{"pageNumber":"349","pageRowStart":"8700","pageSize":"25","recordCount":46619,"records":[{"id":70192505,"text":"70192505 - 2018 - A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","interactions":[],"lastModifiedDate":"2021-08-12T14:44:56.393391","indexId":"70192505","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","docAbstract":"
  1. The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.
  2. We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.
  3. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.
  4. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.
  5. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.12910","usgsCitation":"Chambert, T.A., Waddle, J.H., Miller, D., Walls, S.C., and Nichols, J.D., 2018, A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing: Methods in Ecology and Evolution, v. 9, no. 3, p. 560-570, https://doi.org/10.1111/2041-210X.12910.","productDescription":"11 p.","startPage":"560","endPage":"570","ipdsId":"IP-085481","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469175,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12910","text":"Publisher Index Page"},{"id":438082,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MP51H4","text":"USGS data release","linkHelpText":"Computer automated frog vocalization results from Picayune Strand State Forest, Florida 2011-2012"},{"id":347438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-09","publicationStatus":"PW","scienceBaseUri":"5a07e85ae4b09af898c8cb58","contributors":{"authors":[{"text":"Chambert, Thierry A. 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":5973,"corporation":false,"usgs":true,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":716088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A.W.","contributorId":198461,"corporation":false,"usgs":false,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":716090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":716092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716091,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192340,"text":"70192340 - 2018 - Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","interactions":[],"lastModifiedDate":"2018-03-23T12:06:28","indexId":"70192340","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","docAbstract":"

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2 = 0.77, RMSE = 7%), annual grasses (R2 = 0.70, RMSE = 21%), perennial grasses (R2 = 0.36, RMSE = 12%), forbs (R2 = 0.52, RMSE = 6%), bare earth or litter (R2 = 0.49, RMSE = 19%), and the biomass of shrubs (R2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.09.034","usgsCitation":"Anderson, K.E., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R., McIlroy, S., and Derryberry, D.R., 2018, Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning: Ecological Indicators, v. 84, p. 793-802, https://doi.org/10.1016/j.ecolind.2017.09.034.","productDescription":"10 p.","startPage":"793","endPage":"802","ipdsId":"IP-066377","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469177,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2017.09.034","text":"Publisher Index Page"},{"id":347311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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A database called the “Comprehensive Resource Database” (CRD) was prepared to support U.S. Geological Survey (USGS) assessments of technically recoverable hydrocarbons that might result from the injection of miscible or immiscible carbon dioxide (CO2) for enhanced oil recovery (EOR). The CRD was designed by INTEK Inc., a consulting company under contract to the USGS. The CRD contains data on the location, key petrophysical properties, production, and well counts (number of wells) for the major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska. The CRD includes proprietary data on petrophysical properties of fields and reservoirs from the “Significant Oil and Gas Fields of the United States Database,” prepared by Nehring Associates in 2012, and proprietary production and drilling data from the “Petroleum Information Data Model Relational U.S. Well Data,” prepared by IHS Inc. in 2012. This report describes the CRD and the computer algorithms used to (1) estimate missing reservoir property values in the Nehring Associates (2012) database, and to (2) generate values of additional properties used to characterize reservoirs suitable for miscible or immiscible CO2 flooding for EOR. Because of the proprietary nature of the data and contractual obligations, the CRD and actual data from Nehring Associates (2012) and IHS Inc. (2012) cannot be presented in this report.

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Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
https://energy.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2017-10-24","revisedDate":"2018-06-12","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f0511be4b0220bbd9a1d4c","contributors":{"authors":[{"text":"Carolus, Marshall","contributorId":192606,"corporation":false,"usgs":false,"family":"Carolus","given":"Marshall","email":"","affiliations":[],"preferred":false,"id":698713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biglarbigi, Khosrow","contributorId":192607,"corporation":false,"usgs":false,"family":"Biglarbigi","given":"Khosrow","email":"","affiliations":[],"preferred":false,"id":698714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":698712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":193092,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":698715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":698716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lohr, Celeste D. 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":3866,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste D.","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":698717,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192041,"text":"70192041 - 2018 - Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","interactions":[],"lastModifiedDate":"2017-12-11T13:31:28","indexId":"70192041","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","docAbstract":"

Identifying areas where deteriorating sewer infrastructure is in close proximity to surface waterways is needed to map likely connections between sewers and streams. We present a method to estimate sewer installation year and deterioration status using historical maps of the sewer network, parcel-scale property assessment data, and pipe material. Areas where streams were likely buried into the sewer system were mapped by intersecting the historical stream network derived from a 10-m resolution digital elevation model with sewer pipe locations. Potential sewer leakage hotspots were mapped by identifying where aging sewer pipes are in close proximity (50-m) to surface waterways. Results from Pittsburgh, Pennsylvania (USA), indicated 41% of the historical stream length was lost or buried and the potential interface between sewers and streams is great. The co-location of aging sewer infrastructure (>75 years old) near stream channels suggests that 42% of existing streams are located in areas with a high potential for sewer leakage if sewer infrastructure fails. Mapping the sewer-stream interface provides an approach to better understand areas were failing sewers may contribute a disproportional amount of nutrients and other pathogens to surface waterways.

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zones","interactions":[],"lastModifiedDate":"2018-08-03T16:23:44","indexId":"70192188","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones","docAbstract":"

Due to cultural eutrophication and global climate change, an exponential increase in the number and extent of hypoxic zones in marine and freshwater ecosystems has been observed in the last few decades. Hypoxia, or low dissolved oxygen (DO) concentrations, can produce strong negative ecological impacts and, therefore, is a management concern. We measured biomass and densities of Dreissena in Lake Erie, as well as bottom DO in 2014 using 19 high frequency data loggers distributed throughout the central basin to validate a three-dimensional hydrodynamic-ecological lake model. We found that a deep, offshore hypoxic zone was formed by early August, restricting the Dreissena population to shallow areas of the central basin. Deeper than 20 m, where bottom hypoxia routinely develops, only young of the year mussels were found in small numbers, indicating restricted recruitment and survival of young Dreissena. We suggest that monitoring Dreissenadistribution can be an effective tool for mapping the extent and frequency of hypoxia in freshwater. In addition, our results suggest that an anticipated decrease in the spatial extent of hypoxia resulting from nutrient management has the potential to increase the spatial extent of profundal habitat in the central basin available for Dreissena expansion.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.08.001","usgsCitation":"Karatayev, A.Y., Burlakova, L.E., Mehler, K., Bocaniov, S.A., Collingsworth, P.D., Warren, G., Kraus, R.T., and Hinchey, E.K., 2018, Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones: Journal of Great Lakes Research, v. 44, no. 4, p. 639-649, https://doi.org/10.1016/j.jglr.2017.08.001.","productDescription":"11 p.","startPage":"639","endPage":"649","ipdsId":"IP-074848","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469180,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://europepmc.org/articles/pmc6350251","text":"External Repository"},{"id":347124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.8104248046875,\n 42.779275360241904\n ],\n [\n -78.892822265625,\n 42.90413649491736\n ],\n [\n -79.1400146484375,\n 42.896088552971065\n ],\n [\n -79.716796875,\n 42.87596410238256\n ],\n [\n -80.255126953125,\n 42.80346172417078\n ],\n [\n -80.52978515625,\n 42.589488572714245\n ],\n [\n -80.8154296875,\n 42.68243539838623\n ],\n [\n -81.3702392578125,\n 42.66628070564928\n ],\n [\n -82.0513916015625,\n 42.28950073090457\n ],\n [\n -82.5732421875,\n 42.05745022024682\n ],\n [\n -82.9522705078125,\n 42.02889410108475\n ],\n [\n -83.12805175781249,\n 42.13896840458089\n ],\n [\n -83.36975097656249,\n 41.95949009892467\n ],\n [\n -83.5400390625,\n 41.68111756290652\n ],\n [\n -83.0731201171875,\n 41.430371882652814\n ],\n [\n -82.5457763671875,\n 41.372686481864655\n ],\n [\n -81.7657470703125,\n 41.45507852101139\n ],\n [\n -80.52429199218749,\n 41.93088998442502\n ],\n [\n -79.156494140625,\n 42.524748042348165\n ],\n [\n -78.8104248046875,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffa1e4b0220bbd988f52","contributors":{"authors":[{"text":"Karatayev, Alexander Y.","contributorId":150923,"corporation":false,"usgs":false,"family":"Karatayev","given":"Alexander","email":"","middleInitial":"Y.","affiliations":[{"id":18141,"text":"SUNY Buffalo State","active":true,"usgs":false}],"preferred":false,"id":714643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burlakova, Lyubov E.","contributorId":150918,"corporation":false,"usgs":false,"family":"Burlakova","given":"Lyubov","email":"","middleInitial":"E.","affiliations":[{"id":18141,"text":"SUNY Buffalo State","active":true,"usgs":false}],"preferred":false,"id":714644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mehler, Knut","contributorId":197953,"corporation":false,"usgs":false,"family":"Mehler","given":"Knut","email":"","affiliations":[],"preferred":false,"id":714645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bocaniov, Serghei A.","contributorId":197954,"corporation":false,"usgs":false,"family":"Bocaniov","given":"Serghei","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":714647,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warren, Glenn","contributorId":16375,"corporation":false,"usgs":true,"family":"Warren","given":"Glenn","affiliations":[],"preferred":false,"id":714648,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714642,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinchey, Elizabeth K.","contributorId":197957,"corporation":false,"usgs":false,"family":"Hinchey","given":"Elizabeth","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":714649,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192138,"text":"70192138 - 2018 - Groundwater development stress: Global-scale indices compared to regional modeling","interactions":[],"lastModifiedDate":"2018-09-12T16:08:09","indexId":"70192138","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater development stress: Global-scale indices compared to regional modeling","docAbstract":"

The increased availability of global datasets and technologies such as global hydrologic models and the Gravity Recovery and Climate Experiment (GRACE) satellites have resulted in a growing number of global-scale assessments of water availability using simple indices of water stress. Developed initially for surface water, such indices are increasingly used to evaluate global groundwater resources. We compare indices of groundwater development stress for three major agricultural areas of the United States to information available from regional water budgets developed from detailed groundwater modeling. These comparisons illustrate the potential value of regional-scale analyses to supplement global hydrological models and GRACE analyses of groundwater depletion. Regional-scale analyses allow assessments of water stress that better account for scale effects, the dynamics of groundwater flow systems, the complexities of irrigated agricultural systems, and the laws, regulations, engineering, and socioeconomic factors that govern groundwater use. Strategic use of regional-scale models with global-scale analyses would greatly enhance knowledge of the global groundwater depletion problem.

","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12578","usgsCitation":"Alley, W., Clark, B.R., Ely, M., and Faunt, C., 2018, Groundwater development stress: Global-scale indices compared to regional modeling: Groundwater, v. 56, no. 2, p. 266-275, https://doi.org/10.1111/gwat.12578.","productDescription":"10 p.","startPage":"266","endPage":"275","ipdsId":"IP-088279","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":347137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-15","publicationStatus":"PW","scienceBaseUri":"59eeffa2e4b0220bbd988f5a","contributors":{"authors":[{"text":"Alley, William 0000-0001-7286-3938 walley@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3938","contributorId":140175,"corporation":false,"usgs":true,"family":"Alley","given":"William","email":"walley@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":714370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Brian R. 0000-0001-6611-3807 brclark@usgs.gov","orcid":"https://orcid.org/0000-0001-6611-3807","contributorId":1502,"corporation":false,"usgs":true,"family":"Clark","given":"Brian","email":"brclark@usgs.gov","middleInitial":"R.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":714371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ely, Matt 0000-0003-3190-2907 mely@usgs.gov","orcid":"https://orcid.org/0000-0003-3190-2907","contributorId":1641,"corporation":false,"usgs":true,"family":"Ely","given":"Matt","email":"mely@usgs.gov","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faunt, Claudia C. 0000-0001-5659-7529 ccfaunt@usgs.gov","orcid":"https://orcid.org/0000-0001-5659-7529","contributorId":150147,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia C.","email":"ccfaunt@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192178,"text":"70192178 - 2018 - Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter","interactions":[],"lastModifiedDate":"2020-09-01T14:03:04.536827","indexId":"70192178","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter","docAbstract":"

Soil organic matter (SOM) supports the Earth's ability to sustain terrestrial ecosystems, provide food and fiber, and retains the largest pool of actively cycling carbon. Over 75% of the soil organic carbon (SOC) in the top meter of soil is directly affected by human land use. Large land areas have lost SOC as a result of land use practices, yet there are compensatory opportunities to enhance productivity and SOC storage in degraded lands through improved management practices. Large areas with and without intentional management are also being subjected to rapid changes in climate, making many SOC stocks vulnerable to losses by decomposition or disturbance. In order to quantify potential SOC losses or sequestration at field, regional, and global scales, measurements for detecting changes in SOC are needed. Such measurements and soil-management best practices should be based on well established and emerging scientific understanding of processes of C stabilization and destabilization over various timescales, soil types, and spatial scales. As newly engaged members of the International Soil Carbon Network, we have identified gaps in data, modeling, and communication that underscore the need for an open, shared network to frame and guide the study of SOM and SOC and their management for sustained production and climate regulation.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13896","usgsCitation":"Harden, J.W., Hugelius, G., Ahlstrom, A., Blankinship, J.C., Bond-Lamberty, B., Lawrence, C., Loisel, J., Malhotra, A., Jackson, R.B., Ogle, S.M., Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S.E., Cotrufo, M.F., Keiluweit, M., Heckman, K., Crow, S.E., Silver, W., DeLonge, M., and Nave, L.E., 2018, Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter: Global Change Biology, v. 24, no. 2, p. e705-e718, https://doi.org/10.1111/gcb.13896.","productDescription":"14 p.","startPage":"e705","endPage":"e718","ipdsId":"IP-090298","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":469182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Simple screening approaches for the neurotoxicant methylmercury (MeHg) in aquatic ecosystems may be helpful in risk assessments of natural resources. We explored the development of such an approach in the Adirondack Mountains of New York, USA, a region with high levels of MeHg bioaccumulation. Thirty-six perennial streams broadly representative of 1st and 2nd order streams in the region were sampled during summer low flow and analyzed for several solutes and for Hg concentrations in fish. Several landscape and chemical metrics that are typically strongly related to MeHg concentrations in aquatic biota were explored for strength of association with fish Hg concentrations. Data analyses were based on site mean length-normalized and standardized Hg concentrations (assumed to be dominantly MeHg) in whole juvenile and adult Brook Trout Salvelinus fontinalis, Creek Chub Semotilus atromaculatus, Blacknose Dace Rhinichthys atratulus, and Central Mudminnow Umbra limi, as well as on multi-species z-scores. Surprisingly, none of the landscape metrics was related significantly to regional variation in fish Hg concentrations or to z-scores across the study streams. In contrast, several chemical metrics including dissolved organic carbon (DOC) concentrations, sulfate concentrations (SO42−), pH, ultra-violet absorbance (UV254), and specific ultra-violet absorbance were significantly related to regional variation in fish Hg concentrations. A cluster analysis based on DOC, SO42−, and pH identified three distinct groups of streams: (1) high DOC, acidic streams, (2) moderate DOC, slightly acidic streams, and (3) low DOC circum-neutral streams with relatively high SO42−. Preliminary analysis indicated no significant difference in fish Hg z-scores between the moderate and high DOC groups, so these were combined for further analysis. The resulting two groups showed strong differences (p < 0.001) in DOC and SO42−concentrations as well as in pH and UV254 values. Median fish z-scores were significantly higher (p = 0.002) in the group of streams with higher DOC and UV254 and lower pH and SO42−. Screening values of DOC >6.9 mg/L, SO42− <2.8 mg/L, pH <6.6, and UV254>0.31 cm−1 were tested as thresholds to identify Adirondack stream sites likely to have higher fish Hg concentrations. By applying a combined threshold of exceedance for either pH or UV254, sites with fish Hg concentrations that exceeded a wildlife guideline of 100 ng/g were correctly identified about 75% of the time among the 36 study streams. An estimate of Hg risk applied to a data set of 391 streams based on DOC concentrations showed that about 28% were likely to pose high risk to wildlife; most of these streams were located in the western Adirondacks.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.09.031","usgsCitation":"Burns, D.A., and Riva-Murray, K., 2018, Variation in fish mercury concentrations in streams of the Adirondack region, New York: A simplified screening approach using chemical metrics: Ecological Indicators, v. 84, p. 648-661, https://doi.org/10.1016/j.ecolind.2017.09.031.","productDescription":"14 p.","startPage":"648","endPage":"661","ipdsId":"IP-086048","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":469187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2017.09.031","text":"Publisher Index Page"},{"id":346436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4376220703125,\n 42.99661231842139\n ],\n [\n -73.3172607421875,\n 42.99661231842139\n ],\n [\n -73.3172607421875,\n 44.89090425391711\n ],\n [\n -75.4376220703125,\n 44.89090425391711\n ],\n [\n -75.4376220703125,\n 42.99661231842139\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d74496e4b05fe04cc7e2d4","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riva-Murray, Karen krmurray@usgs.gov","contributorId":168654,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":712012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191264,"text":"70191264 - 2018 - Estimating carbon and showing impacts of drought using satellite data in regression-tree models","interactions":[],"lastModifiedDate":"2022-04-01T22:41:03.609362","indexId":"70191264","displayToPublicDate":"2017-10-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating carbon and showing impacts of drought using satellite data in regression-tree models","docAbstract":"

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2017.1384592","usgsCitation":"Boyte, S.P., Wylie, B.K., Howard, D., Dahal, D., and Gilmanov, T.G., 2018, Estimating carbon and showing impacts of drought using satellite data in regression-tree models: International Journal of Remote Sensing, v. 39, no. 2, p. 374-398, https://doi.org/10.1080/01431161.2017.1384592.","productDescription":"25 p.; Data release","startPage":"374","endPage":"398","ipdsId":"IP-090215","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":346312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397946,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5S8M","text":"USGS data release","description":"USGS data release","linkHelpText":"Mapping average GPP, RE, and NEP for 2000 to 2013 using satellite data integrated into 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sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":711759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":711760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176973,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":711761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":711762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilmanov, Tagir G.","contributorId":146124,"corporation":false,"usgs":false,"family":"Gilmanov","given":"Tagir","email":"","middleInitial":"G.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":711763,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202423,"text":"70202423 - 2018 - States and rates: Complementary approaches to developing flow‐ecology relationships","interactions":[],"lastModifiedDate":"2019-02-28T09:34:54","indexId":"70202423","displayToPublicDate":"2017-09-28T09:26:50","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"States and rates: Complementary approaches to developing flow‐ecology relationships","docAbstract":"
  1. In recognition of the influence of flow on riverine habitats and organisms, stream ecologists have devoted considerable effort to the development of quantitative predictive relationships describing ecological responses to flow variability, i.e. flow‐ecology relationships.
  2. Methods used to generate flow‐ecology relationships can be thought of as a continuum bookended by pure states approaches on one end and by rates approaches on the other. In pure states approaches, the ecological response is a snapshot of a condition or property (i.e. a state) derived from a single measurement in time. In contrast, ecological responses in rates approaches reflect temporal change (i.e. a rate) and are thus reliant on repeated measurements made over time.
  3. Here, we elaborate on the fundamental characteristics of different approaches (pure states, rates and an intermediate approach we call repeated states) for generating flow‐ecology relationships, examine how commonly the different approaches are used in the flow‐ecology literature, conduct an independent analysis to illustrate the different outcomes achieved by applying repeated‐states and rates approaches using a dataset for stream fish diversity in relation to flow magnitude, and identify some of the different ways ecologists are applying rates approaches in flow ecology.
  4. Our literature review revealed that repeated‐states approaches (53% of reviewed studies) were used far more commonly than either pure states (19%) or rates (12%) approaches to generate flow‐ecology relationships. The remaining hybrid studies (17%) used both state and rate responses, and thus also relied on repeated measurements over time.
  5. Despite frequent collection of data suitable for rates approaches, flow‐ecology relationships have generally been developed using states approaches that relate changes in ecological states to different long‐term average flow conditions, rather than to specific flow sequences over much shorter time intervals. Such flow‐ecology relationships cannot generate temporally specific predictions of ecological responses to changing flow conditions (i.e. the expected change in state following a specific flow sequence), nor can they describe demographic processes underlying observed changes. While there are different scenarios in which a pure or repeated‐states approach would be useful, more frequent use of rates approaches would increase our ability to test flow‐ecology hypotheses and our mechanistic understanding of flow‐ecology relationships.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13001","usgsCitation":"Wheeler, K., Wenger, S.J., and Freeman, M., 2018, States and rates: Complementary approaches to developing flow‐ecology relationships: Freshwater Biology, v. 63, no. 8, p. 906-916, https://doi.org/10.1111/fwb.13001.","productDescription":"11 p.","startPage":"906","endPage":"916","ipdsId":"IP-088072","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":361605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"8","noUsgsAuthors":false,"publicationDate":"2017-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Wheeler, Kit","contributorId":203872,"corporation":false,"usgs":false,"family":"Wheeler","given":"Kit","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":758397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wenger, Seth J.","contributorId":64786,"corporation":false,"usgs":true,"family":"Wenger","given":"Seth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":758398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":758399,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191912,"text":"70191912 - 2018 - Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States","interactions":[],"lastModifiedDate":"2018-03-26T14:34:07","indexId":"70191912","displayToPublicDate":"2017-09-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States","docAbstract":"

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

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\"}}]}\n","volume":"20","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-19","publicationStatus":"PW","scienceBaseUri":"59e86834e4b05fe04cd4d1e7","contributors":{"authors":[{"text":"Magarey, Roger","contributorId":197512,"corporation":false,"usgs":false,"family":"Magarey","given":"Roger","email":"","affiliations":[],"preferred":false,"id":713655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newton, Leslie","contributorId":197513,"corporation":false,"usgs":false,"family":"Newton","given":"Leslie","email":"","affiliations":[],"preferred":false,"id":713656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hong, Seung C.","contributorId":197514,"corporation":false,"usgs":false,"family":"Hong","given":"Seung","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":713657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takeuchi, Yu","contributorId":197515,"corporation":false,"usgs":false,"family":"Takeuchi","given":"Yu","email":"","affiliations":[],"preferred":false,"id":713658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christie, Dave","contributorId":197516,"corporation":false,"usgs":false,"family":"Christie","given":"Dave","email":"","affiliations":[],"preferred":false,"id":713659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":713654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kohl, Lisa","contributorId":197517,"corporation":false,"usgs":false,"family":"Kohl","given":"Lisa","email":"","affiliations":[],"preferred":false,"id":713660,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Damus, Martin","contributorId":197518,"corporation":false,"usgs":false,"family":"Damus","given":"Martin","email":"","affiliations":[],"preferred":false,"id":713661,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Higgins, Steven I.","contributorId":197519,"corporation":false,"usgs":false,"family":"Higgins","given":"Steven","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":713662,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Leah","contributorId":197520,"corporation":false,"usgs":false,"family":"Miller","given":"Leah","email":"","affiliations":[],"preferred":false,"id":713663,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Castro, Karen","contributorId":197521,"corporation":false,"usgs":false,"family":"Castro","given":"Karen","email":"","affiliations":[],"preferred":false,"id":713664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"West, Amanda M.","contributorId":139058,"corporation":false,"usgs":false,"family":"West","given":"Amanda M.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":713716,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hastings, John","contributorId":197552,"corporation":false,"usgs":false,"family":"Hastings","given":"John","email":"","affiliations":[{"id":34952,"text":"Center for IPM, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":713717,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cook, Gericke","contributorId":197522,"corporation":false,"usgs":false,"family":"Cook","given":"Gericke","email":"","affiliations":[],"preferred":false,"id":713665,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kartesz, John","contributorId":197523,"corporation":false,"usgs":false,"family":"Kartesz","given":"John","affiliations":[],"preferred":false,"id":713666,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Koop, Anthony","contributorId":197524,"corporation":false,"usgs":false,"family":"Koop","given":"Anthony","email":"","affiliations":[],"preferred":false,"id":713667,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70190745,"text":"70190745 - 2018 - Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling","interactions":[],"lastModifiedDate":"2018-02-12T15:44:26","indexId":"70190745","displayToPublicDate":"2017-09-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling","docAbstract":"

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13863","usgsCitation":"Feng, X., Uriarte, M., Gonzalez, G., Reed, S.C., Thompson, J., Zimmerman, J.K., and Murphy, L., 2018, Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling: Global Change Biology, v. 24, no. 1, p. e213-e232, https://doi.org/10.1111/gcb.13863.","productDescription":"20 p.","startPage":"e213","endPage":"e232","ipdsId":"IP-086186","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":487993,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.13863","text":"Publisher Index Page"},{"id":345704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-21","publicationStatus":"PW","scienceBaseUri":"59ba43b6e4b091459a5629a3","contributors":{"authors":[{"text":"Feng, Xiaohui","contributorId":196416,"corporation":false,"usgs":false,"family":"Feng","given":"Xiaohui","email":"","affiliations":[],"preferred":false,"id":710300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uriarte, Maria","contributorId":196417,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","email":"","affiliations":[],"preferred":false,"id":710301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gonzalez, Grizelle","contributorId":191117,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Grizelle","email":"","affiliations":[],"preferred":false,"id":710302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":710299,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Jill","contributorId":201454,"corporation":false,"usgs":false,"family":"Thompson","given":"Jill","email":"","affiliations":[],"preferred":false,"id":710303,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zimmerman, Jess K.","contributorId":196419,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Jess","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":710304,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murphy, Lora","contributorId":196420,"corporation":false,"usgs":false,"family":"Murphy","given":"Lora","email":"","affiliations":[],"preferred":false,"id":710305,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190445,"text":"70190445 - 2018 - Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","interactions":[],"lastModifiedDate":"2018-03-05T15:50:35","indexId":"70190445","displayToPublicDate":"2017-08-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","title":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","docAbstract":"Northern tamarisk beetles (Diorhabda carinulata) were released in the Upper Colorado River Basin in the United States in\r\n2004–2007 to defoliate introduced tamarisk shrubs (Tamarix spp.) in the region’s riparian zones. The primary purpose was\r\nto control the invasive shrub and reduce evapotranspiration (ET) by tamarisk in an attempt to increase stream flows. We\r\nevaluated beetle–tamarisk interactions with MODIS and Landsat imagery on 13 river systems, with vegetation indices used\r\nas indicators of the extent of defoliation and ET. Beetles are widespread and exhibit a pattern of colonize–defoliate–emigrate,\r\nso that riparian zones contain a mosaic of completely defoliated, partially defoliated, and refoliated tamarisk stands. Based\r\non satellite data and ET algorithms, mean ET before beetle release (2000–2006) was 416 mm/year compared to postrelease\r\n(2007–2015) ET of 355 mm/year (p<0.05) for a net reduction of 61 mm/year. This is lower than initial literature projections\r\nthat ET would be reduced by 300–460 mm/year. Reasons for the lower-than-expected ET reductions are because baseline ET\r\nrates are lower than initially projected, and percentage ET reduction is low because tamarisk stands tend to regrow new leaves\r\nafter defoliation and other plants help maintain canopy cover. Overall reductions in tamarisk green foliage during the study\r\nare 21%. However, ET in the Upper Basin has shown a steady decline since 2007 and equilibrium has not yet been reached.\r\nDefoliation is now proceeding from the Upper Basin into the Lower Basin at a rate of 40 km/year, much faster than initially\r\nprojected.","language":"English","publisher":"Wiley","doi":"10.1111/rec.12575","usgsCitation":"Nagler, P.L., Nguyen, U., Bateman, H.L., Jarchow, C., Glenn, E., Waugh, W.J., and van Riper, C., 2018, Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin: Restoration Ecology, v. 26, no. 2, p. 348-359, https://doi.org/10.1111/rec.12575.","productDescription":"12 p.","startPage":"348","endPage":"359","ipdsId":"IP-079583","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":345398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.78515624999999,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 33.94335994657882\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-25","publicationStatus":"PW","scienceBaseUri":"59a9203ee4b07e1a023ccd95","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nguyen, Uyen","contributorId":71863,"corporation":false,"usgs":false,"family":"Nguyen","given":"Uyen","email":"","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bateman, Heather L.","contributorId":72294,"corporation":false,"usgs":true,"family":"Bateman","given":"Heather","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":709187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":709190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":709191,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190198,"text":"70190198 - 2018 - Weather-centric rangeland revegetation planning","interactions":[],"lastModifiedDate":"2017-12-12T12:31:33","indexId":"70190198","displayToPublicDate":"2017-08-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Weather-centric rangeland revegetation planning","docAbstract":"Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2017.07.003","usgsCitation":"Hardegree, S.P., Abatzoglou, J.T., Brunson, M.W., Germino, M., Hegewisch, K.C., Moffet, C.A., Pilliod, D.S., Roundy, B.A., Boehm, A.R., and Meredith, G.R., 2018, Weather-centric rangeland revegetation planning: Rangeland Ecology and Management, v. 71, no. 1, p. 1-11, https://doi.org/10.1016/j.rama.2017.07.003.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-081305","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":498721,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/671080","text":"External Repository"},{"id":344996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599bf122e4b0b589267ed339","contributors":{"authors":[{"text":"Hardegree, Stuart P.","contributorId":195696,"corporation":false,"usgs":false,"family":"Hardegree","given":"Stuart","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":707927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":707928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brunson, Mark W.","contributorId":195697,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":707925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hegewisch, Katherine C.","contributorId":195698,"corporation":false,"usgs":false,"family":"Hegewisch","given":"Katherine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707930,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moffet, Corey A.","contributorId":195699,"corporation":false,"usgs":false,"family":"Moffet","given":"Corey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707931,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":707926,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roundy, Bruce A.","contributorId":178261,"corporation":false,"usgs":false,"family":"Roundy","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boehm, Alex R.","contributorId":195700,"corporation":false,"usgs":false,"family":"Boehm","given":"Alex","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meredith, Gwendwr R.","contributorId":195701,"corporation":false,"usgs":false,"family":"Meredith","given":"Gwendwr","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707934,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70195497,"text":"70195497 - 2018 - Temporal variation of tectonic tremor activity in southern Taiwan around the 2010 ML6.4 Jiashian Earthquake","interactions":[],"lastModifiedDate":"2018-02-20T10:20:49","indexId":"70195497","displayToPublicDate":"2017-07-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Temporal variation of tectonic tremor activity in southern Taiwan around the 2010 ML6.4 Jiashian Earthquake","docAbstract":"Deep tectonic tremor, which is extremely sensitive to small stress variations, could be used to\nmonitor fault-zone processes during large earthquake cycles and aseismic processes before\nlarge earthquakes. In this study, we develop an algorithm for the automatic detection and\nlocation of tectonic tremor beneath the southern Central Range of Taiwan and examine the\nspatio-temporal relationship between tremor and the 4 March 2010 ML6.4 Jiashian\nearthquake, located about 20 km from active tremor sources. We find that tremor in this\nregion has a relatively short duration, short recurrence time, and no consistent correlation\nwith surface GPS data. We find a short-term increase in the tremor rate 19 days before the\nJiashian mainshock, and around the time when the tremor rate began to rise, one GPS station\nrecorded a flip in its direction of motion. We hypothesize that tremor is driven by a slow-slip\nevent that preceded the occurrence of the shallower Jiashian mainshock, even though the\ninferred slip is too small to be observed by all GPS stations. Our study shows that tectonic\ntremor may reflect stress variation during the pre-nucleation process of a nearby earthquake.","language":"English","publisher":"Wiley","doi":"10.1002/2016JB013925","usgsCitation":"Chao, K., Peng, Z., Hsu, Y., Obara, K., Ching, K., Wu, C., Pu, H., Leu, P., and Wech, A., 2018, Temporal variation of tectonic tremor activity in southern Taiwan around the 2010 ML6.4 Jiashian Earthquake: Journal of Geophysical Research B: Solid Earth, v. 122, no. 7, p. 5417-5434, https://doi.org/10.1002/2016JB013925.","productDescription":"18 p.","startPage":"5417","endPage":"5434","ipdsId":"IP-082546","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469195,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jb013925","text":"Publisher Index Page"},{"id":351812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Taiwan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 120.234375,\n 21.94304553343818\n ],\n [\n 120.58593749999999,\n 21.453068633086783\n ],\n [\n 121.640625,\n 21.94304553343818\n ],\n [\n 121.9921875,\n 23.40276490540795\n ],\n [\n 122.51953124999999,\n 24.206889622398023\n ],\n [\n 122.51953124999999,\n 25.799891182088334\n ],\n [\n 121.640625,\n 25.958044673317843\n ],\n [\n 120.9375,\n 25.3241665257384\n ],\n [\n 119.88281249999999,\n 24.686952411999155\n ],\n [\n 119.35546875000001,\n 23.563987128451217\n ],\n [\n 120.234375,\n 21.94304553343818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-22","publicationStatus":"PW","scienceBaseUri":"5afee787e4b0da30c1bfc2b4","contributors":{"authors":[{"text":"Chao, Kevin","contributorId":202562,"corporation":false,"usgs":false,"family":"Chao","given":"Kevin","email":"","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":728910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peng, Zhigang","contributorId":69432,"corporation":false,"usgs":true,"family":"Peng","given":"Zhigang","affiliations":[],"preferred":false,"id":728911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsu, Ya-Ju","contributorId":202563,"corporation":false,"usgs":false,"family":"Hsu","given":"Ya-Ju","email":"","affiliations":[{"id":36479,"text":"Academia Sinica, Taipei, Taiwan","active":true,"usgs":false}],"preferred":false,"id":728912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Obara, Kazushige","contributorId":202564,"corporation":false,"usgs":false,"family":"Obara","given":"Kazushige","email":"","affiliations":[{"id":7267,"text":"University of Tokyo","active":true,"usgs":false}],"preferred":false,"id":728913,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ching, Kuo-En","contributorId":202565,"corporation":false,"usgs":false,"family":"Ching","given":"Kuo-En","email":"","affiliations":[{"id":36480,"text":"National Cheng Kung University","active":true,"usgs":false}],"preferred":false,"id":728914,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Chunquan","contributorId":46871,"corporation":false,"usgs":true,"family":"Wu","given":"Chunquan","email":"","affiliations":[],"preferred":false,"id":728915,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pu, Hsin-Chieh","contributorId":202567,"corporation":false,"usgs":false,"family":"Pu","given":"Hsin-Chieh","email":"","affiliations":[{"id":36481,"text":"Central Weather Bureau, Taipei, Taiwan","active":true,"usgs":false}],"preferred":false,"id":728916,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leu, Peih-Lin","contributorId":202568,"corporation":false,"usgs":false,"family":"Leu","given":"Peih-Lin","email":"","affiliations":[{"id":36481,"text":"Central Weather Bureau, Taipei, Taiwan","active":true,"usgs":false}],"preferred":false,"id":728917,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":728909,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70190589,"text":"70190589 - 2018 - The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","interactions":[],"lastModifiedDate":"2018-03-29T12:51:13","indexId":"70190589","displayToPublicDate":"2017-06-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","docAbstract":"

The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.

","language":"English","publisher":"Springer","doi":"10.1007/s11273-017-9554-y","usgsCitation":"Vanderhoof, M.K., Distler, H., Lang, M.W., and Alexander, L.C., 2018, The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware: Wetlands Ecology and Management, v. 26, no. 1, p. 63-86, https://doi.org/10.1007/s11273-017-9554-y.","productDescription":"24 p.","startPage":"63","endPage":"86","ipdsId":"IP-084257","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469198,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9534041","text":"External Repository"},{"id":438088,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4T8F","text":"USGS data release","linkHelpText":"Data Release for the influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware"},{"id":352120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.1,\n 38.5\n ],\n [\n -76.1,\n 39.1\n ],\n [\n -75.5,\n 39.1\n ],\n [\n -75.5,\n 38.5\n ],\n [\n -76.1,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-08","publicationStatus":"PW","scienceBaseUri":"5afee787e4b0da30c1bfc2b6","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Distler, Hayley 0000-0001-5006-1360 hdistler@usgs.gov","orcid":"https://orcid.org/0000-0001-5006-1360","contributorId":179359,"corporation":false,"usgs":true,"family":"Distler","given":"Hayley","email":"hdistler@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":709919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Laurie C.","contributorId":196285,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709920,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188018,"text":"70188018 - 2018 - Evidence of sound production by spawning lake trout (Salvelinus namaycush) in lakes Huron and Champlain","interactions":[],"lastModifiedDate":"2018-02-22T12:39:29","indexId":"70188018","displayToPublicDate":"2017-05-26T00:00:00","publicationYear":"2018","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}},"displayTitle":"Evidence of sound production by spawning lake trout (Salvelinus namaycush) in lakes Huron and Champlain","title":"Evidence of sound production by spawning lake trout (Salvelinus namaycush) in lakes Huron and Champlain","docAbstract":"

Two sounds associated with spawning lake trout (Salvelinus namaycush) in lakes Huron and Champlain were characterized by comparing sound recordings to behavioral data collected using acoustic telemetry and video. These sounds were named growls and snaps, and were heard on lake trout spawning reefs, but not on a non-spawning reef, and were more common at night than during the day. Growls also occurred more often during the spawning period than the pre-spawning period, while the trend for snaps was reversed. In a laboratory flume, sounds occurred when male lake trout were displaying spawning behaviors; growls when males were quivering and parallel swimming, and snaps when males moved their jaw. Combining our results with the observation of possible sound production by spawning splake (Salvelinus fontinalis × Salvelinus namaycush hybrid), provides rare evidence for spawning-related sound production by a salmonid, or any other fish in the superorder Protacanthopterygii. Further characterization of these sounds could be useful for lake trout assessment, restoration, and control.

","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2016-0511","usgsCitation":"Johnson, N.S., Higgs, D., Binder, T., Marsden, J., Buchinger, T.J., Brege, L., Bruning, T., Farha, S., and Krueger, C., 2018, Evidence of sound production by spawning lake trout (Salvelinus namaycush) in lakes Huron and Champlain: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 3, p. 429-438, https://doi.org/10.1139/cjfas-2016-0511.","productDescription":"10 p.","startPage":"429","endPage":"438","ipdsId":"IP-085673","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":501380,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/79579","text":"External Repository"},{"id":341809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Drummond Island Lake Trout Refuge, Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.692,\n 45.941\n ],\n [\n -83.619,\n 45.941\n ],\n [\n -83.619,\n 45.899\n ],\n [\n -83.692,\n 45.899\n ],\n [\n -83.692,\n 45.941\n \n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59293e8ee4b016f7a94076d0","contributors":{"authors":[{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":696196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Higgs, Dennis","contributorId":192314,"corporation":false,"usgs":false,"family":"Higgs","given":"Dennis","affiliations":[],"preferred":false,"id":696197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Thomas R.","contributorId":23056,"corporation":false,"usgs":false,"family":"Binder","given":"Thomas R.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":696198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsden, J. Ellen","contributorId":10367,"corporation":false,"usgs":true,"family":"Marsden","given":"J. Ellen","affiliations":[],"preferred":false,"id":696199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buchinger, Tyler John","contributorId":192316,"corporation":false,"usgs":false,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"John","affiliations":[],"preferred":false,"id":696200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brege, Linnea 0000-0002-7495-3619 lbrege@usgs.gov","orcid":"https://orcid.org/0000-0002-7495-3619","contributorId":176976,"corporation":false,"usgs":true,"family":"Brege","given":"Linnea","email":"lbrege@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":696201,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bruning, Tyler 0000-0002-5970-9810 tbruning@usgs.gov","orcid":"https://orcid.org/0000-0002-5970-9810","contributorId":173134,"corporation":false,"usgs":true,"family":"Bruning","given":"Tyler","email":"tbruning@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":696202,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Farha, Steve A. 0000-0001-9953-6996 sfarha@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-6996","contributorId":5170,"corporation":false,"usgs":true,"family":"Farha","given":"Steve A.","email":"sfarha@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":696203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":696204,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70187603,"text":"gip176 - 2018 - Groundwater and streamflow information program Kansas Cooperative Water Science since 1895","interactions":[],"lastModifiedDate":"2021-05-26T11:42:14.849368","indexId":"gip176","displayToPublicDate":"2017-05-10T13:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"176","displayTitle":"Groundwater and Streamflow Information Program Kansas Cooperative Water Science since 1895","title":"Groundwater and streamflow information program Kansas Cooperative Water Science since 1895","docAbstract":"

The U.S. Geological Survey, in cooperation with State, local, and other Federal agencies, operates a network of streamgages throughout the State of Kansas. Data provided by this network are used to forecast floods, operate reservoirs, develop water policy, administer regulation of water, and perform interpretive analyses of streamflow. This data collection and analysis effort has been sustained since 1895 through cooperative matching fund programs that allow the USGS to work with cooperative agencies to solve groundwater and surface water challenges that affect citizens locally and throughout the Nation.  

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Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049

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The U.S. Geological Survey (USGS) is a non-regulatory Earth science agency within the Department of the Interior that provides impartial scientific information to describe and understand the health of our ecosystems and environment; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect our quality of life. The USGS cooperates with Federal, State, tribal, and local agencies in Kansas to deliver long-term data in real-time and interpretive reports describing what those data mean to the public and resource management agencies. USGS science programs in Kansas provide real-time groundwater monitoring at more than 19 locations; streamflow monitoring at more than 216 locations; water-quality and trends in the Little Arkansas and Kansas Rivers; inflows and outflows of sediment to/from reservoirs and in streams; harmful algal bloom research in the Kansas River, Milford Lake, and Cheney Reservoir; water-quantity and water-quality effects of artificial groundwater recharge for the Equus Beds Aquifer Storage and Recovery project near Wichita, Kansas; compilation of Kansas municipal and irrigation water-use data statewide; the occurrence, effects, and movement of environmental pesticides, antibiotics, algal toxins, and taste-and-odor compounds; and funding to the Kansas Water Resources Research Institute to further research and education through Kansas universities.

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Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049

","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2017-05-08","revisedDate":"2022-12-12","noUsgsAuthors":false,"publicationDate":"2017-05-08","publicationStatus":"PW","scienceBaseUri":"591183aee4b0e541a03c1a42","contributors":{"authors":[{"text":"Kramer, Ariele R. 0000-0002-7075-3310 akramer@usgs.gov","orcid":"https://orcid.org/0000-0002-7075-3310","contributorId":185245,"corporation":false,"usgs":true,"family":"Kramer","given":"Ariele","email":"akramer@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":694544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Brian P. 0000-0001-6378-2837 bkelly@usgs.gov","orcid":"https://orcid.org/0000-0001-6378-2837","contributorId":897,"corporation":false,"usgs":true,"family":"Kelly","given":"Brian","email":"bkelly@usgs.gov","middleInitial":"P.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":694543,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220880,"text":"70220880 - 2018 - Global trends in mineral commodities for advanced technologies","interactions":[],"lastModifiedDate":"2021-05-28T20:06:55.57801","indexId":"70220880","displayToPublicDate":"2017-05-03T08:33:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Global trends in mineral commodities for advanced technologies","docAbstract":"

The U.S. Geological Survey National Minerals Information Center (NMIC) is the U.S. Government agency tasked with the collection, analysis, and dissemination of information on the production, consumption, import, export, and other measures of the flows of non-fuel mineral commodities of importance to the U.S. economy and national security. The NMIC and its agency predecessors have maintained a database of this information, collected and published annually, dating back to the beginning of the twentieth century. Time series analysis of annual information from the NMIC provides the opportunity to identify trends in the supply chains of the minerals and metals which are increasingly in demand for advanced technologies. The identification of trends in data for net import reliance, country concentration of production, global demand, price volatility, and other measures, when combined with world governance indicators, can be used to focus attention on individual mineral commodities where supply chain restrictions may develop. Specific examples for U.S. net import reliance, global tantalum primary mining, and mineral criticality screening are presented to illustrate the utility of time series analysis of trends in mineral commodity supply and demand, the types of data required, and the limitations of currently available information.

","language":"English","publisher":"Springer","doi":"10.1007/s11053-017-9340-9","usgsCitation":"Fortier, S.M., Thomas, C.L., McCullough, E.A., and Tolcin, A., 2018, Global trends in mineral commodities for advanced technologies: Natural Resources Research, v. 27, p. 191-200, https://doi.org/10.1007/s11053-017-9340-9.","productDescription":"10 p.","startPage":"191","endPage":"200","ipdsId":"IP-086414","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":386023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","noUsgsAuthors":false,"publicationDate":"2017-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fortier, Steven M. 0000-0001-8123-5749","orcid":"https://orcid.org/0000-0001-8123-5749","contributorId":202406,"corporation":false,"usgs":true,"family":"Fortier","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":816551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Christine Lyn 0000-0002-1391-6072","orcid":"https://orcid.org/0000-0002-1391-6072","contributorId":258823,"corporation":false,"usgs":true,"family":"Thomas","given":"Christine","email":"","middleInitial":"Lyn","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":816552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCullough, Erin A. 0000-0002-9072-7021 emccullough@usgs.gov","orcid":"https://orcid.org/0000-0002-9072-7021","contributorId":196629,"corporation":false,"usgs":true,"family":"McCullough","given":"Erin","email":"emccullough@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":816553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tolcin, Amy 0000-0001-9447-2444 atolcin@usgs.gov","orcid":"https://orcid.org/0000-0001-9447-2444","contributorId":213768,"corporation":false,"usgs":true,"family":"Tolcin","given":"Amy","email":"atolcin@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":816554,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216316,"text":"70216316 - 2018 - Interoperability in planetary research for geospatial data analysis","interactions":[],"lastModifiedDate":"2020-11-11T15:52:00.197038","indexId":"70216316","displayToPublicDate":"2017-04-13T09:43:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3083,"text":"Planetary and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Interoperability in planetary research for geospatial data analysis","docAbstract":"

For more than a decade there has been a push in the planetary science community to support interoperable methods for accessing and working with geospatial data. Common geospatial data products for planetary research include image mosaics, digital elevation or terrain models, geologic maps, geographic location databases (e.g., craters, volcanoes) or any data that can be tied to the surface of a planetary body (including moons, comets or asteroids). Several U.S. and international cartographic research institutions have converged on mapping standards that embrace standardized geospatial image formats, geologic mapping conventions, U.S. Federal Geographic Data Committee (FGDC) cartographic and metadata standards, and notably on-line mapping services as defined by the Open Geospatial Consortium (OGC). The latter includes defined standards such as the OGC Web Mapping Services (simple image maps), Web Map Tile Services (cached image tiles), Web Feature Services (feature streaming), Web Coverage Services (rich scientific data streaming), and Catalog Services for the Web (data searching and discoverability). While these standards were developed for application to Earth-based data, they can be just as valuable for planetary domain. Another initiative, called VESPA (Virtual European Solar and Planetary Access), will marry several of the above geoscience standards and astronomy-based standards as defined by International Virtual Observatory Alliance (IVOA). This work outlines the current state of interoperability initiatives in use or in the process of being researched within the planetary geospatial community.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.pss.2017.04.004","usgsCitation":"Hare, T.M., Rossi, A.P., Frigeri, A., and Marmo, C., 2018, Interoperability in planetary research for geospatial data analysis: Planetary and Space Science, v. 150, p. 36-42, https://doi.org/10.1016/j.pss.2017.04.004.","productDescription":"7 p.","startPage":"36","endPage":"42","ipdsId":"IP-081464","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1706.02683","text":"External Repository"},{"id":380419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":804662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rossi, Angelo P. 0000-0002-0137-1984","orcid":"https://orcid.org/0000-0002-0137-1984","contributorId":244811,"corporation":false,"usgs":false,"family":"Rossi","given":"Angelo","email":"","middleInitial":"P.","affiliations":[{"id":48986,"text":"Jacobs University Bremen","active":true,"usgs":false}],"preferred":false,"id":804666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frigeri, Alessandro 0000-0002-9140-3977","orcid":"https://orcid.org/0000-0002-9140-3977","contributorId":244812,"corporation":false,"usgs":false,"family":"Frigeri","given":"Alessandro","email":"","affiliations":[{"id":48987,"text":"National Institute for Astrophysics, Institute for Space Astrophysics and Planetology","active":true,"usgs":false}],"preferred":false,"id":804667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marmo, Chiara","contributorId":244813,"corporation":false,"usgs":false,"family":"Marmo","given":"Chiara","email":"","affiliations":[{"id":48988,"text":"Laboratory of Interactions and Dynamics of Surface Environments, University Paris-Sud","active":true,"usgs":false}],"preferred":false,"id":804668,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177030,"text":"70177030 - 2018 - The origin of shallow lakes in the Khorezm Province, Uzbekistan, and the history of pesticide use around these lakes","interactions":[],"lastModifiedDate":"2018-01-24T16:03:12","indexId":"70177030","displayToPublicDate":"2016-10-06T10:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2411,"text":"Journal of Paleolimnology","active":true,"publicationSubtype":{"id":10}},"title":"The origin of shallow lakes in the Khorezm Province, Uzbekistan, and the history of pesticide use around these lakes","docAbstract":"

The economy of the Khorezm Province in Uzbekistan relies on the large-scale agricultural production of cotton. To sustain their staple crop, water from the Amu Darya is diverted for irrigation through canal systems constructed during the early to mid-twentieth century when this region was part of the Soviet Union. These diversions severely reduce river flow to the Aral Sea. The Province has >400 small shallow (<3 m deep) lakes that may have originated because of this intensive irrigation. Sediment cores were collected from 12 lakes to elucidate their origin because this knowledge is critical to understanding water use in Khorezm. Core chronological data indicate that the majority of the lakes investigated are less than 150 years old, which supports a recent origin of the lakes. The thickness of lacustrine sediments in the cores analyzed ranged from 20 to 60 cm in all but two of the lakes, indicating a relatively slow sedimentation rate and a relatively short-term history for the lakes. Hydrologic changes in the lakes are evident from loss on ignition and pollen analyses of a subset of the lake cores. The data indicate that the lakes have transitioned from a dry, saline, arid landscape during pre-lake conditions (low organic carbon content) and low pollen concentrations (in the basal sediments) to the current freshwater lakes (high organic content), with abundant freshwater pollen taxa over the last 50–70 years. Sediments at the base of the cores contain pollen taxa dominated by Chenopodiaceae and Tamarix, indicating that the vegetation growing nearby was tolerant to arid saline conditions. The near surface sediments of the cores are dominated by Typha/Sparganium, which indicate freshwater conditions. Increases in pollen of weeds and crop plants indicate an intensification of agricultural activities since the 1950s in the watersheds of the lakes analyzed. Pesticide profiles of DDT (dichlorodiphenyltrichloroethane) and its degradates and γ-HCH (gamma-hexachlorocyclohexane), which were used during the Soviet era, show peak concentrations in the top 10 cm of some of the cores, where estimated ages of the sediments (1950–1990) are associated with peak pesticide use during the Soviet era. These data indicate that the lakes are relatively young (mostly <150 years old) and that without irrigation and canal inputs from the Amu Darya, the lakes would not exist as freshwater lakes.

","language":"English","publisher":"Springer International Publishing","doi":"10.1007/s10933-016-9914-2","usgsCitation":"Rosen, M.R., Crootof, A., Reidy, L., Saito, L., Nishonov, B., and Scott, J.A., 2018, The origin of shallow lakes in the Khorezm Province, Uzbekistan, and the history of pesticide use around these lakes: Journal of Paleolimnology, v. 59, no. 2, p. 201-219, https://doi.org/10.1007/s10933-016-9914-2.","productDescription":"19 p.","startPage":"201","endPage":"219","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073927","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":438095,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319T07","text":"USGS data release","linkHelpText":"Datasets for determining the origin of shallow lakes in the Khorezm Province, Uzbekistan, and the history of pesticide use around these 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The “National Field Manual for the Collection of Water-Quality Data” (NFM) is an online report with separately published chapters that provides the protocols and guidelines by which U.S. Geological Survey personnel obtain the data used to assess the quality of the Nation’s surface-water and groundwater resources. Chapter A10 reviews limnological principles, describes the characteristics that distinguish lakes from reservoirs, and provides guidance for developing temporal and spatial sampling strategies and data-collection approaches to be used in lake and reservoir environmental investigations.

Within this chapter are references to other chapters of the NFM that provide more detailed guidelines related to specific topics and more detailed protocols for the quality assurance and assessment of the lake and reservoir data. Protocols and procedures to address and document the quality of lake and reservoir investigations are adapted from, or referenced to, the protocols and standard operating procedures contained in related chapters of this NFM.

Before 2017, the U.S. Geological Survey (USGS) “National Field Manual for the Collection of Water-Quality Data” (NFM) chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0, 2018) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm-owq@usgs.gov.

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Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2015-09-29","revisedDate":"2018-05-31","noUsgsAuthors":false,"publicationDate":"2015-09-29","publicationStatus":"PW","scienceBaseUri":"5638974be4b0d6133fe72fa2","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128037,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":566893,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70094981,"text":"ofr20141040 - 2018 - Polygons of global undersea features for geographic searches","interactions":[],"lastModifiedDate":"2018-06-25T11:08:55","indexId":"ofr20141040","displayToPublicDate":"2014-03-24T08:59:00","publicationYear":"2018","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":"2014-1040","title":"Polygons of global undersea features for geographic searches","docAbstract":"

A shapefile of 311 undersea features from all major oceans and seas has been created as an aid for retrieving georeferenced information resources. Geospatial information systems with the capability to search user-defined, polygonal geographic areas will be able to utilize this shapefile or secondary products derived from it, such as linked data based on well-known text representations of the individual polygons within the shapefile. Version 1.1 of this report also includes a linked data representation of 299 of these features and their spatial extents.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141040","usgsCitation":"Hartwell, S., Wingfield, D.K., Allwardt, A., Lightsom, F.L., and Wong, F.L., 2018, Polygons of global undersea features for geographic searches (Version 1.1: June 2018; Version 1.0: March 2014): U.S. Geological Survey Open-File Report 2014-1040, HTML, https://doi.org/10.3133/ofr20141040.","productDescription":"HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053850","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":284379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141040.jpg"},{"id":284377,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1040/","text":"Index page","linkFileType":{"id":5,"text":"html"},"description":"OFR 2014-1040"},{"id":354598,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2014/1040/versionHist.txt","size":"1.75 KB","linkFileType":{"id":2,"text":"txt"}},{"id":284378,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1040/ofr2014-1040-title_page.html","text":"Report (HTML)"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -180.0,-90.0 ], [ -180.0,90.0 ], [ 180.0,90.0 ], [ 180.0,-90.0 ], [ -180.0,-90.0 ] ] ] } } ] }","edition":"Version 1.1: June 2018; Version 1.0: March 2014","contact":"

Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543

","tableOfContents":"","revisedDate":"2018-06-25","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd6bc2e4b0b29085104210","contributors":{"authors":[{"text":"Hartwell, Stephen R. shartwell@usgs.gov","contributorId":140879,"corporation":false,"usgs":true,"family":"Hartwell","given":"Stephen R.","email":"shartwell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":491021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wingfield, Dana K.","contributorId":40683,"corporation":false,"usgs":true,"family":"Wingfield","given":"Dana","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":491022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allwardt, Alan O.","contributorId":22051,"corporation":false,"usgs":true,"family":"Allwardt","given":"Alan O.","affiliations":[],"preferred":false,"id":491020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lightsom, Frances L. 0000-0003-4043-3639 flightsom@usgs.gov","orcid":"https://orcid.org/0000-0003-4043-3639","contributorId":1535,"corporation":false,"usgs":true,"family":"Lightsom","given":"Frances","email":"flightsom@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong, Florence L. 0000-0002-3918-5896 fwong@usgs.gov","orcid":"https://orcid.org/0000-0002-3918-5896","contributorId":1990,"corporation":false,"usgs":true,"family":"Wong","given":"Florence","email":"fwong@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491019,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70041721,"text":"ds729 - 2018 - Fatality estimator user’s guide","interactions":[],"lastModifiedDate":"2018-12-11T15:56:56","indexId":"ds729","displayToPublicDate":"2012-12-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"729","displayTitle":"Fatality Estimator User’s Guide","title":"Fatality estimator user’s guide","docAbstract":"

This publication is the User's Guide for software developed to estimate wildlife fatalities at wind-power facilities, although the software is applicable to a variety of circumstances in which the objective is to estimate the size of a superpopulation and the probability of detection of the individuals is less than one. Simple counts of carcasses do not accurately reflect fatality and do not allow comparison among locations because carcasses may be detected at different rates. This software uses data collected during carcass searches and knowledge of detection rates to accurately estimate the number of fatalities and to provide a measure of precision associated with the estimate. These estimates are fundamental to understanding acute and cumulative effects of wind power on wildlife populations.

Only carcasses judged to have been killed after the previous search should be included in the fatality data set submitted to this estimator software. This estimator already corrects for carcasses missed in previous searches, so carcasses judged to have been missed at least once should be considered “incidental” and not included in the fatality data set used to estimate fatality. Note: When observed carcass count is <5 (including 0 for species known to be at risk, but not observed), USGS Data Series 881 (https://pubs.usgs.gov/ds/0881/) is recommended for fatality estimation.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds729","usgsCitation":"Huso, Manuela, Som, Nicholas, and Ladd, Lew, 2018, Fatality estimator user’s guide (ver. 1.2, December 2018): U.S. Geological Survey Data Series 729, 22 p., https://doi.org/10.3133/ds729.","productDescription":"Report: vi, 22 p.; Estimator Software ZIP download","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":311790,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/729/InputPlaceholderV1.1.zip","text":"Input Placeholder 1.1","size":"10 KB","linkFileType":{"id":6,"text":"zip"}},{"id":263935,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/729/pdf/ds729.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":263936,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/729/ds729_EstimatorSetup.zip","text":"Estimator Software","size":"250 KB","linkFileType":{"id":6,"text":"zip"}},{"id":263934,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/729/","text":"Index Page","linkFileType":{"id":5,"text":"html"}},{"id":311792,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/729/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":311791,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/729/ds729_readme.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":360159,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/729/images/coverthb.jpg"}],"edition":"Version 1: Originally posted December 2012; Version 1.1: December 2015; Version 1.2: December 2018","contact":"

Director, Forest and Rangeland Ecosystem Science Center,
U.S. Geological Survey, 777 NW 9th Street
Corvallis, Oregon 97330
http://fresc.usgs.gov 

","tableOfContents":"","publishedDate":"2012-12-11","revisedDate":"2018-12-10","noUsgsAuthors":false,"publicationDate":"2012-12-11","publicationStatus":"PW","scienceBaseUri":"50c8560ce4b03bc63bd6799e","contributors":{"authors":[{"text":"Huso, Manuela M. 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":150012,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":470106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas","contributorId":100264,"corporation":false,"usgs":true,"family":"Som","given":"Nicholas","affiliations":[],"preferred":false,"id":470107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladd, Lew","contributorId":108371,"corporation":false,"usgs":true,"family":"Ladd","given":"Lew","email":"","affiliations":[],"preferred":false,"id":470108,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}