Question
Are self‐organizing maps (SOMs) useful for patterning coastal wetland vegetation communities? Do SOMs provide robust alternatives to traditional classification methods, particularly when underlying species response functions are unknown or difficult to approximate, or when a need exists to continuously classify new samples obtained under ongoing long‐term ecosystem monitoring programs as they become available?.
Location
Coastal Louisiana, USA.
Methods
A SOM was trained from in situ observations of 559 vegetation species relative cover data from 2526 samples collected over eight years at 343 locations across coastal Louisiana. Hierarchical cluster analysis was applied to the SOM output to delineate vegetation community types, and indicator species analysis was conducted. Salinity and flood duration were compared across the delineated community types.
Results
The SOM patterned the 2526 training samples into 260 output neurons, which were further clustered into eleven community types. Clear gradients in salinity and flood duration existed among the community types, and geographic zonation of the communities was evident across the landscape. At some locations assemblages were temporally stable; at other locations they varied considerably. Samples not used in training the network were effectively projected onto the SOM and assigned to one of the delineated community types.
Conclusions
The SOM was effective in delineating plant communities in the region that were qualitatively similar to those obtained in previous investigations. Being robust to skewed distributions and the presence of outliers, SOMs provide an alternative to traditional distribution‐based statistical approaches. Their ability to efficiently classify new data into existing community types makes their use an ideal approach to classifying samples obtained from ongoing, long‐term ecological monitoring programs.
Birds are essential components of most ecosystems and provide many services valued by society. However, many populations have undergone striking declines as their habitats have been lost or degraded by human activities. Terrestrial grasslands are vital habitat for birds in the North American Prairie Pothole Region (PPR), but grassland conversion and fragmentation from agriculture and energy-production activities have destroyed or degraded millions of hectares. Conservation grasslands can provide alternate habitat. In the United States, the Conservation Reserve Program (CRP) is the largest program maintaining conservation grasslands on agricultural lands, but conservation grasslands in the PPR have declined by over 1 million ha since the program’s zenith in 2007. We used an ecosystem-services model (InVEST) parameterized for the PPR to quantify grassland-bird habitat remaining in 2014 and to assess the degradation status of the remaining grassland-bird habitat as influenced by crop and energy (i.e., oil, natural gas, and wind) production. We compared our resultant habitat-quality ratings to grassland-bird abundance data from the North American Breeding Bird Survey to confirm that ratings were related to grassland-bird abundance. Of the grassland-bird habitat remaining in 2014, about 19% was degraded by crop production that occurred within 0.1 km of grassland habitats, whereas energy production degraded an additional 16%. We further quantified the changes in availability of grassland-bird habitat under various land-cover scenarios representing incremental losses (10%, 25%, 50%, 75%, and 100%) of CRP grasslands from 2014 levels. Our model identified 1 million ha (9%) of remaining grassland-bird habitat in the PPR that would be lost or degraded if all CRP conservation grasslands were returned to crop production. Grassland regions world-wide face similar challenges in maintaining avian habitat in the face of increasing commodity and energy production to sate the food and energy needs of a growing world population. Identifying ways to model the impacts of the tradeoff between food and energy production and wildlife production is an important step in creating solutions.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0198382","usgsCitation":"Shaffer, J.A., Roth, C.L., and Mushet, D.M., 2019, Modeling effects of crop production, energy development and conservation-grassland loss on avian habitat: PLoS ONE, v. 14, no. 1, p. 1-17, https://doi.org/10.1371/journal.pone.0198382.","productDescription":"e0198382; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-089961","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468001,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0198382","text":"Publisher Index Page"},{"id":437609,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72J69RM","text":"USGS data release","linkHelpText":"Modeling effects of crop production, energy development and conservation-grassland loss on avian habitat: dataset of BBS data, ND, with habitat rankings"},{"id":360786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Region","volume":"14","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":755277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roth, Cali L. 0000-0001-9077-2765 croth@usgs.gov","orcid":"https://orcid.org/0000-0001-9077-2765","contributorId":174422,"corporation":false,"usgs":true,"family":"Roth","given":"Cali","email":"croth@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":755278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":755279,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227779,"text":"70227779 - 2019 - A seascape-scale habitat model to support management of fishing impacts on benthic ecosystems","interactions":[],"lastModifiedDate":"2022-01-31T14:52:59.91393","indexId":"70227779","displayToPublicDate":"2019-01-09T08:48:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A seascape-scale habitat model to support management of fishing impacts on benthic ecosystems","docAbstract":"Minimizing fishing impacts on seafloor ecosystems is a growing focus of ocean management; however, few quantitative tools exist to guide seascape-scale habitat management. To meet these needs, we developed a model to assess benthic ecosystem impacts from fishing gear contact. The habitat impacts model is cast in discrete time and can accommodate overlapping fisheries as well as incorporate gear-specific contact dynamics. We implemented the model in the North Pacific using fishing data from 2003 to 2017, estimating that habitat in 3.1% of the 1.2 million km2 study area was disturbed at the end of the simulation period. A marked decline in habitat disturbance was evident since 2010, attributable to a single regulatory gear change that lifted trawl gear components off the seafloor. Running scenarios without these gear modifications showed these policies might have contributed to a 24% reduction in habitat disturbance since their implementation. Ultimately, model outputs provide direct estimates of the spatial and temporal trends of habitat effects from fishing — a key component of regulatory policies for many of the world’s fisheries.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0243","usgsCitation":"Smeltz, T.S., Harris, B., Olson, J., and Sethi, S., 2019, A seascape-scale habitat model to support management of fishing impacts on benthic ecosystems: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 10, p. 1836-1844, https://doi.org/10.1139/cjfas-2018-0243.","productDescription":"9 p.","startPage":"1836","endPage":"1844","ipdsId":"IP-092665","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468002,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2018-0243","text":"External Repository"},{"id":395132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smeltz, T. Scott","contributorId":272598,"corporation":false,"usgs":false,"family":"Smeltz","given":"T.","email":"","middleInitial":"Scott","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":832207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Bradley","contributorId":272599,"corporation":false,"usgs":false,"family":"Harris","given":"Bradley","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":832208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olson, John","contributorId":272600,"corporation":false,"usgs":false,"family":"Olson","given":"John","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":832209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832206,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215987,"text":"70215987 - 2019 - Improving estimates and forecasts of lake carbon dynamics using data assimilation","interactions":[],"lastModifiedDate":"2020-11-03T14:07:26.459331","indexId":"70215987","displayToPublicDate":"2019-01-09T08:04:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2622,"text":"Limnology and Oceanography: Methods","active":true,"publicationSubtype":{"id":10}},"title":"Improving estimates and forecasts of lake carbon dynamics using data assimilation","docAbstract":"Lakes are biogeochemical hotspots on the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. Observations and models of lake carbon pools and fluxes are rarely explicitly combined through data assimilation despite successful use of this technique in other fields. Data assimilation adds value to both observations and models by constraining models with observations of the system and by leveraging knowledge of the system formalized by the model to objectively fill observation gaps. In this article, we highlight the utility of data assimilation in lake carbon cycling research by using the ensemble Kalman filter to combine simple lake carbon models with observations of lake carbon pools and fluxes. We demonstrate that data assimilation helps reduce uncertainty in estimates of lake carbon pools and fluxes and more accurately estimate the true carbon pool size compared to estimates derived from observations alone. Data assimilation techniques should be embraced as valuable tools for lake biogeochemists interested in learning about ecosystem dynamics and forecasting ecosystem states and processes.
Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fpls.2018.01964","usgsCitation":"Cailleret, M., Dakos, V., Jansen, S., Robert, E., Aakala, T., Amoroso, M.M., Antos, J., Bigler, C., Bugmann, H., Caccianaga, M., Camarero, J., Cherubini, P., Coyea, M.R., Cufar, K., Das, A., Davi, H., Gea-Izquierdo, G., Gillner, S., Haavik, L.J., Hartmann, H., Heres, A., Hultine, K.R., Janda, P., Kane, J.M., Kharuk, V.I., Kitzberger, T., Klein, T., Levanic, T., Linares, J., Lombardi, F., Makinen, H., Mészáros, I., Metsaranta, J.M., Oberhuber, W., Papadopoulos, A., Petritan, A.M., Rohner, B., Sanguesa-Barreda, G., Smith, J.M., Stan, A.B., Stojanovic, D.B., Suarez, M., Svoboda, M., Trotsiuk, V., Villalba, R., Westwood, A.R., Wyckoff, P.H., and Martínez-Vilalta, J., 2019, Early-warning signals of individual tree mortality based on annual radial growth: Frontiers in Plant Science, v. 9, p. 1-14, https://doi.org/10.3389/fpls.2018.01964.","productDescription":"Article 1964; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-101375","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fpls.2018.01964","text":"Publisher Index Page"},{"id":360792,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Cailleret, Maxime 0000-0001-6561-1943","orcid":"https://orcid.org/0000-0001-6561-1943","contributorId":181952,"corporation":false,"usgs":false,"family":"Cailleret","given":"Maxime","email":"","affiliations":[],"preferred":false,"id":755204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dakos, Vasilis","contributorId":198880,"corporation":false,"usgs":false,"family":"Dakos","given":"Vasilis","email":"","affiliations":[],"preferred":false,"id":755205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jansen, Steven","contributorId":181953,"corporation":false,"usgs":false,"family":"Jansen","given":"Steven","email":"","affiliations":[],"preferred":false,"id":755206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robert, Elisabeth M. 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The aim of this study was to investigate the association between short-term exposure to ambient air pollutants and CVD (cardiovascular disease) events in a long-term observational period. The study included the events of cardiovascular diseases (namely coronary artery disease, ischemic heart disease, myocardial infarction, and pneumo thrombo embolism) within the population of Shiraz, from March 21, 2009 to March 20, 2015. Also, each patient’s demographics were recorded. Main meteorological variables and five ambient pollutants (CO, O3, SO2, NO2, and PM10) were recorded. Statistical analysis was performed using linear regression (GLM) and a generalized additive model (GAM) estimating Poisson distribution and adjusted for the main risk factors and ambient meteorological variables. A mild prevalence (51.5%) of coronary artery disease (CAD) was registered in 6425 events. In GLM analysis, we observed an association among the pollutants with the coronary artery disease hospital admissions which was in the order of CO, NO2, and PM10. The highest association of each pollutant with hospital admission was observed as PM10 at lag 4 (RR = 1.08; 95% CI 1.02, 1.14 and p < 0.05), NO2 at lag 0 (RR = 1.22; 95% CI 1.00, 1.48), and CO at lag 0 (RR = 1.52 95% CI = (1.16, 1.99)). However, on dusty days, there were significantly higher numbers of referrals of cardiovascular patients (mean = 7.54 ± 4.44 and p = 0.002,) than on non-dusty days. According to these data, dust storms and some types of pollutants in the air are responsible for more admissions to hospitals for cardiovascular problems.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-018-3952-4","usgsCitation":"Soleimani, Z., Boloorani, A.D., Khalifeh, R., Griffin, D.W., and Mesdaghinia, A., 2019, Short-term effects of ambient air pollution and cardiovascular events in Shiraz, Iran, 2009 to 2015: Environmental Science and Pollution Research, v. 26, no. 7, p. 6359-6367, https://doi.org/10.1007/s11356-018-3952-4.","productDescription":"9 p.","startPage":"6359","endPage":"6367","ipdsId":"IP-097671","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":367056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","city":"Shiraz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 52.397918701171875,\n 29.476067910657175\n ],\n [\n 52.6959228515625,\n 29.476067910657175\n ],\n [\n 52.6959228515625,\n 29.742916942840562\n ],\n [\n 52.397918701171875,\n 29.742916942840562\n ],\n [\n 52.397918701171875,\n 29.476067910657175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Soleimani, Zahra","contributorId":218616,"corporation":false,"usgs":false,"family":"Soleimani","given":"Zahra","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boloorani, Ali Darvishi","contributorId":218617,"corporation":false,"usgs":false,"family":"Boloorani","given":"Ali","email":"","middleInitial":"Darvishi","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khalifeh, Reza","contributorId":218618,"corporation":false,"usgs":false,"family":"Khalifeh","given":"Reza","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mesdaghinia, Alireza","contributorId":218619,"corporation":false,"usgs":false,"family":"Mesdaghinia","given":"Alireza","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769649,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204053,"text":"70204053 - 2019 - A comparison of age- and size-structured assessment models applied to a stock of cisco in Thunder Bay, Ontario","interactions":[],"lastModifiedDate":"2025-02-07T15:26:06.265811","indexId":"70204053","displayToPublicDate":"2019-01-04T10:06:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of age- and size-structured assessment models applied to a stock of cisco in Thunder Bay, Ontario","docAbstract":"Stock assessments are critical to modern fisheries management, supporting the calculation of key reference variables used to make informed management decisions. However, there is still considerable uncertainty as to which class of assessment models is appropriate to use under different circumstances. A common class of models used when age data are available are statistical catch-at-age assessment (SCAA) models, which track annual cohorts through time. When age data are unavailable, as is often the case in invertebrate fisheries where the lack of a bony structure such as otoliths makes aging difficult, statistical catch-at-size assessment (SCSA) models are more often employed, tracking fish or invertebrates through time by size-classes rather than ages. Do SCAA models actually perform better than SCSA models when age data are available, or is this just an assumption we make in fisheries research and management? We examined this question by evaluating the effectiveness of both SCAA and SCSA models in characterizing cisco, Coregonus artedi, population dynamics in Thunder Bay, Ontario. Both models were fit using an integrated framework with multiple sources of data including hydroacoustic estimates of spawning stock, fishery-dependent and -independent age/length compositions, and harvest data. Our results suggest that for cisco in Thunder Bay, data-limitations related to lack of size-composition data over the size range for which cisco growth is rapid resulted in difficulty estimating relative year-class strength within a SCSA. This led to parameter confounding and ultimately the inability to estimate natural mortality within a SCSA. This hampered the utility of a SCSA model in comparison with a SCAA model when age-composition data were available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2018.09.014","usgsCitation":"Fisch, N.C., Bence, J., Myers, J., Berglund, E.K., and Yule, D., 2019, A comparison of age- and size-structured assessment models applied to a stock of cisco in Thunder Bay, Ontario: Fisheries Research, v. 209, p. 86-100, https://doi.org/10.1016/j.fishres.2018.09.014.","productDescription":"15 p.","startPage":"86","endPage":"100","ipdsId":"IP-096832","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":365245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","city":"Thunder Bay","otherGeospatial":"Thunder Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.77159118652344,\n 48.57524422229134\n ],\n [\n -89.14237976074219,\n 48.48430069812584\n ],\n [\n -89.22203063964844,\n 48.43512327303003\n ],\n [\n -89.20829772949219,\n 48.30923114039641\n ],\n [\n -89.23919677734375,\n 48.307404328381544\n ],\n [\n -89.27627563476562,\n 48.2338208530875\n ],\n [\n -89.27902221679688,\n 48.204998474152255\n ],\n [\n -89.26666259765625,\n 48.193098793553624\n ],\n [\n -89.088134765625,\n 48.22284281261854\n ],\n [\n -88.934326171875,\n 48.30512072140391\n ],\n [\n -88.87115478515625,\n 48.3617240221937\n ],\n [\n -88.8409423828125,\n 48.40185599006367\n ],\n [\n -88.74206542968749,\n 48.55297816440071\n ],\n [\n -88.77159118652344,\n 48.57524422229134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"209","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fisch, Nicholas C","contributorId":216152,"corporation":false,"usgs":false,"family":"Fisch","given":"Nicholas","email":"","middleInitial":"C","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":765293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":765294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Myers, Jared T. 0009-0004-9362-8792","orcid":"https://orcid.org/0009-0004-9362-8792","contributorId":44055,"corporation":false,"usgs":false,"family":"Myers","given":"Jared T.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":765295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berglund, Eric K.","contributorId":115926,"corporation":false,"usgs":false,"family":"Berglund","given":"Eric","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":765296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yule, Daniel L. 0000-0002-0117-5115 dyule@usgs.gov","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":139532,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765297,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227788,"text":"70227788 - 2019 - Main stem and off-channel habitat use by juvenile Chinook salmon in a sub-Arctic riverscape","interactions":[],"lastModifiedDate":"2022-01-31T14:38:35.685981","indexId":"70227788","displayToPublicDate":"2019-01-04T08:28:04","publicationYear":"2019","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":"Main stem and off-channel habitat use by juvenile Chinook salmon in a sub-Arctic riverscape","docAbstract":"Mangrove forests are vulnerable to accelerated sea-level rise associated with climate warming because they occupy a relatively narrow zone on the mid-to-upper-intertidal flats. The fate of these ecosystems largely depends on their capacity to accrete sediment at a rate sufficient to maintain their elevation relative to sea level. We investigated the role of biophysical processes and feedbacks controlling surface-elevation dynamics in a fluvial sediment-rich Avicennia marina mangrove forest (New Zealand) at seasonal-to-inter-annual timescales (over 9 years) using the Rod Surface Elevation Table method. We found that sediment accretion in the forest was not measurably enhanced by episodic and short-lived storm discharges from rivers nor by elevated sea levels during storms. Critically, the coupling of frequent onshore winds and resulting resuspension of intertidal muds, with the fortnightly cycle of spring tide inundation, controlled sediment delivery and resulting accretion rates of 13 to 47 mm y−1. In turn, net surface-elevation trends of 0 to 28 mm y−1 were dominated by the physical processes of sediment accretion and shallow subsidence due to seasonal desiccation and resulting compaction of the infrequently inundated forest platform (4 to 16 mm y−1). Our data suggest that monthly and seasonal variation in tidally controlled hydroperiod and sediment delivery rather than episodic storm events are important for the maintenance of mangrove elevation within the intertidal zone.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0330-5","usgsCitation":"Andrew Swales, Reeve, G., Cahoon, D., and Catherine Lovelock, 2019, Landscape evolution of a fluvial sediment-rich Avicennia marina mangrove forest: Insights from seasonal and inter-annual surface-elevation dynamics: Ecosystems, v. 22, no. 6, p. 1232-1255, https://doi.org/10.1007/s10021-018-0330-5.","productDescription":"14 p.","startPage":"1232","endPage":"1255","ipdsId":"IP-096280","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468007,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10021-018-0330-5","text":"Publisher Index Page"},{"id":367531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Firth of Thames","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.25115966796875,\n -37.24344837865412\n ],\n [\n 175.60546875,\n -37.24344837865412\n ],\n [\n 175.60546875,\n -37.02448395075963\n ],\n [\n 175.25115966796875,\n -37.02448395075963\n ],\n [\n 175.25115966796875,\n -37.24344837865412\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Andrew Swales","contributorId":219057,"corporation":false,"usgs":false,"family":"Andrew Swales","affiliations":[{"id":17985,"text":"National Institute of Water and Atmospheric Research, Hamilton, New Zealand","active":true,"usgs":false}],"preferred":false,"id":771168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reeve, Glen","contributorId":219058,"corporation":false,"usgs":false,"family":"Reeve","given":"Glen","email":"","affiliations":[{"id":17985,"text":"National Institute of Water and Atmospheric Research, Hamilton, New Zealand","active":true,"usgs":false}],"preferred":false,"id":771169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":208039,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":771167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Catherine Lovelock","contributorId":219059,"corporation":false,"usgs":false,"family":"Catherine Lovelock","affiliations":[{"id":39954,"text":"The University of Queensland, St Lucia, Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":771170,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202013,"text":"70202013 - 2019 - Quarterly wildlife mortality report January 2019","interactions":[],"lastModifiedDate":"2023-10-12T16:51:53.461069","indexId":"70202013","displayToPublicDate":"2019-01-01T14:36:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3769,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":10}},"title":"Quarterly wildlife mortality report January 2019","docAbstract":"The USGS National Wildlife Health Center (NWHC) Quarterly Mortality Report provides brief summaries of epizootic mortality and morbidity events by quarter. The write-ups, highlighting epizootic events and other wildlife disease topics of interest, are published in the Wildlife Disease Association quarterly newsletter. A link is provided in this WDA newsletter to the Wildlife Health Information Sharing Partnership event reporting system (WHISPers) so readers can view associated data.","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Bodenstein, B., Dusek, R.J., Rocke, T.E., and Richgels, K.L., 2019, Quarterly wildlife mortality report January 2019: Wildlife Disease Association Newsletter, p. 22-23.","productDescription":"2 p.","startPage":"22","endPage":"23","ipdsId":"IP-104316","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":361032,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"},{"id":361034,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523 brichards@usgs.gov","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":3533,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"brichards@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":189820,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richgels, Katherine L. 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D.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":756691,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204665,"text":"70204665 - 2019 - Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for volcanic systems","interactions":[],"lastModifiedDate":"2020-10-06T20:26:12.686077","indexId":"70204665","displayToPublicDate":"2019-01-01T13:24:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for volcanic systems","docAbstract":"Volcanoes are hazardous to local and global populations, but only a fraction are continuously monitored by ground-based sensors. For example, in Latin America, more than 60% of Holocene volcanoes are unmonitored, meaning long-term multi-parameter datasets of volcanic activity are rare and sparse. We use satellite observations of degassing, thermal anomalies, and surface deformation spanning 17 years at 47 of the most active volcanoes in Latin America, and compare these datasets to ground-based observations archived by the Global Volcanism Program (GVP). This first comparison of multi-satellite time-series on a regional scale provides information regarding volcanic behavior during, non-, pre-, syn- and post-eruptive periods. For example, at Copahue volcano, deviations from background activity in all three types of satellite measurements were manifested months to years in advance of renewed eruptive activity in 2012. By quantifying the amount of degassing, thermal output, and deformation measured at each of these volcanoes, we test the classification of these volcanoes as open or closed volcanic systems. We find that ~28% of the volcanoes do not fall into either classification and the rest show elements of both, demonstrating a dynamic range of behavior that can change over time. Finally, we recommend how volcano monitoring could be improved through better coordination of available satellite-based capabilities and new instruments.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB016199","usgsCitation":"Reath, K., Pritchard, M., Poland, M.P., Delgado, F., Carn, S., Coppola, D., Andrews, B.J., Ebmeier, S., Rumpf, M.E., Henderson, S., Baker, S., Lundgren, P., Wright, R.E., Biggs, J., Lopez, T., Wauthier, C., Moruzzi, S., Alcott, A., Wessels, R., Griswold, J.P., Ogburn, S.E., Loughlin, S.C., Meyer, F., Vaughan, R.G., and Bagnardi, M., 2019, Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for volcanic systems: Journal of Geophysical Research, v. 124, no. 1, p. 195-218, https://doi.org/10.1029/2018JB016199.","productDescription":"24 p.","startPage":"195","endPage":"218","ipdsId":"IP-103967","costCenters":[{"id":617,"text":"Volcano Science 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In this study, we characterized spatial and temporal patterns of spring phenology and explored how they were related to preceding temperature and moisture conditions. We tested correlations between late winter weather and indicators of the onset and the length of the spring growing period with 250-m resolution time-series satellite data (2001 – 2013) for Wyoming, USA. In western Wyoming mountains, drier and warmer conditions during late winter were associated with earlier spring green-up onset of growth in forests, shrubs, and grasses. In the northeast mountains, onset of spring correlated positively with preceding warmer temperatures, but not with precipitation. In most basin and plains shrublands and grasslands, spring onset was not correlated with temperature, although earlier onset of spring was correlated with drier conditions in 25% of shrub/scrub areas. Results about the length of spring were less definitive, with warmer temperatures related to longer green-up time for 12–30% of the land cover in western mountains but to shorter green-up time periods for 10–20% of the grasses and shrubs in basins and plains. Complex phenological patterns are likely to affect ungulate foraging behaviour on a local scale.
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\"}}]}","volume":"40","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-16","publicationStatus":"PW","scienceBaseUri":"5bfd146fe4b0815414ca38f8","contributors":{"authors":[{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","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":751958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@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":751960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","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":751959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":210786,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":751961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215591,"text":"70215591 - 2019 - Perspectives and challenges for the use of radar in biological conservation","interactions":[],"lastModifiedDate":"2020-10-25T17:42:42.466413","indexId":"70215591","displayToPublicDate":"2018-12-31T12:40:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Perspectives and challenges for the use of radar in biological conservation","docAbstract":"Radar is at the forefront for the study of broad‐scale aerial movements of birds, bats and insects and related issues in biological conservation. Radar techniques are especially useful for investigating species which fly at high altitudes, in darkness, or which are too small for applying electronic tags. Here, we present an overview of radar applications in biological conservation and highlight its future possibilities. Depending on the type of radar, information can be gathered on local‐ to continental‐scale movements of airborne organisms and their behaviour. Such data can quantify flyway usage, biomass and nutrient transport (bioflow), population sizes, dynamics and distributions, times and dimensions of movements, areas and times of mass emergence and swarming, habitat use and activity ranges. Radar also captures behavioural responses to anthropogenic disturbances, artificial light and man‐made structures. Weather surveillance and other long‐range radar networks allow spatially broad overviews of important stopover areas, songbird mass roosts and emergences from bat caves. Mobile radars, including repurposed marine radars and commercially dedicated ‘bird radars’, offer the ability to track and monitor the local movements of individuals or groups of flying animals. Harmonic radar techniques have been used for tracking short‐range movements of insects and other small animals of conservation interest. However, a major challenge in aeroecology is determining the taxonomic identity of the targets, which often requires ancillary data obtained from other methods. Radar data have become a global source of information on ecosystem structure, composition, services and function and will play an increasing role in the monitoring and conservation of flying animals and threatened habitats worldwide.
Tensiometer-equipped data acquisition systems measure and record positive and negative soil-water pressures. These data contribute to studies in hillslope hydrology, including analyses of rainfall runoff, near-surface hydrologic response, and slope stability. However, the unique ability of a tensiometer to rapidly and accurately measure pre- and post-saturation subsurface pressures requires maintenance techniques that have precluded their application to unattended sensor networks in semiarid regions. Under suction, the de-aired water in the tensiometer is drawn from a porous cup. Under positive pressure, dissolved gases from pore water infiltrates the cup. Over time, both contribute to unreliable readings and/or poor signal response through cavitation. To address this problem, we used commercially available equipment to develop a simple system of solenoid valves and a water reservoir that enable automated in situ tensiometer refilling. We tested the system at two post-wildfire hydrologic monitoring sites in the Angeles National Forest, southern California. We present example results from 3 mo of monitoring and show how the tensiometers can be refilled by a remote trigger. By remotely refilling the tensiometer, we were able to continuously monitor quasi-saturated soil pore-water pressures without making repeated and costly maintenance visits.
","language":"English","publisher":"Soil Science Society of America, Inc","doi":"10.2136/vzj2018.04.0070","usgsCitation":"Smith, J.B., and Kean, J.W., 2019, Long-term soil-water tension measurements in semi-arid environments: A method for automated tensiometer refilling: Vadose Zone Journal, v. 17, no. 1, 180070; 5 p., https://doi.org/10.2136/vzj2018.04.0070.","productDescription":"180070; 5 p.","ipdsId":"IP-102211","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468018,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2018.04.0070","text":"Publisher Index Page"},{"id":437612,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98G0FS2","text":"USGS data release","linkHelpText":"Hillslope hydrologic monitoring data following the 2009 Station Fire, Los Angeles County, California, November 2015 to June 2017"},{"id":364974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":764900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":764901,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203609,"text":"70203609 - 2019 - UZIG research: Measurement and characterization of unsaturated zone processes under wide-ranging climates and changing conditions","interactions":[],"lastModifiedDate":"2019-05-23T15:21:55","indexId":"70203609","displayToPublicDate":"2018-12-20T15:19:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"UZIG research: Measurement and characterization of unsaturated zone processes under wide-ranging climates and changing conditions","docAbstract":"Unsaturated zone properties and processes are central to understanding the interacting effects of land-use change, contamination, and hydroclimate on our ability to grow food, sustain clean water supplies, and minimize loss of life and property. Advances in unsaturated zone science are being achieved through collaborations across traditional boundaries where information from biological, physical, and chemical disciplines is combined for new insights. The Unsaturated Zone Interest Group (UZIG) is an organization that exists principally to promote multidisciplinary collaborations and the sharing of ideas, expertise, and technical assets. Here we summarize key findings from 14 papers, several of which originated from a meeting convened by UZIG in 2017 at the University of Florida in Gainesville titled “Land-Use Change, Climate Change, and Hydrologic Extremes: Unsaturated Zone Responses and Feedbacks.” This special section of Vadose Zone Journal contains multidisciplinary research in three general categories relevant to measuring and understanding unsaturated zone responses to changing land uses and climate: (i) unsaturated zone properties and processes; (ii) soil–plant–atmosphere interactions; and (iii) novel field sampling devices. A strong cross-cutting theme in these papers is the value of continuous monitoring data and ways of utilizing them to discover novel hydrologic, biologic, and pedologic information. As climatic and land-use conditions change and demands for resources and stresses on ecosystems continue to intensify, it is vital to improve our fundamental understanding of the processes at work in the unsaturated zone. Toward that goal, we discuss the need for improved ground-based unsaturated zone monitoring networks.
","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2018.11.0198","usgsCitation":"Trost, J.J., Mirus, B.B., Perkins, K., Henson, W.R., Nimmo, J.R., and Munoz-Carpena, R., 2019, UZIG research: Measurement and characterization of unsaturated zone processes under wide-ranging climates and changing conditions: Vadose Zone Journal, v. 17, no. 1, 5 p., https://doi.org/10.2136/vzj2018.11.0198.","productDescription":"5 p.","ipdsId":"IP-102646","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468019,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2018.11.0198","text":"Publisher Index Page"},{"id":364135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Trost, Jared J. 0000-0003-0431-2151 jtrost@usgs.gov","orcid":"https://orcid.org/0000-0003-0431-2151","contributorId":3749,"corporation":false,"usgs":true,"family":"Trost","given":"Jared","email":"jtrost@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":763261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":763262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":763263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":763264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":763265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Munoz-Carpena, Rafael","contributorId":215860,"corporation":false,"usgs":false,"family":"Munoz-Carpena","given":"Rafael","email":"","affiliations":[{"id":39322,"text":"University of Florida at Gainesville","active":true,"usgs":false}],"preferred":false,"id":763266,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204242,"text":"70204242 - 2019 - Estimating occurrence, prevalence, and detection of amphibian pathogens: Insights from occupancy models","interactions":[],"lastModifiedDate":"2019-07-16T10:40:25","indexId":"70204242","displayToPublicDate":"2018-12-19T10:26:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occurrence, prevalence, and detection of amphibian pathogens: Insights from occupancy models","docAbstract":"Understanding the distribution of pathogens across landscapes and their prevalence within host populations is a common aim of wildlife managers. Despite the need for unbiased estimates of pathogen occurrence and prevalence for planning effective management interventions, many researchers fail to account for imperfect pathogen detection. Instead raw data are often reported, which may lead to ineffective, or even detrimental, management actions. We illustrate the utility of occupancy models for generating unbiased estimates of disease parameters by 1) providing a written tutorial describing how to fit these models in Program PRESENCE and 2) presenting a case study with the pathogen ranavirus. We analyzed ranavirus detection data from a wildlife refuge (Maryland, US) using occupancy modeling, which yields unbiased estimates of pathogen occurrence and prevalence. We found ranavirus prevalence was underestimated by up to 30% if imperfect pathogen detection was ignored. The unbiased estimate of ranavirus prevalence in larval wood frog (Lithobates sylvaticus; 0.73) populations was higher than in larval spotted salamander (Ambystoma maculatum; 0.56) populations. In addition, the odds of detecting ranavirus in tail samples were 6.7 times higher than detecting ranavirus in liver samples. Therefore, tail samples presented a nonlethal sampling method for ranavirus that may be able to detect early (nonsystemic) infections.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2018-02-042","usgsCitation":"Mosher, B.A., Brand, A., Wiewel, A., Miller, D., Gray, M., Miller, D.L., and Campbell Grant, E.H., 2019, Estimating occurrence, prevalence, and detection of amphibian pathogens: Insights from occupancy models: Journal of Wildlife Diseases, v. 55, no. 3, p. 563-575, https://doi.org/10.7589/2018-02-042.","productDescription":"13 p.","startPage":"563","endPage":"575","ipdsId":"IP-074863","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":365579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent Research Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.82807922363281,\n 39.06571441680544\n ],\n [\n -76.82275772094727,\n 39.06371515917004\n ],\n [\n -76.82842254638672,\n 39.05958318062962\n ],\n [\n -76.82292938232422,\n 39.0518517325806\n ],\n [\n -76.82533264160156,\n 39.0487855791302\n ],\n [\n -76.8244743347168,\n 39.04438608298337\n ],\n [\n -76.83134078979492,\n 39.04065296228084\n ],\n [\n -76.82481765747069,\n 39.03371950048907\n ],\n [\n -76.82172775268555,\n 39.013715318365406\n ],\n [\n -76.81159973144531,\n 39.00904686141452\n ],\n [\n -76.80610656738281,\n 39.01144782062009\n ],\n [\n -76.80438995361328,\n 39.016649619293\n ],\n [\n -76.79924011230469,\n 39.01398207802642\n ],\n [\n -76.79683685302734,\n 39.01798335219163\n ],\n [\n -76.78104400634766,\n 39.022117764305015\n ],\n [\n -76.77572250366211,\n 39.028652309716236\n ],\n [\n -76.75718307495117,\n 39.0374529875311\n ],\n [\n -76.75134658813477,\n 39.03558626866692\n ],\n [\n -76.7398452758789,\n 39.04638588792801\n ],\n [\n -76.73709869384766,\n 39.05518435709179\n ],\n [\n -76.72164916992188,\n 39.07291127545158\n ],\n [\n -76.72525405883789,\n 39.07984089017707\n ],\n [\n -76.72319412231445,\n 39.08703630823101\n ],\n [\n -76.7530632019043,\n 39.08783575382141\n ],\n [\n -76.74568176269531,\n 39.08663658203791\n ],\n [\n -76.74808502197266,\n 39.08290569500107\n ],\n [\n -76.75975799560547,\n 39.08397168286113\n ],\n [\n -76.76422119140625,\n 39.08050716342113\n ],\n [\n -76.7705726623535,\n 39.08543738986714\n ],\n [\n -76.76799774169922,\n 39.091832845856075\n ],\n [\n -76.78258895874023,\n 39.08890166718027\n ],\n [\n -76.80490493774414,\n 39.092499005837915\n ],\n [\n -76.83065414428711,\n 39.06838000557286\n ],\n [\n -76.82807922363281,\n 39.06571441680544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mosher, B. A.","contributorId":216927,"corporation":false,"usgs":false,"family":"Mosher","given":"B.","email":"","middleInitial":"A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":766136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brand, Adrianne","contributorId":216928,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":766137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiewel, ANM","contributorId":216929,"corporation":false,"usgs":false,"family":"Wiewel","given":"ANM","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":766138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, D. A. W.","contributorId":216930,"corporation":false,"usgs":false,"family":"Miller","given":"D. A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":766139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, MT","contributorId":216931,"corporation":false,"usgs":false,"family":"Gray","given":"MT","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":766140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Debra L.","contributorId":192524,"corporation":false,"usgs":false,"family":"Miller","given":"Debra","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":766141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":766135,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203775,"text":"70203775 - 2019 - Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data","interactions":[],"lastModifiedDate":"2019-06-12T08:55:18","indexId":"70203775","displayToPublicDate":"2018-12-18T09:48:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data","docAbstract":"Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the observed B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore provides evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.","language":"English","publisher":"ESA","doi":"10.1002/ecy.2583","usgsCitation":"Marino, J.A., Peacor, S.D., Bunnell, D., Vanderploeg, H.A., Pothoven, S.A., Elgin, A.K., Bence, J., Jiao, J., and Ionides, E.L., 2019, Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data: Ecology, v. 100, no. 3, Article e02583, 14 p., https://doi.org/10.1002/ecy.2583.","productDescription":"Article e02583, 14 p.","ipdsId":"IP-096682","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":488820,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2027.42/148243","text":"External Repository"},{"id":364584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marino, John A.","contributorId":216168,"corporation":false,"usgs":false,"family":"Marino","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":17862,"text":"Bradley University","active":true,"usgs":false}],"preferred":false,"id":764074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacor, Scott D.","contributorId":216169,"corporation":false,"usgs":false,"family":"Peacor","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":764075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216167,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","email":"dbunnell@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":764073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanderploeg, Henry A.","contributorId":195891,"corporation":false,"usgs":false,"family":"Vanderploeg","given":"Henry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":764076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pothoven, Steven A.","contributorId":92998,"corporation":false,"usgs":false,"family":"Pothoven","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":764077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elgin, Ashley K.","contributorId":216170,"corporation":false,"usgs":false,"family":"Elgin","given":"Ashley","email":"","middleInitial":"K.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":764078,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":764079,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jiao, J.","contributorId":216171,"corporation":false,"usgs":false,"family":"Jiao","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":764080,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ionides, Edward L.","contributorId":216172,"corporation":false,"usgs":false,"family":"Ionides","given":"Edward","email":"","middleInitial":"L.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":764081,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211910,"text":"70211910 - 2019 - Congruent population genetic structure but differing depths of divergence for three alpine stoneflies with similar ecology and geographic distributions","interactions":[],"lastModifiedDate":"2020-08-11T18:24:44.733885","indexId":"70211910","displayToPublicDate":"2018-12-17T13:14:33","publicationYear":"2019","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":"Congruent population genetic structure but differing depths of divergence for three alpine stoneflies with similar ecology and geographic distributions","docAbstract":"The glacial aquifer system, which is a collection of aquifers within Quaternary sediments in the glaciated conterminous United States, is a principal aquifer that supplies groundwater that serves about 42 million people and accounts for about 5 percent of the Nation’s drinking water. This aquifer system (the area of maximum glacial advance) underlies parts of 25 States and covers 1.87×106 square kilometers. A hydrogeologic framework is presented that divides the glaciated United States into 17 distinct hydrogeologic terranes using a geologic approach based on previous mapping. Each hydrogeologic terrane contains Quaternary sediment that is derived from a common depositional history and can be characterized by similar texture and thickness. Characteristics of Quaternary sediments are described using attributes computed from a lithologic database of well logs compiled from 24 States (excluding Kentucky). The hydrogeologic framework presents a nationwide picture of the glacial aquifer system and provides generalizations concerning the nature of aquifers within it (for example, whether the aquifers are shallow or deep, and unconfined or confined). In this way insights can be gained from understanding the similarities and differences in distinct parts of the glacial aquifer system and how they relate to water use and quality and to aquifer vulnerability.
Delineation of hydrogeologic terranes was based on an interpretation of existing geologic mapping of Quaternary sediments and the thickness of unconsolidated material. Overall thickness of Quaternary sediment was used to qualitatively rank the generalized complexity of the hydrogeologic framework in each terrane: “lower” complexity (assigned a terrane code 1), “moderate” complexity (terrane code 2), and “higher” complexity (terrane code 3). Letter designations appended to the terrane codes (for example, 1A, 1B, or 1C) differentiate terranes of similar complexity. Two unique areas, where thick, stratified, coarse-grained sediment dominates, were assigned terrane code 4.
Elements of this hydrogeologic framework include a glacial environments and surficial sediments geodatabase, which includes lithologic, geomorphic, and stratigraphic characterization of Quaternary sediments based on previous mapping; a gridded database of sediment and aquifer characteristics computed from lithologic logs obtained from water-well driller records; a water-use database with information on public-water supply systems and sources of groundwater; and estimated recharge computed from a geologically based soil-water balance model. A generalized map of the bedrock geology based on previous State-level mapping is included as well.
Quaternary sediment in the glaciated United States includes glacial, postglacial (Holocene) and nonglacial sediments. At land surface, 60 percent of the glacial sediment is till. Large areas of outwash and ice contact sediments are extensive throughout the Midwest but generally are confined to valleys in the Northeast and the Northwest. Lacustrine sediments were deposited in proglacial lakes adjacent to the present Great Lakes and in glacial Lake Agassiz in the eastern Dakotas and northwestern Minnesota. The median thickness of Quaternary sediment ranges from 6 to 45 meters across the 17 hydrogeologic terranes, but the maximum thickness is more than 500 meters in some areas. Quaternary sediments generally contain less than 10 percent coarse material; the median range is near zero percent under till to about 50 percent under ice contact and outwash sediments. About 80 percent of the coarse material lies within 25 to 40 meters of land surface.
In most of the glaciated United States, there is a small likelihood of penetrating an aquifer-material interval containing coarse material at least 3 meters thick. A single aquifer-material interval was recorded in about 44 percent of lithologic logs, whereas about 11 percent of the logs penetrated multiple intervals. About 44 percent of water wells in the lithologic database are completed in Quaternary sediment, and many of these Quaternary water wells (42 percent) are confined by at least 7.5 meters of fine materials. About 33 percent of these Quaternary water wells are unconfined—the remainder are where only thin layers (less than 3 meters) of coarse material are present. The median depths of Quaternary water wells range from 13 to 40 meters among the 17 hydrogeologic terranes.
Recharge ranges from more than 400 millimeters per year in the Northeast to 11 millimeters or less per year in the Dakotas and Montana (median value of 136 millimeters per year). Annual groundwater withdrawals compiled by county range on an areal basis from less than 1 to 370 millimeters per year, and the mean is 7.4 millimeters per year. About 36 percent of the withdrawals are for public-water supply, of which 70 percent are derived from Quaternary sediments. Groundwater withdrawals are less than 10 percent of recharge throughout most of the glaciated conterminous United States but are a larger proportion of recharge near urban areas in the Northeast and the Midwest, and in counties throughout drier parts of the Midwest.
The salient characteristics of the 17 hydrogeologic terranes are presented through maps and a set of descriptive plots to facilitate visual comparisons between selected sediment and aquifer characteristics. The thickness of Quaternary sediment generally increases from the lower complexity terranes through the higher complexity terranes, consistent with their delineation. Median proportions of coarse material in Quaternary sediment and depths to aquifer-material intervals are highly variable (less than 10 to 50 percent, and 0 to 30 meters, respectively). Median thicknesses of aquifer-material intervals generally fall within a narrow range (10 to 20 meters), except in two terranes that contain thick coarse-grained sediment (30 to 35 meters). The source of water in wells varies from mostly bedrock wells in the lower complexity terranes to mostly Quaternary wells in the higher complexity terranes where the sediment is thickest. A tree diagram compiled from a hierarchical cluster analysis of a matrix composed of metrics based on sediment and aquifer characteristics, and the distribution of water wells in each terrane, indicates some groups of terranes that can be treated as comparable when analyzing groundwater flow and quality.
Aquifer-material intervals indicated on maps prepared from the lithologic logs, including unconfined and confined conditions, correlate well with aquifer systems delineated on state maps for Illinois, Indiana, and North Dakota. The large scale of the study limits the resolution at which the maps can be interpreted, however, and alluvial units are not mapped correctly for some valleys in the Northeast and the Northwest. Lithologic logs used in the study are biased toward shallow depths because not all logs penetrate the entire thickness of Quaternary sediment, but this bias should not limit the utility of the sediment and aquifer descriptions because shallow depths are commonly exploited for water supply. The hydrogeologic framework will support ongoing studies of groundwater flow and quality in the U.S. Geological Survey National Water Quality Assessment program for the glaciated United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185091","usgsCitation":"Yager, R.M., Kauffman, L.J., Soller, D.R., Haj, A.E., Heisig, P.M., Buchwald, C.A., Westenbroek, S.M., and Reddy, J.E., 2019, Characterization and occurrence of confined and unconfined aquifers in Quaternary sediments in the glaciated conterminous United States (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2018–5091, 90 p., https://doi.org/10.3133/sir20185091.","productDescription":"Report: ix, 90 p.; Interactive Leaflet maps; Data releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081249","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":359750,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6PQG","text":"USGS data release","description":"USGS data release","linkHelpText":"Databases used to develop a hydrogeologic framework for Quaternary sediments in the glaciated conterminous United States"},{"id":359748,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/ds1090","text":"Data Series 1090","linkHelpText":"- Hydrogeologic Framework for Characterization and Occurrence of Confined and Unconfined Aquifers in Quaternary Sediments in the Glaciated Conterminous United States—A Digital Map Compilation and Database"},{"id":359747,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HH6J8X","text":"USGS data release","description":"USGS data release","linkHelpText":"Digital products from a hydrogeologic framework for Quaternary sediments within the glaciated conterminous United States"},{"id":359745,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5091/coverthb2.jpg"},{"id":359749,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2018/5091/sir20185091_index.html","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Index page for oversized, interactive Leaflet maps"},{"id":359746,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5091/sir20185091.pdf","text":"Report","size":"17.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5091"},{"id":361089,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5091/versionHist.txt","size":"1.27 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.969069883,\n 35.090811167\n ],\n [\n -65.343237884,\n 35.090811167\n ],\n [\n -65.343237884,\n 50.932504994\n ],\n [\n -124.969069883,\n 50.932504994\n ],\n [\n -124.969069883,\n 35.090811167\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: February 2019; Version 1.0: December 2018","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street
Iowa City, IA 52240
The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, began a study in 2013 to better understand the effects of drought stress on the Cedar River alluvial aquifer. After an evaluation of the existing groundwater-flow models for the alluvial aquifer, a plan was begun to construct an updated groundwater-flow model capable of evaluating the effect of prolonged drought and increased demand. As part of the effort to update the existing groundwater-flow model, data were collected during an airborne electromagnetic (AEM) survey in May 2017. The study area for the AEM survey encompasses about 53 square kilometers of the Cedar River Basin, west of Cedar Rapids, Iowa, and includes a 19-kilometer reach of the Cedar River. The AEM survey of the Cedar River alluvial aquifer and adjacent areas was completed to characterize the subsurface geology of the area to refine a lithologic framework. The collected AEM data were postprocessed by numerical inversion using the program EM1DFM to produce subsurface apparent resistivity cross sections. Changes observed in resistivity profile values with depth were used to infer lithologic changes and delineate three of the four lithologic units designated in the lithologic framework for this area: alluvial deposits, glacial till, and bedrock; hereafter referred to as the “lithologic framework.” The fourth unit, composed of surficial eolian sediments, was not delineated in these profiles because these units are thin and discontinuous and are not reliably distinguishable from flood plain alluvial deposits. For the purposes of delineating lithologic units using the AEM data, bedrock was assumed to be the lowest unit in a profile, glacial till was deposited on a bedrock surface, and alluvium was deposited on erosional till or bedrock surfaces.
A three-dimensional fence diagram was created as part of the lithologic framework to further define the extent and thickness of the lithologic units near the Cedar River alluvial aquifer. The fence diagram shows a three-dimensional perspective of unit thickness, extent, and orientation of the delineated lithologic framework. A lithologic framework, by design, is intended to represent a simplification of a more complex natural system through data interpolation between known points, which usually are lithologic logs. The resistivity profiles produced from the AEM survey allow for continuous mapping and accurate interpolation of lithology between lithologic logs; however, the apparent resistivity value may reflect several characteristics of subsurface materials including variations in lithology, porosity, water quality, grain sorting, and degree of saturation. In this study, the only variables considered were those related to changes in the subsurface material.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3423","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Valder, J.F., Haj, A.E., Bristow, E.L., and Valseth, K.J., 2019, Delineation of selected lithologic units using airborne electromagnetic data near Cedar Rapids, Iowa (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Map 3423, 2 sheets, 9-p. pamphlet, https://doi.org/10.3133/sim3423.","productDescription":"Pamphlet: vi, 9 p.; 2 Sheets: 42.0 x 24.0 inches and 44.0 x 28.0 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101741","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361084,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3423/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3423 Version History"},{"id":360194,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3423/sim3423_sheet2.pdf","text":"Sheet 2","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3423 Sheet 2"},{"id":360192,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3423/sim3423_pamphlet.pdf","text":"Pamphlet","size":"2.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3423 Pamphlet"},{"id":360191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3423/coverthb2.jpg"},{"id":360193,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3423/sim3423_sheet1.pdf","text":"Sheet 1","size":"7.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3423 Sheet 1"},{"id":360208,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS882S","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Airborne electromagnetic and magnetic survey data and inverted resistivity models, Cedar Rapids, Iowa, May 2017"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8,\n 42\n ],\n [\n -91.7,\n 42\n ],\n [\n -91.7,\n 42.0667\n ],\n [\n -91.8,\n 42.0667\n ],\n [\n -91.8,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: February 2019; Version 1.0: December 2018","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing salt marsh management decisions at the Bombay Hook National Wildlife Refuge in Delaware. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of eight salt marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per salt marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that for the objectives and actions considered here, total management benefits would increase consistently up to approximately \\$300,000, but that further expenditures would yield diminishing return on investment. Management actions selected within optimal portfolios at total costs less than \\$300,000 included hydrologic restoration, recontouring adjacent uplands to facilitate marsh migration, and burning the marsh. The prototype presented here provides a framework for decision making at the Bombay Hook National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181160","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Guiteras, S.T., and Mitchell, L.R., 2018, Optimization of salt marsh management at the Bombay Hook National Wildlife Refuge, Delaware, through use of structured decision making (ver. 1.1, May 2019): U.S. Geological Survey Open-File Report 2018–1160, 29 p., https://doi.org/10.3133/ofr20181160.","productDescription":"vi, 29 p.","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098065","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":360083,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1160/coverthb2.jpg"},{"id":364017,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2018/1160/versionHist.txt","text":"Version History","size":"1.35 KB","linkFileType":{"id":2,"text":"txt"}},{"id":360084,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1160/ofr20181160.pdf","text":"Report","size":"26.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1160"}],"country":"United States","state":"Delaware","otherGeospatial":"Bombay Hook National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.52928924560547,\n 39.18410260153466\n ],\n [\n -75.3885269165039,\n 39.18410260153466\n ],\n [\n -75.3885269165039,\n 39.30667511534216\n ],\n [\n -75.52928924560547,\n 39.30667511534216\n ],\n [\n -75.52928924560547,\n 39.18410260153466\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 29, 2019","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
Before the digital era, seismograms were recorded in analog form and read manually by analysts. The digital era represents only about 25% of the total time span of instrumental seismology. Analog data provide important constraints on earthquake processes over the long term, and in some cases are the only data available. The media on which analog data are recorded degrades with time and there is an urgent need for cost‐effective approaches to preserve the information they contain. In this study, we work directly with images by constructing a set of image‐based methods for earthquake processing, rather than pursue the usual approach of converting analog data to vector time series. We demonstrate this approach on one month of continuous Develocorder films from the Rangely earthquake control experiment run by the U.S. Geological Survey (USGS). We scan the films into images and compress these into low‐dimensional feature vectors as input to a classifier that separates earthquakes from noise in a defined feature space. We feed the detected event images into a short‐term average/long‐term average (STA/LTA) picker, a grid‐search associator, and a 2D image correlator to measure both absolute arrival times and relative arrival‐time differences between events. We use these measurements to locate the earthquakes using hypoDD. In the month that we studied, we identified 40 events clustered near the injection wells. In the original study, Raleigh et al. (1976) identified only 32 events during the same period. Scanning without vectorizing analog seismograms represents an attractive approach to archiving these perishable data. We demonstrated that it is possible to carry out precision seismology directly on such images. Our approach has the potential for wide application to analog seismograms.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220180298","usgsCitation":"Wang, K., Ellsworth, W., Beroza, G.C., Williams, G., Zhang, M., Schroeder, D., and Rubinstein, J.L., 2019, Seismology with dark data: Image-based processing of analog records using machine learning for the rangely earthquake control experiment: Seismological Research Letters, v. 90, no. 2A, p. 553-562, https://doi.org/10.1785/0220180298.","productDescription":"10 p.","startPage":"553","endPage":"562","ipdsId":"IP-101838","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":363470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","issue":"2A","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Kaiwen","contributorId":215275,"corporation":false,"usgs":false,"family":"Wang","given":"Kaiwen","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":761970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, William L. 0000-0001-8378-4979","orcid":"https://orcid.org/0000-0001-8378-4979","contributorId":194691,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William L.","affiliations":[],"preferred":false,"id":761971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beroza, Gregory C.","contributorId":191201,"corporation":false,"usgs":false,"family":"Beroza","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":761972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Gordon","contributorId":215276,"corporation":false,"usgs":false,"family":"Williams","given":"Gordon","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":761973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Miao","contributorId":215277,"corporation":false,"usgs":false,"family":"Zhang","given":"Miao","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":761974,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schroeder, Dustin","contributorId":215278,"corporation":false,"usgs":false,"family":"Schroeder","given":"Dustin","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":761975,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":206551,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":761969,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}