrestrial skinks (Carlia ailanpalai [Curious Skink], Emoia caeruleocauda [Pacific Bluetailed Skink]) using glueboards placed on the ground in the shade for 3 h on rainless mornings, yielding 10,286 skink captures. We additionally monitored two species of nocturnal arboreal geckos (Hemidactylus frenatus [Common House Gecko]; Lepidodactylus lugubris [Mourning Gecko]) on the basis of 15,212 sightings. We compared these count samples to a series of complete censuses we conducted from four or more total removal plots (everything removed to mineral soil) totaling 400 m2(about 1% of study site) in each of the years 1995, 1999, and 2012, providing time-stamped quantification of detectability for each species. Unfortunately, the actual population trajectories taken by the four species were masked by unexplained variation in detectability. This observation of debilitating latent variability in lizard detectability under nearly ideal conditions undercuts our trust in population estimation techniques that fail to quantify venue-specific detectability, rely on pooled detection probability estimates, or assume that modulation in predefined environmental covariates suffices for estimating detectability.
","language":"English","publisher":"The Society for the Study of Amphibians and Reptiles","doi":"10.1670/14-085","usgsCitation":"Rodda, G.H., Dean-Bradley, K., Campbell, E., Fritts, T.H., Lardner, B., Yackel Adams, A.A., and Reed, R., 2015, Stability of detectability over 17 years at a single site and other lizard detection comparisons from Guam: Journal of Herpetology, v. 49, no. 4, p. 513-521, https://doi.org/10.1670/14-085.","productDescription":"9 p.","startPage":"513","endPage":"521","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057632","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":314455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.60411071777344,\n 13.233261466546951\n ],\n [\n 144.60411071777344,\n 13.655328309840225\n ],\n [\n 144.97283935546872,\n 13.655328309840225\n ],\n [\n 144.97283935546872,\n 13.233261466546951\n ],\n [\n 144.60411071777344,\n 13.233261466546951\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"569f5e34e4b0961cf27fd16d","contributors":{"authors":[{"text":"Rodda, Gordon H. roddag@usgs.gov","contributorId":3196,"corporation":false,"usgs":true,"family":"Rodda","given":"Gordon","email":"roddag@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dean-Bradley, Kathryn","contributorId":152309,"corporation":false,"usgs":false,"family":"Dean-Bradley","given":"Kathryn","email":"","affiliations":[{"id":18905,"text":"2016 Rosemont Drive, Landenberg, Pennsylvania, USA","active":true,"usgs":false}],"preferred":false,"id":588874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Earl W. III","contributorId":84202,"corporation":false,"usgs":true,"family":"Campbell","given":"Earl W.","suffix":"III","affiliations":[],"preferred":false,"id":588875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritts, Thomas H.","contributorId":77204,"corporation":false,"usgs":true,"family":"Fritts","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":588876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lardner, Bjorn lardnerb@usgs.gov","contributorId":5546,"corporation":false,"usgs":true,"family":"Lardner","given":"Bjorn","email":"lardnerb@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588877,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588878,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":588872,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70162084,"text":"70162084 - 2015 - Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams","interactions":[],"lastModifiedDate":"2016-06-01T15:39:43","indexId":"70162084","displayToPublicDate":"2016-01-13T10:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams","docAbstract":"Long-term changes in nitrate concentration and flux between the middle of the 20th century and the first decade of the 21st century were estimated for the Des Moines River and the Middle Illinois River, two Midwestern Corn Belt streams, using a novel weighted regression approach that is able to detect subtle changes in solute transport behavior over time. The results show that the largest changes in flow-normalized concentration and flux occurred between 1960 and 1980 in both streams, with smaller or negligible changes between 1980 and 2004. Contrasting patterns were observed between (1) nitrate export linked to non-point sources, explicitly runoff of synthetic fertilizer or other surface sources and (2) nitrate export presumably associated with point sources such as urban wastewater or confined livestock feeding facilities, with each of these modes of transport important under different domains of streamflow. Surface runoff was estimated to be consistently most important under high-flow conditions during the spring in both rivers. Nitrate export may also have been considerable in the Des Moines River even under some conditions during the winter when flows are generally lower, suggesting the influence of point sources during this time. Similar results were shown for the Middle Illinois River, which is subject to significant influence of wastewater from the Chicago area, where elevated nitrate concentrations were associated with at the lowest flows during the winter and fall. By modeling concentration directly, this study highlights the complex relationship between concentration and streamflow that has evolved in these two basins over the last 50 years. This approach provides insights about changing conditions that only become observable when stationarity in the relationship between concentration and streamflow is not assumed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.03.062","usgsCitation":"Kelly, V.J., Stets, E., and Crawford, C.G., 2015, Long-term changes in nitrate conditions over the 20th century in two Midwestern Corn Belt streams: Journal of Hydrology, v. 525, p. 559-571, https://doi.org/10.1016/j.jhydrol.2015.03.062.","productDescription":"13 p.","startPage":"559","endPage":"571","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059752","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":471511,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.03.062","text":"Publisher Index Page"},{"id":314258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa","city":"Keosaque, Peoria","otherGeospatial":"Des Moines River, Middle Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.01221466064453,\n 40.70380607385548\n ],\n [\n -92.01221466064453,\n 40.76676160346336\n ],\n [\n -91.9071578979492,\n 40.76676160346336\n ],\n [\n -91.9071578979492,\n 40.70380607385548\n ],\n [\n -92.01221466064453,\n 40.70380607385548\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.72396850585936,\n 40.602484146302096\n ],\n [\n -89.72396850585936,\n 40.76182096906601\n ],\n [\n -89.44656372070312,\n 40.76182096906601\n ],\n [\n -89.44656372070312,\n 40.602484146302096\n ],\n [\n -89.72396850585936,\n 40.602484146302096\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"525","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56977530e4b039675d00a6c0","contributors":{"authors":[{"text":"Kelly, Valerie J. vjkelly@usgs.gov","contributorId":4161,"corporation":false,"usgs":true,"family":"Kelly","given":"Valerie","email":"vjkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. estets@usgs.gov","contributorId":3593,"corporation":false,"usgs":true,"family":"Stets","given":"Edward G.","email":"estets@usgs.gov","affiliations":[],"preferred":false,"id":588485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Charles G. 0000-0003-1653-7841 cgcrawfo@usgs.gov","orcid":"https://orcid.org/0000-0003-1653-7841","contributorId":1064,"corporation":false,"usgs":true,"family":"Crawford","given":"Charles","email":"cgcrawfo@usgs.gov","middleInitial":"G.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":588486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70161998,"text":"70161998 - 2015 - Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","interactions":[],"lastModifiedDate":"2019-12-12T10:54:01","indexId":"70161998","displayToPublicDate":"2016-01-11T16:00:00","publicationYear":"2015","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":"Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","docAbstract":"The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.
","language":"English","publisher":"MDPI","doi":"10.3390/rs70912503","usgsCitation":"Jones, J., 2015, Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network: Remote Sensing, v. 9, no. 7, p. 12503-12538, https://doi.org/10.3390/rs70912503.","productDescription":"36 p.","startPage":"12503","endPage":"12538","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066317","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70912503","text":"Publisher Index Page"},{"id":314188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.331787109375,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 24.831610355586918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"5694d22de4b039675d005dc0","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":588290,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70159276,"text":"70159276 - 2015 - Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System","interactions":[],"lastModifiedDate":"2017-05-04T10:53:38","indexId":"70159276","displayToPublicDate":"2016-01-11T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5000,"text":"Long Term Resource Monitoring Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"2015-T001","title":"Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System","docAbstract":"The U.S. Geological Survey (USGS)-Upper Midwest Environmental Sciences Center (UMESC) was responsible for development of several land cover/land use (LCU) systemic datasets of the Upper Mississippi River System (UMRS). These efforts (1989 and 2000) were funded by the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program (UMRR) Long Term Resource Monitoring (LTRM) element. Development of systemic datasets includes the acquisition, processing, and serving of high-resolution aerial photography and land cover/land use spatial datasets (http://www.umesc.usgs.gov/data_library/land_cover_use/land_cover_use_data.html). In 2008, the UMRR reached a collaborative agreement with the U.S. Fish and Wildlife Service-Region 3 to collect high-resolution digital imagery of the entire UMRS floodplain during 2010–11 for LTRM. The UMESC helped acquire, process, and serve this imagery, as well as produce and serve the 2010–11 LCU systemic dataset of the UMRS floodplain. Digital imagery for Pools 13, 26, La Grange, and Open River South was collected using an Applanix DSS 439 digital sensor system with a 40 millimeter lens and Color Infrared (CIR) filter. The imagery was collected at a resolution of 20 centimeters/pixel (8 inches/pixel) for Pool 13 and 40 centimeters/pixel (16 inches/pixel) for Pools 26, Open River South, and La Grange. All imagery was projected to Universal Transverse Mercator (UTM) Zone 15, North American Datum of 1983 (NAD 83). The General Wetland Vegetation Classification (GWVC) system used for mapping is hierarchical, and its 31 classes can be collapsed into broader categories using either a 15- or 7-class level.
\nWhile the 1989 and 2000 LCU systemic datasets have not gone through a traditional thematic accuracy assessment (AA) in the past, nor have they undergone a validation analysis, the end products are of high quality. For each systemic dataset produced (1989, 2000, 2010–11), extensive field reconnaissance is performed before photointerpretation. The intent of this field reconnaissance is to learn, test, and verify image signatures as they relate to vegetation types. Questionable areas on the imagery are visited, and the plants or land features observed in the area are recorded for reference. This procedure verifies vegetation signatures on the imagery with those on the ground. In addition, once the photointerpretation is complete, the final LCU dataset undergoes extensive quality assurance/quality control to ensure the imagery is mapped correctly.
\nSince the 2000 LCU systemic dataset was developed, there has been a growing interest in completing thematic AAs for the LTRM LCU spatial datasets. The objective of an AA is to measure the probability that a particular location has been assigned its correct vegetation class. An AA estimates thematic (map class) errors in the data, giving users information needed to determine data suitability for a particular application. At the same time, data producers are able to learn more about the nature of errors in the data. Thus, the two attributes of an AA are “producers’ accuracy,” which is the probability that an AA point has been mapped correctly (also referred to as an error of omission); and “users’ accuracy,” which is the probability that the map actually represents what was found on the ground (also referred to as error of commission). Producers’ and users’ accuracies can be obtained from the same set of data by using different analyses.
\nAccuracy assessment is an extensive effort that requires seasonal field personnel and equipment, data entry, analyses, and post processing—tasks that are costly and time consuming. The geospatial team at the UMESC has suggested a validation process for understanding the accuracy of the spatial datasets, which will be tested on at least some areas of the UMRS. Validation is not a true verification of map-class type in the field; however, it can provide the user of the map with useful information that is similar to a field AA.
\nSimilar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Jakusz, J., Dieck, J., Langrehr, H., Ruhser, J., and Lubinski, S., 2015, Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System: Long Term Resource Monitoring Technical Report 2015-T001, vi, 46 p.","productDescription":"vi, 46 p.","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061221","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":310113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/mis/ltrmp2015-t001/coverthb.jpg"},{"id":310199,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/mis/ltrmp2015-t001/ltrm2015t001.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"LTRMP TR2015-T001"}],"country":"United States","state":"Iowa, Illinois, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River System","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-87.800477,42.49192],[-87.812461,42.232278],[-87.524844,41.691635],[-87.531646,39.347888],[-87.640435,39.166727],[-87.496537,38.778571],[-87.975511,38.232742],[-88.158207,37.664542],[-88.078046,37.532029],[-88.450127,37.411717],[-88.490068,37.067874],[-89.058036,37.188767],[-89.171881,37.068184],[-89.202607,36.601576],[-89.343753,36.630991],[-89.429311,36.481875],[-89.55264,36.577178],[-89.527029,36.341679],[-89.703511,36.243412],[-89.615128,36.113816],[-89.733095,36.000608],[-90.368718,35.995812],[-90.075934,36.281485],[-90.157136,36.484317],[-94.617919,36.499414],[-94.605734,39.122204],[-95.082714,39.516712],[-94.876344,39.806894],[-95.382957,40.027112],[-95.870481,40.71248],[-95.929889,41.415155],[-96.096186,41.547192],[-96.077543,41.777824],[-96.628741,42.757532],[-96.448134,43.104452],[-96.598396,43.495074],[-96.453049,43.500415],[-96.452948,45.268925],[-96.835451,45.586129],[-96.587093,45.816445],[-96.559271,46.058272],[-96.789572,46.639079],[-96.851293,47.589264],[-97.139497,48.153108],[-97.108655,48.691484],[-97.238387,48.982631],[-95.153711,48.998903],[-95.153314,49.384358],[-94.974286,49.367738],[-94.555835,48.716207],[-93.741843,48.517347],[-92.984963,48.623731],[-92.634931,48.542873],[-92.698824,48.494892],[-92.341207,48.23248],[-92.066269,48.359602],[-91.542512,48.053268],[-90.88548,48.245784],[-90.703702,48.096009],[-89.489226,48.014528],[-90.735927,47.624343],[-92.058888,46.809938],[-92.025789,46.710839],[-91.781928,46.697604],[-90.880358,46.957661],[-90.78804,46.844886],[-90.920813,46.637432],[-90.327548,46.550262],[-89.929158,46.29975],[-88.141001,45.930608],[-88.13364,45.823128],[-87.831442,45.714938],[-87.887828,45.358122],[-87.647454,45.345232],[-87.72796,45.207956],[-87.59188,45.094689],[-87.983065,44.72073],[-87.970702,44.530292],[-87.021088,45.296541],[-87.73063,43.893862],[-87.910172,43.236634],[-87.800477,42.49192]]],[[[-86.880572,45.331467],[-86.956192,45.351179],[-86.82177,45.427602],[-86.880572,45.331467]]]]},\"properties\":{\"name\":\"Iowa\",\"nation\":\"USA \"}}]}","contact":"Upper Midwest Environmental Science Center
2630 Fanta Reed Road
La Crosse, WI 54603
http://www.umesc.usgs.gov/
http://www.umesc.usgs.gov/ltrmp.html
Historical data for nine selected streamgages in southwest and south-central Kansas were used in an assessment of streamflow characteristics and trends. This information is required by the U.S. Fish and Wildlife Service and the Kansas Department of Wildlife, Parks and Tourism to assist with the effective management of Etheostoma cragini (Arkansas darter) habitats and populations in the State. Changing streamflow conditions, such as a reduction or elimination of streamflow, may adversely affect the Arkansas darter. Priority basins for the Arkansas darter represented by the selected streamgages include the Cimarron River, Rattlesnake Creek, the North Fork Ninnescah River, the South Fork Ninnescah River, the Medicine Lodge River, and the Chikaskia River.
\nStreamflow conditions were assessed using annual streamflow characteristics computed for the period of record for each of the selected streamgages. Specific streamflow characteristics computed were mean discharge, mean base flow, 90th-percentile flow, 10th-percentile flow, minimum 7-day mean flow, minimum 28-day mean flow, number of days of flow less than 1 cubic foot per second, and number of zero-flow days.
\nTwo of the priority basins had statistically significant decreases in annual mean discharge during the period of record. In the Cimarron River Basin, there was a pronounced multidecadal decrease in the magnitude and variability of annual mean discharge. Concurrently, the percentage of the annual mean discharge that was contributed by base flow increased. In the Rattlesnake Creek Basin, there was a pre-1985 decrease in annual mean discharge. Typically, in these two basins, significant decreases were indicated for mean base flow, 90th-percentile flow, 10th-percentile flow, minimum 7-day mean flow, and minimum 28-day mean flow. No significant trend in annual mean discharge was indicated for the North Fork Ninnescah, South Fork Ninnescah, Medicine Lodge, and Chikaskia River Basins. For the Medicine Lodge and Chikaskia River Basins as well as the downstream part of the South Fork Ninnescah River Basin, a significant increase in mean base flow and 10th-percentile flow was indicated. Also, for the latter two basins, a significant increase was indicated for minimum 7-day mean flow.
\nFactors investigated to explain long-term trends in annual mean discharge, or lack thereof, included precipitation and groundwater withdrawals. Annual precipitation in the study area varied substantially from 1951 to 2013 with no pronounced long-term trend. Thus, a precipitation-related explanation for the significant decrease in annual mean discharge in the Cimarron River and Rattlesnake Creek Basins was not supported. Because the most pronounced decreases in annual mean discharge were in the basin with the largest groundwater-level declines (that is, the Cimarron River Basin), both in terms of magnitude and areal extent, it is likely that groundwater withdrawals were a primary, if not dominant, causative factor.
\nThe occurrence of extremely low-flow (less than 1 cubic foot per second) and zero-flow days varied by basin and year. Typically, such days occurred in the summer and autumn for all basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155167","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Kansas Department of Wildlife, Parks and Tourism","usgsCitation":"Juracek, K.E., 2015, Streamflow characteristics and trends at selected streamgages in southwest and south-central Kansas: U.S. Geological Survey Scientific Investigations Report 2015–5167, 20 p., https://dx.doi.org/10.3133/sir20155167.","productDescription":"vi, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065381","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":312557,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5167/sir20155167.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5167"},{"id":312556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5167/coverthb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.0465087890625,\n 37.00255267215955\n ],\n [\n -102.0465087890625,\n 38.69408504756833\n ],\n [\n -96.9488525390625,\n 38.69408504756833\n ],\n [\n -96.9488525390625,\n 37.00255267215955\n ],\n [\n -102.0465087890625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
http://ks.water.usgs.gov
The main component of this publication is a geologic map database prepared using geographic information system (GIS) applications. The geodatabase of geologic points, lines, and polygons was produced as a compilation from five adjoining map sections originally published as printed maps in 1987 (see references in metadata). Four of the sections (U.S. Geological Survey Miscellaneous Field Studies Maps MF–1957, MF–1958, MF–1959, MF–1960) were created by scanning and geo-referencing stable base map material consisting of mylar positives. The final section (MF–1956) was compiled by hand tracing an enlargement of the available printed paper base map onto mylar using a #00 rapidograph pen, the mylar positive was then digitally scanned and geo-referenced. This method was chosen because the original basemap materials (mylar positives) for the MF–1956 section were unavailable at the time of this publication. Due to the condition of the available MF–1956 map section used as the base (which had previously been folded) the accuracy within the boundary of the MF–1956 section is presumed to be degraded in certain areas. The locations of the degraded areas and the degree of degradation within these areas is unclear. Final compilation of the database was completed using the ArcScan toolset, and the Editor toolset in ESRI ArcMap 10.1. Polygon topology was created from the lines and labels were added to the resultant geological polygons, lines, and points. Joseph A. Bard and David W. Ramsey updated and corrected the geodatabase, created the metadata and web presence, and provided the GIS-expertise to bring the geodatabase and metadata to completion. Included are links to files to view or print the original map sheets and the accompanying pamphlets.
\nThe orignial geologic maps were prepared under the Geothermal Research Program of the U.S. Geological Survey as a basis for interpreting the history of magmatic activity in the volcanic field. The San Francisco field, which is largely Pleistocene in age, is in northern Arizona, just north of the broad transition zone between the Colorado Plateau and the Basin and Range province. It is one of several dominantly basaltic volcanic fields of the late Cenozoic age situated near the margin of the Colorado Plateau. The volcanic field contains rocks ranging in composition from basalt to rhyolite—the products of eruption through Precambrian basement rocks and approximately a kilometer of overlying, nearly horizontal, Paleozoic and Mesozoic sedimentary rocks. About 500 km3 of erupted rocks cover about 5,000 km2 of predominantly Permian and locally preserved Triassic sedimentary rocks that form the erosionally stripped surface of the Colorado Plateau in Northern Arizona.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds961","usgsCitation":"Bard, J.A., Ramsey, D.W., Wolfe, E.W., Ulrich, G.E., Newhall, C.G., Moore, R.B., Bailey, N.G., and Holm, R.F., 2015, Database compilation for the geologic map of the San Francisco volcanic field, north-central Arizona: U.S. Geological Survey Data Series 961, https://dx.doi.org/10.3133/ds961","productDescription":"Database; Metadata; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053386","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":313858,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/mf1956","text":"Miscellaneous Field Studies Map 1956","linkHelpText":"Geologic map of the SP Mountain part of the San Francisco Volcanic Field, north-central Arizona"},{"id":313859,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/mf1957","text":"Miscellaneous Field Studies Map 1957","linkHelpText":"Geologic map of the northwest part of the San Francisco Volcanic Field, north-central Arizona"},{"id":313860,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/mf1958","text":"Miscellaneous Field Studies Map 1958","linkHelpText":"Geologic map of the southwest part of the San Francisco volcanic field, north-central Arizona"},{"id":313857,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0961/ds961_metadata_san_francisco_volcanic_field.txt","text":"San Francisco volcanic field"},{"id":314062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":313851,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/0961/ds961_database.zip","text":"Database","size":"162 MB","linkFileType":{"id":6,"text":"zip"}},{"id":313852,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/0961/readme.txt"},{"id":313853,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0961/ds961_metadata_geolines.txt","text":"Geolines"},{"id":313854,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0961/ds961_metadata_geounits.txt","text":"Geounits"},{"id":313855,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0961/ds961_metadata_map_extents.txt","text":"Map extents"},{"id":313856,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/0961/ds961_metadata_sample_points.txt","text":"Sample points"},{"id":313861,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/mf1959","text":"Miscellaneous Field Studies Map 1959","linkHelpText":"Geologic map of the central part of the San Francisco Volcanic Field, north-central Arizona"},{"id":313862,"rank":12,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/mf1960","text":"Miscellaneous Field Studies Map 1960","linkHelpText":"Geologic map of the east part of the San Francisco Volcanic Field, north-central Arizona"},{"id":399132,"rank":14,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_103857.htm"}],"country":"United States","state":"Arizona","otherGeospatial":"San Francisco volcanic field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.4161,\n 35.0761\n ],\n [\n -111.1189,\n 35.0761\n ],\n [\n -111.1189,\n 35.8411\n ],\n [\n -112.4161,\n 35.8411\n ],\n [\n -112.4161,\n 35.0761\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Hazards Program
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12201 Sunrise Valley Drive, MS 904
Reston, VA 20192
http://volcanoes.usgs.gov/
The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, National Park Service, and Argonne National Laboratory, completed a decision analysis to use in the evaluation of alternatives in the Environmental Impact Statement concerning the long-term management of water releases from Glen Canyon Dam and associated management activities. Two primary decision analysis methods, multicriteria decision analysis and the expected value of information, were used to evaluate the alternative strategies against the resource goals and to evaluate the influence of uncertainty.
\nA total of 18 performance metrics associated with 8 out of 12 resource goals (fundamental objectives) were developed by the Bureau of Reclamation and National Park Service in partnership with subject-matter teams composed of Federal, State, tribal, and private experts. A total of 19 long-term strategies associated with 7 alternatives were developed by the Bureau of Reclamation, National Park Service, Argonne National Laboratory, U.S. Geological Survey, and Cooperating Agencies. The 19 long-term strategies were evaluated against the 18 performance metrics using a series of coupled simulation models, taking into account the effects of several important sources of uncertainty. A total of 27 Federal, State, tribal, and nongovernmental agencies were invited by the Assistant Secretary of Interior to participate in a swing-weighting exercise to understand the range of perspectives about how to place relative value on the resource goals and performance metrics; 14 of the 27 chose to participate. The results of the swing-weighting exercise were combined with the evaluation of the alternatives to complete a multicriteria decision analysis. The effects of uncertainty on the ranking of long-term strategies were evaluated through calculation of the value of information.
\nThe alternatives and their long-term strategies differed across performance metrics, producing unavoidable tradeoffs; thus, there was no long-term strategy that was dominated by another across all performance metrics. When the performance of each alternative was weighted across performance metrics, three alternatives (B, D, and G) were top-ranked depending on the set of weights proposed: Alternative B was favored by those stakeholders that placed a high value on hydropower; Alternative G was favored by those stakeholders that placed a high value on the restoration of natural processes, like beachbuilding and natural vegetation; and Alternative D was favored by the remaining stakeholders. Surprisingly, these rankings were not sensitive to the critical uncertainties that were evaluated; that is, the choice of a preferred long-term strategy was sensitive to the value-based judgment about how to place relative weight on the resource goals but was not sensitive to the uncertainties in the system dynamics that were evaluated in this analysis. The one area of uncertainty that did slightly affect the ranking of alternatives was the long-term pattern of hydrological input; because of this sensitivity, some attention to the possible effects of climate change is warranted.
\nThe results of the decision analysis are meant to serve as only one of many sources of information that can be used to evaluate the alternatives proposed in the Environmental Impact Statement. These results only focus on those resource goals for which quantitative performance metrics could be formulated and evaluated; there are other important aspects of the resource goals that also need to be considered. Not all the stakeholders who were invited to participate in the decision analysis chose to do so; thus, the Bureau of Reclamation, National Park Service, and U.S. Department of Interior may want to consider other input.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155176","collaboration":"Prepared in cooperation with the Bureau of Reclamation, National Park Service, and Argonne National Laboratory","usgsCitation":"Runge, M.C., LaGory, K.E., Russell, Kendra, Balsom, J.R., Butler, R.A., Coggins, L.G., Jr., Grantz, K.A., Hayse, John, Hlohowskyj, Ihor, Korman, Josh, May, J.E., O’Rourke, D.J., Poch, L.A., Prairie, J.R., VanKuiken, J.C., Van Lonkhuyzen, R.A., Varyu, D.R., Verhaaren, B.T., Vesekla, T.D., Williams, N.T., Wuthrich, K.K., Yackulic, C.B., Billerbeck, R.P., and Knowles, G.W., 2015, Decision analysis to support development of the Glen Canyon Dam Long-Term Experimental and Management Plan: U.S. Geological Survey Scientific Investigations Report 2015–5176, 64 p., https://dx.doi.org/10.3133/sir20155176.","productDescription":"xi, 64 p.","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070238","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":312032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5176/sir20155176.pdf","text":"Report","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5176"},{"id":312031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5176/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.70825195312501,\n 35.074964853989556\n ],\n [\n -114.70825195312501,\n 37.00255267215955\n ],\n [\n -111.258544921875,\n 37.00255267215955\n ],\n [\n -111.258544921875,\n 35.074964853989556\n ],\n [\n -114.70825195312501,\n 35.074964853989556\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
12100 Beech Forest Rd., Ste 4039
Laurel, MD 20708-4039
The U.S. Government created the Kofa National Wildlife Refuge (Kofa NWR) in 1939 in response to a citizen campaign to improve desert bighorn sheep populations in Arizona. The Kofa NWR is mountainous and remote, and its management by the U.S. Fish and Wildlife Service (FWS) keeps anthropogenic disturbance levels low. As such, Partners In Flight (PIF) listed the Kofa NWR as one of its Sonoran Desert portfolio sites in its Desert Bird Conservation Plan (McCreedy and others, 2009). Research presented here demonstrates that bird communities within a well-managed and remote Sonoran Desert portfolio site can nonetheless be negatively affected by human-caused stressors like fire and Brown-headed Cowbird (Molothrus ater) parasitism, which originate from beyond the Kofa NWR’s boundaries.
\nIn chapter 1, we examine how avian productivity can be influenced by timing of nest initiation. We demonstrate that (1) late nesting dates are correlated with low winter precipitation levels, a condition expected to occur in greater frequency in coming decades due to climate change (Seager and others, 2007) and that (2) late nesting dates result in decreased productivity, due to higher rates of nest depredation and, in the case of an open-cup nesting species, higher rates of brood parasitism experienced later in the breeding season. The brood parasite, the Brown-headed Cowbird, appears to forage and roost on agricultural lands north of the Kofa NWR’s boundary. From that location, they commute to the refuge to parasitize other passerine bird nests. Drought and subsequent loss in primary production have been correlated with decreased productivity for birds that breed in arid habitats (Preston and Rotenberry, 2006; Chase and others, 2005; Johnson and others, 2002; Morrison and Bolger, 2002; Brown and Li, 1996; Anderson and Anderson, 1973), yet drought’s standing as a threat to bird populations has been underestimated in recent regional conservation planning efforts (McCreedy and others, 2009; Latta and others, 1999).
\nIn chapter 2, we examine the effects of the King Valley fire on breeding and migrant birds within the Kofa NWR. This fire was caused by incendiary weapons testing within Yuma Proving Ground, south of the Kofa NWRboundary (Esque and others, 2013). We found large differences in spring migrant and breeding species abundance and richness between bird count stations within the 2005 King Valley fire zone and bird count stations immediately outside the fire perimeter. Habitat loss to fire, and the subsequent slow regeneration of a Sonoran Desert flora that is not well adapted to fire disturbance, is a recognized threat to bird populations (McCreedy and others, 2009; Latta, 1999), and of all Sonoran Desert wildlife, birds may be the most impacted by loss of perennial Sonoran Desert vegetation to fire (Esque and Schwalbe, 2002). We conclude that decreases in both breeding and migrant use of washes within burned areas will likely persist into the long term (>25 years) due to slow return rates of xeroriparian woodlands lost in the fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151240","usgsCitation":"McCreedy, C., van Riper, C., III, Esque, T.C., and Darrah, A.J., 2015, Effects of drought and fire on bird communities of the Kofa National Wildlife Refuge, Arizona: U.S. Geological Survey Open-File Report 2015–1240, 34 p., https://dx.doi.org/10.3133/ofr20151240.","productDescription":"iv, 34 p.","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057555","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":313109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1240/ofr20151240.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1240 Report PDF"},{"id":313108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1240/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Kofa 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 -114.27154541015625,\n 32.923402043498875\n ],\n [\n -114.27154541015625,\n 33.578014746143985\n ],\n [\n -113.72772216796875,\n 33.578014746143985\n ],\n [\n -113.72772216796875,\n 32.923402043498875\n ],\n [\n -114.27154541015625,\n 32.923402043498875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC staff, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
http://sbsc.wr.usgs.gov/
Charles van Riper III
ST Research Ecologist - Emeritus
USGS/Southwest Biological Science Center
520 North Park Avenue
University of Arizona
Tucson, AZ 85719
ph (520) 670-6671 ext 267
An understanding of historic and current water quality is needed to manage and improve aquatic communities within the Blackwater River watershed, WV. The Blackwater River, which historically offered an excellent Salvelinus fontinalis (Brook Trout) fishery, has been affected by logging, coal mining, use of off-road vehicles, and land development. Using information-theoretic methods, we examined trends in water quality at 12 sites in the watershed for the 14 years of 1980–1993. Except for Beaver Creek, downward trends in acidity and upward trends in alkalinity, conductivity, and hardness were consistent with decreases in hydrogen ion concentration. Water-quality trends for Beaver Creek were inconsistent with the other sites and reflect ongoing coal-mining influences. Dissolved oxygen trended downward, possibly due to natural conditions, but remained above thresholds that would be detrimental to aquatic life. Water quality changed only slightly within the watershed from 1980–1993, possibly reflecting few changes in development and land uses during this time. These data serve as a baseline for future water-quality studies and may help to inform management planning.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.014.sp711","usgsCitation":"Smith, J., Welsh, S., Anderson, J.T., and Fortney, R.H., 2015, Water quality trends in the Blackwater River watershed, West Virginia: Southeastern Naturalist, v. 14, no. sp7, p. 103-111, https://doi.org/10.1656/058.014.sp711.","productDescription":"9 p.","startPage":"103","endPage":"111","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053509","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":313997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Blackwater River Watershed","volume":"14","issue":"sp7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568f8c3ce4b0e7a44bc5ec9c","contributors":{"authors":[{"text":"Smith, Jessica","contributorId":152104,"corporation":false,"usgs":false,"family":"Smith","given":"Jessica","email":"","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":587949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":587831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, James T.","contributorId":28071,"corporation":false,"usgs":false,"family":"Anderson","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":587950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fortney, Ronald H.","contributorId":37576,"corporation":false,"usgs":false,"family":"Fortney","given":"Ronald","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":587951,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159806,"text":"ofr20151225 - 2015 - Paleoseismology of the Denali fault system at the Schist Creek site, central Alaska","interactions":[],"lastModifiedDate":"2016-01-07T08:44:45","indexId":"ofr20151225","displayToPublicDate":"2016-01-06T18:00:00","publicationYear":"2015","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":"2015-1225","title":"Paleoseismology of the Denali fault system at the Schist Creek site, central Alaska","docAbstract":"Two hand-dug trenches at the Schist Creek site on the Denali fault system in central Alaska exposed evidence of four surface-rupturing earthquakes on the basis of upward terminations of fault strands and at least one buried, scarp-derived colluvial wedge. Limited radiocarbon ages provide some constraints on times of the ruptures. The youngest rupture (PE1) likely occurred about 200–400 years ago, the penultimate rupture (PE2) is younger than 1,200 years old, the third event back (PE3) occurred between 1,200 and 2,700 years ago, and the oldest rupture (PE4) occurred more than 2,700 and less than 17,000 years ago. Evidence for a possible additional rupture (PE4?) is equivocal and probably is related to earthquake PE4. On the basis of a nearby measured slip rate of 9.4 ± 1.6 millimeters per year and the long interevent times between our documented ruptures, we believe that our paleoseismic record at this site is incomplete. We suspect one undocumented earthquake between PE1 and PE2 and one or perhaps two more earthquakes between PE2 and PE3. We found stratigraphic evidence in the trenches for only four or possibly five (PE4?) earthquakes, but the addition of two or three inferred earthquakes yields a record of eight possible surface ruptures at the Schist Creek site. Our interpretation of the paleoseismic history at the site is consistent with recurrence intervals of several hundred years on this section of the Denali fault system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151225","collaboration":"Prepared in cooperation with the Alaska Division of Geological and Geophysical Surveys and the University of Alaska–Fairbanks","usgsCitation":"Personius, S.F., Crone, A.J., Burns, P.A.C, and Rozell, Ned, 2015, Paleoseismology of the Denali fault system at the Schist Creek site, central Alaska: U.S. Geological Survey Open-File Report 2015-1225, 15 p., 1 oversize plate, https://dx.doi.org/10.3133/ofr20151225.","productDescription":"iii, 15 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069007","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":313022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1225/coverthb.jpg"},{"id":313023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1225/ofr20151225.pdf","text":"Report","size":"33.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1225"}],"country":"United States","state":"Alaska","otherGeospatial":"Schist Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.568115234375,\n 63.35705629693301\n ],\n [\n -148.568115234375,\n 63.563229678512144\n ],\n [\n -148.128662109375,\n 63.563229678512144\n ],\n [\n -148.128662109375,\n 63.35705629693301\n ],\n [\n -148.568115234375,\n 63.35705629693301\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225-0046
http://geohazards.cr.usgs.gov/
In 2001, tags applied to the federally endangered species cui-ui (Chasmistes cujus) to study their population dynamics were discovered strewn throughout the American White Pelican (Pelecanus erythrorhynchos) nesting colony on Anaho Island, Pyramid Lake, Nevada. Cui-ui are endemic to Pyramid Lake, and Anaho Island harbors one of North America’s largest nesting colonies of American White Pelican. Cui-ui are consumed by pelicans during the fish’s spring migration into the Truckee River to reproduce. The predatory success of pelican has been validated by determining the odds of finding a tag from a predated cui-ui within the Anaho Island nesting colony. It is unknown how many cui-ui tags are eliminated by birds before arrival to the colony versus how many are brought to the colony but never recovered. The focus of this study was to improve the estimate of the chances of collecting a tag from a predated adult cui-ui in the pelican nesting colony by feeding dead tagged Lahontan cutthroat trout (Oncorhynchus clarkii henshawi) and common carp (Cyprinus carpio) to pelican and subsequently searching for these tags within the colony. We also randomly deployed 1,000 dispersal tags throughout the nesting colony, searching for these after one and two breeding seasons. After adding 1,027 fed fish to 547 previously fed fish, we estimated 5.3 percent of the tagged cui-ui taken by pelican were recovered during tag searches. A study of dispersal tags randomly deployed within the pelican nesting colony showed that 51.5 percent would be expected to be recovered after at least one breeding season after being deployed. Results of our studies indicate that more than 90 percent of tags from adult cui-ui are eliminated by birds outside the pelican nesting colony. Tags recovered from other species and the site at which they were tagged are also reported. Most notable were recovered Lahontan cutthroat trout tags, which were the highest in number, but their proximity to double-crested cormorant (Phalacrocorax auritus) nests suggests this species to be the primary predator. Tags from other species of fish came from as far as the Columbia River, Washington (about 600 kilometers). This study provides an important baseline for future tag recovery from the pelican nesting colony on Anaho Island and opens new questions to American White Pelican movement patterns.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151242","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Scoppettone, G.G., Fabes, M.C., Rissler, P.H., and Withers, Donna, 2016, Fish tag recovery from Anaho Island nesting colony, Pyramid Lake, Nevada: U.S. Geological Survey Open-File Report 2015-1242, 28 p., https://dx.doi.org/10.3133/ofr20151242.","productDescription":"Report: iv, 28 p.; 1 Appendix","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068748","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":313939,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2015/1242/ofr20151242_appendixa.xlsx","text":"Appendix A","size":"15 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2015-1242 Appendix A"},{"id":313938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1242/ofr20151242.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1242 PDF"},{"id":313937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1242/coverthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Anaho Island, Pyramid Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.52301025390624,\n 39.94475254043076\n ],\n [\n -119.52301025390624,\n 39.964095972290416\n ],\n [\n -119.49743270874022,\n 39.964095972290416\n ],\n [\n -119.49743270874022,\n 39.94475254043076\n ],\n [\n -119.52301025390624,\n 39.94475254043076\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Flooding in the Northeastern United States during 2011 was widespread and record setting. This report summarizes peak streamflows that were recorded by the U.S. Geological Survey (USGS) during separate flooding events in February, March, April, May, July, August, and September. The flooding of late April, which combined snowmelt and heavy rain and the floods associated with the tropical storms of late August and September, were the most severe and widespread. Precipitation totals from March to May for Pennsylvania, New York, and Vermont were documented as being the highest totals in 117 years of record. In late August, the heavy rains associated with Hurricane Irene produced widespread flooding in many parts of the Northeastern United States, which resulted in damage estimates in excess of $7 billion and approximately 45 deaths. In September, Tropical Storm Lee produced 6–12 inches of rain in parts of the Northeastern United States adding to the growing total of record peak streamflows set in 2011.
\nThe annual exceedance probability (AEP) for 327 streamgages in the Northeastern United States were computed using annual peak streamflow data through 2011 and are included in this report. The 2011 peak streamflow for 129 of those streamgages was estimated to have an AEP of less than or equal to 1 percent. Almost 100 of these peak streamflows were a result of the flooding associated with Hurricane Irene in late August 2011. More extreme than the 1-percent AEP, is the 0.2-percent AEP. The USGS recorded peak streamflows at 31 streamgages that equaled or exceeded the estimated 0.2-percent AEP during 2011. Collectively, the USGS recorded peak streamflows having estimated AEPs of less than 1 percent in Connecticut, Delaware, Maine, Maryland, Massachusetts, Ohio, Pennsylvania, New Hampshire, New Jersey, New York, and Vermont and new period-of-record peak streamflows were recorded at more than 180 streamgages resulting from the floods of 2011.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1821","usgsCitation":"Suro, T.P., Roland, M.A., and Kiah, R.G., 2015, Flooding in the Northeastern United States, 2011: U.S. Geological Survey Professional Paper 1821, 32 p., https://dx.doi.org/10.3133/pp1821.","productDescription":"Report: v, 32 p.; 2 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062473","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":313165,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1821/downloads/pp1821_table2.xlsx","text":"Table 2","size":"80.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 2"},{"id":313163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1821/pp1821.pdf","text":"Report","size":"16.0 MB","description":"Professional Paper 1821"},{"id":313162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1821/coverthb.jpg"},{"id":313164,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1821/downloads/pp1821_table1.xlsx","text":"Table 1","size":"152 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 1"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, Ohio, Pennsylvania, New Hampshire, New Jersey, New York, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.3447265625,\n 45.336701909968106\n ],\n [\n -72.2900390625,\n 45.398449976304086\n ],\n [\n -70.751953125,\n 46.07323062540838\n ],\n [\n -70.1806640625,\n 46.255846818480336\n ],\n [\n -69.6533203125,\n 45.706179285330855\n ],\n [\n -68.8623046875,\n 44.15068115978091\n ],\n [\n -70.224609375,\n 43.03677585761058\n ],\n [\n -70.400390625,\n 42.45588764197166\n ],\n [\n -69.5654296875,\n 42.06560675405716\n ],\n [\n -69.78515625,\n 41.3108238809182\n ],\n [\n -70.400390625,\n 40.91351257612758\n ],\n [\n -71.1474609375,\n 40.88029480552824\n ],\n [\n -72.333984375,\n 40.54720023441049\n ],\n [\n -73.6962890625,\n 40.111688665595956\n ],\n [\n -74.2236328125,\n 39.436192999314095\n ],\n [\n -74.8828125,\n 38.71980474264239\n ],\n [\n -75.05859375,\n 37.85750715625203\n ],\n [\n -75.41015624999999,\n 37.405073750176946\n ],\n [\n -75.849609375,\n 36.914764288955936\n ],\n [\n -76.728515625,\n 36.77409249464195\n ],\n [\n -78.1787109375,\n 36.949891786813296\n ],\n [\n -78.75,\n 37.75334401310656\n ],\n [\n -79.4970703125,\n 38.61687046392973\n ],\n [\n -80.244140625,\n 38.993572058209466\n ],\n [\n -81.2109375,\n 38.993572058209466\n ],\n [\n -81.298828125,\n 38.41055825094609\n ],\n [\n -82.30957031249999,\n 38.47939467327645\n ],\n [\n -83.1884765625,\n 38.71980474264239\n ],\n [\n -83.8037109375,\n 38.47939467327645\n ],\n [\n -84.638671875,\n 38.788345355085625\n ],\n [\n -85.5615234375,\n 38.685509760012\n ],\n [\n -86.044921875,\n 39.36827914916014\n ],\n [\n -85.7373046875,\n 40.48038142908172\n ],\n [\n -85.20996093749999,\n 40.91351257612758\n ],\n [\n -85.341796875,\n 42.00032514831621\n ],\n [\n -84.5068359375,\n 42.293564192170095\n ],\n [\n -83.8037109375,\n 42.35854391749705\n ],\n [\n -83.14453125,\n 42.16340342422401\n ],\n [\n -83.14453125,\n 41.73852846935917\n ],\n [\n -82.30957031249999,\n 41.57436130598913\n ],\n [\n -81.2548828125,\n 41.934976500546604\n ],\n [\n -80.15625,\n 42.391008609205045\n ],\n [\n -79.6728515625,\n 42.71473218539458\n ],\n [\n -79.62890625,\n 43.229195113965005\n ],\n [\n -78.8818359375,\n 43.61221676817573\n ],\n [\n -78.22265625,\n 43.61221676817573\n ],\n [\n -77.783203125,\n 43.929549935614595\n ],\n [\n -77.607421875,\n 44.308126684886126\n ],\n [\n -76.7724609375,\n 44.49650533109348\n ],\n [\n -75.498046875,\n 44.84029065139799\n ],\n [\n -74.8388671875,\n 45.182036837015886\n ],\n [\n -73.3447265625,\n 45.336701909968106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://water.usgs.gov/osw/
This map and associated digital databases are the result of compilation and interpretation of published and unpublished 1:250,000-scale and limited 1:500,000- to 1:63,360-scale maps. Covering the entire state of Alaska, it reflects more than a century of work by a host of geologists and almost two decades of compilation work. There are two versions of the map: a detailed digital version, and a simplified, “generalized” map for print. The map units described in the accompanying pamphlet reflect those of the detailed digital map. At the end of each unit description, the generalized map unit for that unit is listed.
\nCompilation of this map began in September 1996, using available 1:250,000-scale data to compile and release a regional map of central Alaska (Wilson and others, 1998). An ongoing iterative process was used to describe and correlate individual geologic units to produce the units for this statewide map and its interim products—a series of regional geologic map compilations—which were released as the process continued (in the references cited in the pamphlet that are shown with an *). As additional geologic data were acquired, previously released data, correlations, and interpretations were updated as needed. Compilation of this map was complex, because the original source maps were made by different generations of geologists, mapping with very different ideas. Several of the older maps were completed before the concepts of accreted (suspect) terranes or even plate tectonics existed. On the other hand, some of the more recent maps were so governed by terrane analysis that conventional stratigraphic nomenclature was not used or is obscured. We adopted a traditional stratigraphic approach and avoided use of the sometimes controversial and commonly inconsistently defined or applied terrane terminology.
\nOur decision to adopt a traditional approach is evident in a map that emphasizes the age and lithology of map units, rather than differences among fault-bounded packages of rocks. We did our best to resolve conflicting interpretations and map data from the regional compilations and from the individual source maps in areas where regional compilations had not been produced. We made every effort to preserve the original geologic map information, incorporating, where available, new data, but we were careful to not overinterpret the geologic data. Yet even our willingness to make interpretations and revisions did not enable us in some areas to resolve mapping conflicts or to reconcile different mapping styles. Therefore, there are several areas on the map where map units are separated by “quadrangle boundary faults.” More data and fieldwork may allow resolution of these conflicts.
\nThis Alaska compilation is unique in that it is integrated with a rich database of information provided in the spatial datasets and standalone attribute databases. Within the spatial files every line and polygon is attributed to its original source; the references to these sources are contained in related tables, as well as in stand-alone tables. Additional attributes include typical lithology, geologic setting, and age range for the map units. Also included are tables of radiometric ages.
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Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Environmental tracers are useful for determining groundwater age and recharge source, yet their application in studies of geogenic Cr(VI) in groundwater has been limited. Environmental tracer data from 166 wells located in the Sacramento Valley, northern California, were interpreted and compared to Cr concentrations to determine the origin and age of groundwater with elevated Cr(VI), and better understand where Cr(VI) becomes mobilized and how it evolves along flowpaths. In addition to major ion and trace element concentrations, the dataset includes δ18O, δ2H, 3H concentration, 14C activity (of dissolved inorganic C), δ13C, 3He/4He ratio, and noble gas concentrations (He, Ne, Ar, Kr, Xe). Noble gas recharge temperatures (NGTs) were computed, and age-related tracers were interpreted in combination to constrain the age distribution in samples and sort them into six different age categories spanning from <60 yr old to >10,000 yr old. Nearly all measured Cr is in the form of Cr(IV). Concentrations range from <1 to 46 μg L−1, with 10% exceeding the state of California’s Cr(VI) maximum contaminant level of 10 μg L−1. Two groups with elevated Cr(VI) (⩾5 μg L−1) were identified. Group 1 samples are from the southern part of the valley and contain modern (<60 yr old) water, have elevated NO3− concentrations (>3 mg L−1), and commonly have δ18O values enriched relative to local precipitation. These samples likely contain irrigation water and are elevated due to accelerated mobilization of Cr(VI) in the unsaturated zone (UZ) in irrigated areas. Group 2 samples are from throughout the valley and typically contain water 1000–10,000 yr old, have δ18O values consistent with local precipitation, and have unexpectedly warm NGTs. Chromium(VI) concentrations in Group 2 samples may be elevated for multiple reasons, but the hypothesis most consistent with all available data (notably, the warm NGTs) is a relatively long UZ residence time due to recharge through a deep UZ near the margin of the basin. A possible explanation for why Cr(VI) may be primarily mobilized in the UZ rather than farther along flowpaths in the oxic portion of the saturated zone is more dynamic cycling of Mn in the UZ due to transient moisture and redox conditions.
\nEnvironmental tracers provide information on groundwater age, recharge conditions, and flow processes which can be helpful for evaluating groundwater sustainability and vulnerability. Dissolved noble gas data have proven particularly useful in mountainous terrain because they can be used to determine recharge elevation. However, tracer-derived recharge elevations have not been utilized as calibration targets for numerical groundwater flow models. Herein, we constrain and calibrate a regional groundwater flow model with noble-gas-derived recharge elevations for the first time. Tritium and noble gas tracer results improved the site conceptual model by identifying a previously uncertain contribution of mountain block recharge from the Coast Mountains to an alluvial coastal aquifer in humid southwestern British Columbia. The revised conceptual model was integrated into a three-dimensional numerical groundwater flow model and calibrated to hydraulic head data in addition to recharge elevations estimated from noble gas recharge temperatures. Recharge elevations proved to be imperative for constraining hydraulic conductivity, recharge location, and bedrock geometry, and thus minimizing model nonuniqueness. Results indicate that 45% of recharge to the aquifer is mountain block recharge. A similar match between measured and modeled heads was achieved in a second numerical model that excludes the mountain block (no mountain block recharge), demonstrating that hydraulic head data alone are incapable of quantifying mountain block recharge. This result has significant implications for understanding and managing source water protection in recharge areas, potential effects of climate change, the overall water budget, and ultimately ensuring groundwater sustainability.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015WR017274","usgsCitation":"Doyle, J.M., Gleeson, T., Manning, A.H., and Mayer, K.U., 2015, Using noble gas tracers to constrain a groundwater flow model with recharge elevations: A novel approach for mountainous terrain: Water Resources Research, v. 51, no. 10, p. 8094-8113, https://doi.org/10.1002/2015WR017274.","productDescription":"20 p.","startPage":"8094","endPage":"8113","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065559","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":471513,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr017274","text":"Publisher Index Page"},{"id":313328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-12","publicationStatus":"PW","scienceBaseUri":"568ce932e4b0e7a44bc0f115","contributors":{"authors":[{"text":"Doyle, Jessica M.","contributorId":151068,"corporation":false,"usgs":false,"family":"Doyle","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":18175,"text":"Waterline Resources Inc., Nanaimo, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":584241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gleeson, Tom","contributorId":81041,"corporation":false,"usgs":true,"family":"Gleeson","given":"Tom","email":"","affiliations":[],"preferred":false,"id":584242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":584240,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, K. Ulrich","contributorId":151069,"corporation":false,"usgs":false,"family":"Mayer","given":"K.","email":"","middleInitial":"Ulrich","affiliations":[{"id":18176,"text":"Department of Earth and Ocean Science, University of British Columbia, Vancouver, British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":584243,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70157594,"text":"ofr20151187 - 2015 - Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level","interactions":[],"lastModifiedDate":"2016-01-05T08:36:22","indexId":"ofr20151187","displayToPublicDate":"2016-01-04T13:30:00","publicationYear":"2015","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":"2015-1187","title":"Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level","docAbstract":"This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results.
\nThe purpose of this report is to document and compare three simple methodologies that have previously been used to provide estimates with associated errors of the acceleration of sea-level elevation. These techniques have been used by coastal scientists and planners in assessing coastal risk over a wide range of spatial and temporal scales. Because relative sea-level (SL) elevation time series contain energetic fluctuations at many time scales, extracting what can be relatively small rate and acceleration signals (along with estimates of the error) from much larger “noise” has proven to be both difficult and controversial. Acceleration is a preferred measure of SL response to recent changes in the Earth’s climate because over time scales of 100 years or less slow vertical land motions (such as glacial isostatic adjustment) contribute only to the linear signal and not to acceleration, thus reducing the complexity of the analysis. Hence acceleration is useful if the goal of a study is to characterize and quantify the hazard associated with the changing relative elevation of water with respect to land on decadal time scales. Although in some cases it may be necessary to determine the cause of relative sea level rise, as a first step, it is important to accurately estimate the magnitude of the threat.
\nMost researchers agree that global sea level (GSL) rose persistently through much of the 20th century at about 1.5–2.0 millimeters per year (mm/yr). There is far less agreement about whether the rate of sea-level rise (SLR) is increasing (that is, an acceleration in SL).
\nRecent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, ASL(t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying ASL(t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151187","issn":"2331-1258","usgsCitation":"Doran, K.S., Howd, P.A., and Sallenger, A.H., Jr., 2015, Detecting sea-level hazards—Simple regression-based methods for calculating the acceleration of sea level: U.S. Geological Survey Open-File Report 2015–1187, 28 p.","productDescription":"v, 28 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-039300","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":313205,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1187/cover.jpg"},{"id":313038,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1187/ofr20151187.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1187"}],"contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
Statistical summaries of streamflow data collected at 184 streamgages in Iowa are presented in this report. All streamgages included for analysis have at least 10 years of continuous record collected before or through September 2013. This report is an update to two previously published reports that presented statistical summaries of selected Iowa streamflow data through September 1988 and September 1996. The statistical summaries include (1) monthly and annual flow durations, (2) annual exceedance probabilities of instantaneous peak discharges (flood frequencies), (3) annual exceedance probabilities of high discharges, and (4) annual nonexceedance probabilities of low discharges and seasonal low discharges. Also presented for each streamgage are graphs of the annual mean discharges, mean annual mean discharges, 50-percent annual flow-duration discharges (median flows), harmonic mean flows, mean daily mean discharges, and flow-duration curves. Two sets of statistical summaries are presented for each streamgage, which include (1) long-term statistics for the entire period of streamflow record and (2) recent-term statistics for or during the 30-year period of record from 1984 to 2013. The recent-term statistics are only calculated for streamgages with streamflow records pre-dating the 1984 water year and with at least 10 years of record during 1984–2013. The streamflow statistics in this report are not adjusted for the effects of water use; although some of this water is used consumptively, most of it is returned to the streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151214","collaboration":"Prepared in cooperation with the Iowa Department of Transportation, the Iowa Highway Research Board (Iowa DOT Research Project TR-669), and the U.S. Army Corps of Engineers","usgsCitation":"Eash, D.A., O’Shea, P.S., Weber, J.R., Nguyen, K.T., Montgomery, N.L., and Simonson, A.J., 2015, Statistical summaries of selected Iowa streamflow data through September 2013: U.S. Geological Survey Open-File Report 2015–1214, 18 p., https://dx.doi.org/10.3133/ofr20151214.","productDescription":"Report: vii, 18 p.; Table; Companion File","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1984-01-01","ipdsId":"IP-064768","costCenters":[{"id":351,"text":"Iowa Water Science 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The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-0661.1","usgsCitation":"Hooten, M., and Hobbs, N., 2015, A guide to Bayesian model selection for ecologists: Ecological Monographs, v. 85, no. 1, p. 3-28, https://doi.org/10.1890/14-0661.1.","productDescription":"26 p.","startPage":"3","endPage":"28","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052758","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"85","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"575941b1e4b04f417c25676b","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":637477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hobbs, N.T.","contributorId":9498,"corporation":false,"usgs":true,"family":"Hobbs","given":"N.T.","email":"","affiliations":[],"preferred":false,"id":637820,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173866,"text":"70173866 - 2015 - Distributional changes in the western Burrowing Owl (Athene cunicularia hypugaea) in North America from 1967 to 2008","interactions":[],"lastModifiedDate":"2016-07-11T12:54:16","indexId":"70173866","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Distributional changes in the western Burrowing Owl (Athene cunicularia hypugaea) in North America from 1967 to 2008","docAbstract":"The quantification of shifts in bird distributions in response to climate change provides an opportunity to gain a deeper understanding of the processes that influence species persistence. We used data from the North American Breeding Bird Survey (BBS) to document changes in the distributional limits of the western Burrowing Owl (Athene cunicularia hypugaea) from 1967 to 2008. We used logistic regression to model presence probability (p) as a function of longitude, latitude, and year. We modeled a linear trend in logit(p) through time with slope and intercept modeled as a double Fourier series of longitude and latitude. We found that the western Burrowing Owl has experienced an intriguing southward shift in the northern half of its breeding range, contrary to what is predicted by most species niche models and what has been observed for many other species in North America. The breeding range of the Burrowing Owl has been shrinking near its northern, western, and eastern edges. Our model detected the population declines that were observed in California and eastern Washington, in locations where maps based on route-specific estimating equations had predicted significant population increases. We suggest that the northern boundary of the breeding distribution of the western Burrowing Owl has contracted southward and the southern boundary of the species' breeding distribution has expanded southward into areas of northern Mexico that were formerly used only by wintering migrants.
","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-14-00004.1","usgsCitation":"Macias-Duarte, A., and Conway, C.J., 2015, Distributional changes in the western Burrowing Owl (Athene cunicularia hypugaea) in North America from 1967 to 2008: Journal of Raptor Research, v. 49, no. 1, p. 75-83, https://doi.org/10.3356/JRR-14-00004.1.","productDescription":"9 p.","startPage":"75","endPage":"83","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1967-01-01","ipdsId":"IP-058063","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323715,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125,\n 30\n ],\n [\n -125,\n 55\n ],\n [\n -95,\n 55\n ],\n [\n -95,\n 30\n ],\n [\n -125,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57627c30e4b07657d19a69d8","contributors":{"authors":[{"text":"Macias-Duarte, Alberto","contributorId":70605,"corporation":false,"usgs":true,"family":"Macias-Duarte","given":"Alberto","email":"","affiliations":[],"preferred":false,"id":639137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638861,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173656,"text":"70173656 - 2015 - A predictive model to inform adaptive management of double-crested cormorants and fisheries in Michigan","interactions":[],"lastModifiedDate":"2016-06-08T09:28:11","indexId":"70173656","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2827,"text":"Natural Resource Modeling","active":true,"publicationSubtype":{"id":10}},"title":"A predictive model to inform adaptive management of double-crested cormorants and fisheries in Michigan","docAbstract":"The proliferation of double-crested cormorants (DCCOs; Phalacrocorax auritus) in North America has raised concerns over their potential negative impacts on game, cultured and forage fishes, island and terrestrial resources, and other colonial water birds, leading to increased public demands to reduce their abundance. By combining fish surplus production and bird functional feeding response models, we developed a deterministic predictive model representing bird–fish interactions to inform an adaptive management process for the control of DCCOs in multiple colonies in Michigan. Comparisons of model predictions with observations of changes in DCCO numbers under management measures implemented from 2004 to 2012 suggested that our relatively simple model was able to accurately reconstruct past DCCO population dynamics. These comparisons helped discriminate among alternative parameterizations of demographic processes that were poorly known, especially site fidelity. Using sensitivity analysis, we also identified remaining critical uncertainties (mainly in the spatial distributions of fish vs. DCCO feeding areas) that can be used to prioritize future research and monitoring needs. Model forecasts suggested that continuation of existing control efforts would be sufficient to achieve long-term DCCO control targets in Michigan and that DCCO control may be necessary to achieve management goals for some DCCO-impacted fisheries in the state. Finally, our model can be extended by accounting for parametric or ecological uncertainty and including more complex assumptions on DCCO–fish interactions as part of the adaptive management process.
","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12071","usgsCitation":"Tsehaye, I., Jones, M., Irwin, B.J., Fielder, D., Breck, J.E., and Luukkonen, D., 2015, A predictive model to inform adaptive management of double-crested cormorants and fisheries in Michigan: Natural Resource Modeling, v. 28, no. 3, p. 348-376, https://doi.org/10.1111/nrm.12071.","productDescription":"29 p.","startPage":"348","endPage":"376","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060377","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":323242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-25","publicationStatus":"PW","scienceBaseUri":"575941b4e4b04f417c256778","contributors":{"authors":[{"text":"Tsehaye, Iyob","contributorId":106801,"corporation":false,"usgs":true,"family":"Tsehaye","given":"Iyob","email":"","affiliations":[],"preferred":false,"id":637805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Michael L.","contributorId":119922,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6600,"text":"Qauntitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":637806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641 bjirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":4037,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian","email":"bjirwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":637462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielder, David G.","contributorId":85434,"corporation":false,"usgs":true,"family":"Fielder","given":"David G.","affiliations":[],"preferred":false,"id":637807,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Breck, James E.","contributorId":171518,"corporation":false,"usgs":false,"family":"Breck","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":637808,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luukkonen, David R.","contributorId":111336,"corporation":false,"usgs":true,"family":"Luukkonen","given":"David R.","affiliations":[],"preferred":false,"id":637809,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70173548,"text":"70173548 - 2015 - Habitat use of non-native burbot in a western river","interactions":[],"lastModifiedDate":"2019-12-14T06:49:59","indexId":"70173548","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use of non-native burbot in a western river","docAbstract":"Burbot, Lota lota (Linnaeus), were illegally introduced into the Green River drainage, Wyoming in the 1990s. Burbot could potentially alter the food web in the Green River, thereby negatively influencing socially, economically, and ecologically important fish species. Therefore, managers of the Green River are interested in implementing a suppression program for burbot. Because of the cost associated with the removal of undesirable species, it is critical that suppression programs are as effective as possible. Unfortunately, relatively little is known about the habitat use of non-native burbot in lotic systems, severely limiting the effectiveness of any removal effort. We used hurdle models to identify habitat features influencing the presence and relative abundance of burbot. A total of 260 burbot was collected during 207 sampling events in the summer and autumn of 2013. Regardless of the season, large substrate (e.g., cobble, boulder) best predicted the presence and relative abundance of burbot. In addition, our models indicated that the occurrence of burbot was inversely related to mean current velocity. The efficient and effective removal of burbot from the Green River largely relies on an improved understanding of the influence of habitat on their distribution and relative abundance.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-015-2176-6","usgsCitation":"Klein, Z.B., Quist, M.C., Rhea, D.T., and Senecal, A.C., 2015, Habitat use of non-native burbot in a western river: Hydrobiologia, v. 757, no. 1, p. 61-71, https://doi.org/10.1007/s10750-015-2176-6.","productDescription":"11 p.","startPage":"61","endPage":"71","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059416","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.302734375,\n 40.78054143186033\n ],\n [\n -108.67675781249999,\n 40.78054143186033\n ],\n [\n -108.67675781249999,\n 43.389081939117496\n ],\n [\n -110.302734375,\n 43.389081939117496\n ],\n [\n -110.302734375,\n 40.78054143186033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"757","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-10","publicationStatus":"PW","scienceBaseUri":"575fd92de4b04f417c2baa21","contributors":{"authors":[{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":638626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rhea, Darren T.","contributorId":74650,"corporation":false,"usgs":true,"family":"Rhea","given":"Darren","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":638627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senecal, Anna C.","contributorId":171649,"corporation":false,"usgs":false,"family":"Senecal","given":"Anna","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":638628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173644,"text":"70173644 - 2015 - Life-history tradeoffs and reproductive cycles in Spotted Owls","interactions":[],"lastModifiedDate":"2017-10-24T15:11:03","indexId":"70173644","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3544,"text":"The Auk","onlineIssn":"1938-4254","printIssn":"0004-8038","active":true,"publicationSubtype":{"id":10}},"title":"Life-history tradeoffs and reproductive cycles in Spotted Owls","docAbstract":"The study of tradeoffs among life-history traits has long been key to understanding the evolution of life-history strategies. However, more recently, evolutionary ecologists have realized that reproductive costs have the potential to influence population dynamics. Here, we tested for costs of reproduction in the California Spotted Owl (Strix occidentalis occidentalis), and assessed whether costs of reproduction in year t − 1 on reproduction in year t could be responsible for regionally synchronized biennial cycles in reproductive output. Logistic regression analysis and multistate mark–recapture models with state uncertainty revealed that breeding reduced the likelihood of reproducing in the subsequent year by 16% to 38%, but had no influence on subsequent survival. We also found that costs of reproduction in year t − 1 were correlated with climatic conditions in year t, with evidence of higher costs during the dry phase of the El Niño–Southern Oscillation. Using a simulation-based population model, we showed that strong reproductive costs had the potential to create biennial cycles in population-level reproductive output; however, estimated costs of reproduction appeared to be too small to explain patterns observed in Spotted Owls. In the absence of strong reproductive costs, we hypothesize that observed natural cycles in the reproductive output of Spotted Owls are related to as-yet-unmeasured, regionally concordant fluctuations in environmental conditions or prey resources. Despite theoretical evidence for demographic effects, our analyses illustrate that linking tradeoffs to actual changes in population processes will be challenging because of the potential confounding effects of individual and environmental variation.
","language":"English","publisher":"American Ornithological Society","doi":"10.1642/AUK-14-98.1","usgsCitation":"Stoelting, R.E., Gutierrez, R.J., Kendall, W., and Peery, M.Z., 2015, Life-history tradeoffs and reproductive cycles in Spotted Owls: The Auk, v. 132, no. 1, p. 46-64, https://doi.org/10.1642/AUK-14-98.1.","productDescription":"19 p.","startPage":"46","endPage":"64","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051728","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471530,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1642/auk-14-98.1","text":"Publisher Index Page"},{"id":323260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"132","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57594209e4b04f417c2568ce","contributors":{"authors":[{"text":"Stoelting, Ricka E.","contributorId":171533,"corporation":false,"usgs":false,"family":"Stoelting","given":"Ricka","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":637864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gutierrez, R. J.","contributorId":7647,"corporation":false,"usgs":false,"family":"Gutierrez","given":"R.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":637865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peery, M. Zachariah","contributorId":171534,"corporation":false,"usgs":false,"family":"Peery","given":"M.","email":"","middleInitial":"Zachariah","affiliations":[],"preferred":false,"id":637866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173655,"text":"70173655 - 2015 - Re-examination of sea lamprey control policies for the St. Marys River: Completion of an adaptive management cycle","interactions":[],"lastModifiedDate":"2016-06-08T09:31:16","indexId":"70173655","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Re-examination of sea lamprey control policies for the St. Marys River: Completion of an adaptive management cycle","docAbstract":"The St. Marys River (SMR) historically has been a major producer of sea lampreys (Petromyzon marinus) in the Laurentian Great Lakes. In the early 2000s, a decision analysis (DA) project was conducted to evaluate sea lamprey control policies for the SMR; this project suggested that an integrated policy of trapping, sterile male releases, and Bayluscide treatment was the most cost-effective policy. Further, it concluded that formal assessment of larval sea lamprey abundance and distribution in the SMR would be valuable for future evaluation of control strategies. We updated this earlier analysis, adding information from annual larval assessments conducted since 1999 and evaluating additional control policies. Bayluscide treatments continued to be critical for sea lamprey control, but high recruitment compensation minimized the effectiveness of trapping and sterile male release under current feasible ranges. Because Bayluscide control is costly, development of strategies to enhance trapping success remains a priority. This study illustrates benefits of an adaptive management cycle, wherein models inform decisions, are updated based on learning achieved from those decisions, and ultimately inform future decisions.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2014-0567","usgsCitation":"Jones, M., Brenden, T.O., and Irwin, B.J., 2015, Re-examination of sea lamprey control policies for the St. Marys River: Completion of an adaptive management cycle: Canadian Journal of Fisheries and Aquatic Sciences, v. 72, no. 10, p. 1538-1551, https://doi.org/10.1139/cjfas-2014-0567.","productDescription":"14 p.","startPage":"1538","endPage":"1551","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060924","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":323243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"10","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5759422be4b04f417c256963","contributors":{"authors":[{"text":"Jones, Michael L.","contributorId":119922,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6600,"text":"Qauntitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":637810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":637811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641 bjirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":4037,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian","email":"bjirwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":637461,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}