We propose creating and maintaining records of engagement and decision-making (RoED) to help us and our communities better understand ourselves, our goals, our decisions, and the dynamic systems in which we all live. The purpose of RoED is to go well beyond noting that dialogue occurred or a decision was reached. The records should, in ways appropriate to the context and participants, document interactions and note biases, beliefs, emotions, behaviors, norms, and values. These crucial aspects are generally absent in academic papers and formal reports, yet they always play a role in decision-making processes. While not a panacea for addressing critical biophysical and social challenges, we propose that a comprehensive framework for promoting realistic, legitimate and inclusive engagement could enhance trust, establish institutional memory, and when and where appropriate, ensure greater transparency. The aim is to create and maintain RoED to collect significant information and share insights from multi-stakeholder decision-making processes from diverse institutions, contexts, and disciplinary domains. In the long-term RoED could promote more effective adaptive management or governance approaches. This paper describes an exploratory phase intended to catalyze collaborative efforts worldwide.
We examine the annual, seasonal, monthly, and diurnal climate responses to the land use change (LUC) in eastern United States and Cuba during four epochs (1650, 1850, 1920, and 1992) with ensemble simulations conducted with the RegCM4 regional climate model that includes the Biosphere Atmosphere Transfer Scheme (BATS1e) surface physics package (Dickinson et al., 1993). We derived the land use (LU) data sets by harmonizing a previous reconstruction (Steyaert & Knox, 2008) with updated observations and modeled potential vegetation. The eight‐member ensembles for each epoch were driven with randomly perturbed 1990–2002 atmospheric boundary conditions derived from the National Center for Environmental Prediction global reanalysis. LUC induces statistically significant climate responses across all epochs; the largest changes occur between 1850 and 1920 with the widespread conversion of forests in the United States and forests, grassland, and woody wetlands in Cuba to agriculture. The atmospheric feedback from the aggregated grid‐cell responses attributed to physical and biophysical parameters in BATS1e alters the circulation in the lower atmosphere, thereby propagating the LUC regionally. Depending on the season and location, the altered circulation reinforces, attenuates, or has little effect on surface responses. Relative to pre‐settlement (1650), the 1992 LU produces colder mean annual air temperature (−0.09 ± 0.16 °C) and increased precipitation (0.08 ± 0.09 mm day−1) over the United States, warmer (0.08 °C) and wetter (0.03 mm day−1) conditions over Florida, and warming (0.32 °C) and drying (−0.03 mm day−1) over Cuba, indicating that LUC has played a varying role in climate change over the region.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019jd030965","usgsCitation":"Hostetler, S.W., Reker, R., Alder, J.R., Loveland, T., Willard, D.A., Bernhardt, C.E., Sundquist, E.T., and Thompson, R., 2019, Application of a regional climate model to assess changes in the climatology of the Eastern US and Cuba associated with historic landcover change: JGR Atmospheres, v. 124, no. 22, p. 11722-11745, https://doi.org/10.1029/2019jd030965.","productDescription":"24 p.","startPage":"11722","endPage":"11745","ipdsId":"IP-106240","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":379374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Cuba","state":"Florida","otherGeospatial":"Eastern United States, Cuba","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.603515625,\n 29.916852233070173\n ],\n [\n -92.98828125,\n 29.152161283318915\n ],\n [\n -89.736328125,\n 29.305561325527698\n ],\n [\n -87.5390625,\n 30.29701788337205\n ],\n [\n -84.19921875,\n 29.6880527498568\n ],\n [\n -82.177734375,\n 25.562265014427492\n ],\n [\n -80.5078125,\n 24.926294766395593\n ],\n [\n -79.62890625,\n 25.958044673317843\n ],\n [\n -81.38671875,\n 29.22889003019423\n ],\n [\n -81.123046875,\n 31.80289258670676\n ],\n [\n -75.9375,\n 35.96022296929667\n ],\n [\n -75.76171875,\n 37.50972584293751\n ],\n [\n -73.740234375,\n 39.50404070558415\n ],\n [\n -74.8828125,\n 41.705728515237524\n ],\n [\n -80.419921875,\n 41.96765920367816\n ],\n [\n -87.5390625,\n 41.64007838467894\n ],\n [\n -91.49414062499999,\n 40.51379915504413\n ],\n [\n -95.625,\n 40.64730356252251\n ],\n [\n -94.21875,\n 36.31512514748051\n ],\n [\n -94.306640625,\n 33.797408767572485\n ],\n [\n -93.603515625,\n 29.916852233070173\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.55078125,\n 21.861498734372567\n ],\n [\n -77.51953125,\n 19.476950206488414\n ],\n [\n -74.1796875,\n 19.559790136497412\n ],\n [\n -73.916015625,\n 21.289374355860424\n ],\n [\n -82.44140625,\n 24.126701958681668\n ],\n [\n -84.814453125,\n 22.998851594142913\n ],\n [\n -84.55078125,\n 21.861498734372567\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"22","noUsgsAuthors":false,"publicationDate":"2019-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - 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Near the Mendocino Triple Junction, however, we recognize several abrupt GNSS velocity changes that reflect a different process. After correcting for earthquakes and seasonal loading, we find that several dozen GNSS time series show spatially coherent east‐west velocity changes of ~2 mm/yr and that these changes coincide in time with regional M > 6.5 earthquakes. We consider several hypotheses and propose that dynamically triggered changes in megathrust coupling best explain the data. Our inversions locate the coupling changes slightly updip of the tremor‐producing zone. We speculate that fluid exchange surrounding the tremor region may be important. Such observations of transient coupling changes are rare and challenging to explain mechanistically but have important implications for earthquake processes on faults.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019gl084395","usgsCitation":"Materna, K.Z., Bartlow, N., Wech, A., Williams, C., and Burgmann, R., 2019, Dynamically triggered changes of plate interface coupling in Southern Cascadia: Geophysical Research Letters, v. 46, no. 22, p. 12890-12899, https://doi.org/10.1029/2019gl084395.","productDescription":"10 p.","startPage":"12890","endPage":"12899","ipdsId":"IP-108740","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":459536,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl084395","text":"Publisher Index Page"},{"id":379373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Southern Cascadia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.0791015625,\n 39.45316112807394\n ],\n [\n -123.22265625000001,\n 39.45316112807394\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -126.0791015625,\n 42.00032514831621\n ],\n [\n -126.0791015625,\n 39.45316112807394\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"22","noUsgsAuthors":false,"publicationDate":"2019-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":801370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartlow, Noel 0000-0002-9961-5608","orcid":"https://orcid.org/0000-0002-9961-5608","contributorId":242895,"corporation":false,"usgs":false,"family":"Bartlow","given":"Noel","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":801371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":801372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Charles 0000-0001-7435-9196","orcid":"https://orcid.org/0000-0001-7435-9196","contributorId":243027,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":801373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":801374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206024,"text":"70206024 - 2019 - Quantifying source and sink habitats and pathways in spatially structured populations: A generalized modelling approach","interactions":[],"lastModifiedDate":"2019-10-18T06:31:20","indexId":"70206024","displayToPublicDate":"2019-10-14T11:24:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying source and sink habitats and pathways in spatially structured populations: A generalized modelling approach","docAbstract":"The ability to classify habitats and movement pathways as sources or sinks is an important part of the decision making process for the conservation of spatially structured populations. Diverse approaches have been used to quantify the importance of habitats and pathways in a spatial network, however these approaches have been limited by a lack of general applicability across life histories and movement strategies. In this paper we develop a generalized per-capita\ncontribution metric, the C-metric, for quantifying habitat and pathway quality. This metric is novel in that it can be applied broadly to both metapopulations and migratory species. It allows for any number of age and sex classes, unlimited number of seasons or time intervals within the annual cycle, and for density-dependent parameters. We demonstrate the flexibility of the metric\nwith four case studies: a hypothetical metapopulation, elk of the Greater Yellowstone Ecosystem, northern pintail ducks in North America, and the eastern population of the monarch butterfly. General computer code to calculate the per-capita contribution metric is provided. We demonstrate that the C-metric is useful for identifying source and sink habitats in a network and suggest that the C-metric could be supplemented by some measure of network structure for\na more robust description of habitat or pathway importance.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2019.06.003","usgsCitation":"Diffendorfer, J., Sample, C., Beiri, J.A., Allen, B.L., Dementieva, Y., Carson, A., Higgins, C., Piatt, S., Qiu, S., Stafford, S., Mattsson, B., Semmens, D.J., and Thogmartin, W.E., 2019, Quantifying source and sink habitats and pathways in spatially structured populations: A generalized modelling approach: Ecological Modelling, v. 407, p. 1-10, https://doi.org/10.1016/j.ecolmodel.2019.06.003.","productDescription":"108715, 10p.","startPage":"1","endPage":"10","onlineOnly":"N","ipdsId":"IP-107928","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science 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We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-019-11472-7","usgsCitation":"Bastida, F., Garcia, C.M., Fierer, N., Eldridge, D.J., Bowker, M.A., Abades, S.R., Alfaro, F.D., Berhe, A., Cutler, N.A., Gallardo, A., Garcia-Velazquez, L., Hart, S.C., Hayes, P.E., Hernandez, T., Hseu, Z., Jehmlich, N., Kirchmair, M., Lambers, H., Neuhauser, S., Pena-Ramirez, V.M., Perez, C.A., Reed, S.C., Santos, F., Siebe, C., Sullivan, B., Trivedi, P., Vera, A., Williams, M., Moreno, J.M., and Delgado-Baquerizo, M., 2019, Global ecological predictors of the soil priming effect: Nature Communications, v. 10, 3481, 9 p., https://doi.org/10.1038/s41467-019-11472-7.","productDescription":"3481, 9 p.","ipdsId":"IP-102199","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":459540,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-11472-7","text":"Publisher Index 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A.","contributorId":214703,"corporation":false,"usgs":false,"family":"Cutler","given":"Nick","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":801921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gallardo, Antonio","contributorId":52024,"corporation":false,"usgs":true,"family":"Gallardo","given":"Antonio","email":"","affiliations":[],"preferred":false,"id":801922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Garcia-Velazquez, Laura","contributorId":240756,"corporation":false,"usgs":false,"family":"Garcia-Velazquez","given":"Laura","affiliations":[],"preferred":false,"id":801923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hart, Stephen C.","contributorId":189074,"corporation":false,"usgs":false,"family":"Hart","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":801924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hayes, Patrick E.","contributorId":214704,"corporation":false,"usgs":false,"family":"Hayes","given":"Patrick","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":801925,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hernandez, Teresa","contributorId":243274,"corporation":false,"usgs":false,"family":"Hernandez","given":"Teresa","email":"","affiliations":[],"preferred":false,"id":801926,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hseu, Zeng-Yei","contributorId":214705,"corporation":false,"usgs":false,"family":"Hseu","given":"Zeng-Yei","email":"","affiliations":[],"preferred":false,"id":801927,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Jehmlich, Nico","contributorId":243275,"corporation":false,"usgs":false,"family":"Jehmlich","given":"Nico","email":"","affiliations":[],"preferred":false,"id":801928,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Kirchmair, 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A.","contributorId":214708,"corporation":false,"usgs":false,"family":"Perez","given":"Cecilia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":801933,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":801934,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Santos, Fernanda","contributorId":243276,"corporation":false,"usgs":false,"family":"Santos","given":"Fernanda","affiliations":[],"preferred":false,"id":801935,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Siebe, Christina","contributorId":214710,"corporation":false,"usgs":false,"family":"Siebe","given":"Christina","email":"","affiliations":[],"preferred":false,"id":801936,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sullivan, Benjamin W.","contributorId":102401,"corporation":false,"usgs":true,"family":"Sullivan","given":"Benjamin W.","affiliations":[],"preferred":false,"id":801937,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Trivedi, Pankaj","contributorId":240760,"corporation":false,"usgs":false,"family":"Trivedi","given":"Pankaj","email":"","affiliations":[],"preferred":false,"id":801938,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Vera, Alfonso","contributorId":243277,"corporation":false,"usgs":false,"family":"Vera","given":"Alfonso","email":"","affiliations":[],"preferred":false,"id":801939,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Williams, Mark","contributorId":214696,"corporation":false,"usgs":false,"family":"Williams","given":"Mark","affiliations":[],"preferred":false,"id":801940,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Moreno, Jose M.","contributorId":150464,"corporation":false,"usgs":false,"family":"Moreno","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":18029,"text":"D Ciencias Ambientales, U Castilla La Mancha, Toledo, Spain","active":true,"usgs":false}],"preferred":false,"id":801941,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Delgado-Baquerizo, Manuel","contributorId":214645,"corporation":false,"usgs":false,"family":"Delgado-Baquerizo","given":"Manuel","email":"","affiliations":[{"id":39101,"text":"Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA","active":true,"usgs":false}],"preferred":false,"id":801942,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70212818,"text":"70212818 - 2019 - Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","interactions":[],"lastModifiedDate":"2024-05-16T14:56:12.436485","indexId":"70212818","displayToPublicDate":"2019-10-14T08:20:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","docAbstract":"Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.
Birds with altricial offspring need to feed them regularly, but each feeding visit risks drawing attention to the nest and revealing its location to potential predators. Synchronisation of visits by both parents has been suggested as a behavioural adaptation to reduce the risk of nest predation. Under this hypothesis, higher risk of nest predation favours greater synchrony of parental feeding visits. We investigated this prediction over three timescales using nestling provisioning data from 25 passerine species in Tasmania and New Zealand. We estimated the extent to which parents actively synchronised their visits to the nest by comparing observed patterns of synchrony with those expected to occur at random. We found that in general, species did not synchronise visits more often than expected by chance. Species varied in the tendency to synchronise visits, but this variation was not explained by likely predation pressure in the distant evolutionary past: New Zealand endemic species, which evolved in the absence of mammalian nest predators, synchronised their visits as often as species which evolved with more diverse predatory guilds. Nest predation risk has increased over time in New Zealand due to introduced predators, but synchrony in visits also was not explained by manipulated predation risk: visit synchrony was equivalent between a predator-removal site and a site where predators remained. However, within one New Zealand species, visit synchrony was higher for mainland populations, which have been exposed to predatory mammals for c.800 years, than for a population on an offshore island to which predatory mammals were never introduced. We conclude that breeding birds may have some capacity to adapt the synchrony with which they provision over short evolutionary timescales. However, the lack of synchrony in most species suggests that either asynchrony provides benefits that outweigh the greater risk of predation, or synchrony incurs costs not compensated by reduced predation.
","language":"English","publisher":"Frontiers Research Foundation","doi":"10.3389/fevo.2019.00389","usgsCitation":"Khwaja, N., Massaro, M., Martin, T.E., and Briskie, J.V., 2019, Do parents synchronise nest visits as an antipredator adaptation in birds of New Zealand and Tasmania?: Frontiers in Ecology and Environment, v. 7, 389, 11 p., https://doi.org/10.3389/fevo.2019.00389.","productDescription":"389, 11 p.","ipdsId":"IP-107192","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00389","text":"Publisher Index Page"},{"id":393649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, New Zealand","state":"Tasmania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.31640625,\n -44.087585028245165\n ],\n [\n 148.798828125,\n -44.087585028245165\n ],\n [\n 148.798828125,\n -40.51379915504413\n ],\n [\n 144.31640625,\n -40.51379915504413\n ],\n [\n 144.31640625,\n -44.087585028245165\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 169.716796875,\n -47.338822694822\n ],\n [\n 176.923828125,\n -40.44694705960048\n ],\n [\n 178.9453125,\n -37.43997405227057\n ],\n [\n 176.8359375,\n -36.87962060502676\n ],\n [\n 172.35351562499997,\n -33.9433599465788\n ],\n [\n 173.759765625,\n -37.5097258429375\n ],\n [\n 173.14453125,\n -39.43619299931407\n ],\n [\n 174.19921875,\n -40.44694705960048\n ],\n [\n 171.03515625,\n -40.044437584608566\n ],\n [\n 165.322265625,\n -46.07323062540835\n ],\n [\n 167.6953125,\n -47.93106634750977\n ],\n [\n 169.716796875,\n -47.338822694822\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2019-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Khwaja, Nyil","contributorId":270665,"corporation":false,"usgs":false,"family":"Khwaja","given":"Nyil","email":"","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massaro, Melanie","contributorId":270666,"corporation":false,"usgs":false,"family":"Massaro","given":"Melanie","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":829713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briskie, James V.","contributorId":270667,"corporation":false,"usgs":false,"family":"Briskie","given":"James","email":"","middleInitial":"V.","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829716,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205952,"text":"70205952 - 2019 - sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar","interactions":[],"lastModifiedDate":"2019-10-11T09:17:44","indexId":"70205952","displayToPublicDate":"2019-10-11T08:55:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar","docAbstract":"This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely (R^2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections (R^2 = 0.95) but the agreement was not as strong for the transect with greater depths (R^2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and < 1% at the second.","language":"English","publisher":"MDPI","doi":"10.3390/rs11192317","usgsCitation":"Kinzel, P.J., and Legleiter, C.J., 2019, sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar: Remote Sensing, v. 11, no. 19, 2317, 19 p., https://doi.org/10.3390/rs11192317.","productDescription":"2317, 19 p.","onlineOnly":"Y","ipdsId":"IP-111227","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":459558,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11192317","text":"Publisher Index Page"},{"id":437308,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBGCPT","text":"USGS data release","linkHelpText":"UAS-based remotely sensed data and field measurements of flow depth and velocity from the Blue River, Colorado, October 17-18, 2019"},{"id":368258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Grand County","otherGeospatial":"Blue River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.40190124511719,\n 40.03977220579366\n ],\n [\n -106.38670921325682,\n 40.03977220579366\n ],\n [\n -106.38670921325682,\n 40.04581742420946\n ],\n [\n -106.40190124511719,\n 40.04581742420946\n ],\n [\n -106.40190124511719,\n 40.03977220579366\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":773024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":773025,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205990,"text":"70205990 - 2019 - Tropical cyclones and the organization of mangrove forests: A review","interactions":[],"lastModifiedDate":"2020-03-20T13:12:27","indexId":"70205990","displayToPublicDate":"2019-10-11T07:00:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":789,"text":"Annals of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Tropical cyclones and the organization of mangrove forests: A review","docAbstract":"Background\nMany mangrove ecosystems are periodically exposed to high velocity winds and surge from tropical cyclones and often recover with time and continue to provide numerous societal benefits in the wake of storm events.\n\nScope\nThis review focuses on the drivers and disturbance mechanisms (visible and functional) that tropical cyclones of various intensities have on mangrove ecosystem properties from around the world, as well as the potential ecosystem services role offered by mangroves along storm-ravaged coastlines. When viewed together, studies describe repeatable types of impact and a variety of responses of mangroves that make them ecologically resilient to high velocity winds, and which have served to advance the notion that mangroves are disturbance-adapted ecosystems.\n\nConclusions\nStudies have documented massive tree mortality and forest structural shifts as well as high variability of spatial effects associated with proximity and direction of tropical cyclone trajectory that influence biogeochemical processes, recovery of individual trees, and forest regeneration and succession. Mangroves provide coastal protection through surge and wind suppression during tropical cyclones, and yet are able to overcome wind effects and often recover unless some alternate environmental stress is at play (e.g., hydrologic alteration or sedimentation). Structural elements of mangroves are influenced by the legacies imposed by past tropical cyclone injury, which affect their current appearance, and presumably function of mangroves, at any point in time. However, much is yet to be discovered about the importance of tropical cyclones on these fascinating botanical ecosystems including the role of storm-based sediment subsidies, and much more effort will be needed to predict future recovery patterns as frequency and intensity of tropical cyclones potentially change.","language":"English","publisher":"Oxford","doi":"10.1093/aob/mcz161","usgsCitation":"Krauss, K., and Osland, M., 2019, Tropical cyclones and the organization of mangrove forests: A review: Annals of Botany, v. 123, no. 2, p. 213-234, https://doi.org/10.1093/aob/mcz161.","productDescription":"22 p.","startPage":"213","endPage":"234","ipdsId":"IP-105603","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459562,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aob/mcz161","text":"Publisher Index Page"},{"id":368334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":773222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":219805,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":773223,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205926,"text":"70205926 - 2019 - Drought in the U.S. Caribbean: Impacts to freshwater ecosystems","interactions":[],"lastModifiedDate":"2020-12-09T13:06:15.088591","indexId":"70205926","displayToPublicDate":"2019-10-11T06:53:54","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Drought in the U.S. Caribbean: Impacts to freshwater ecosystems","docAbstract":"Healthy and functioning freshwater ecosystems are needed for successful conservation and management of native fish and invertebrate species, and the services they provide to human communities, across the U.S. Caribbean. Yet streams, rivers, and reservoirs are vulnerable to the effects of extreme weather events, urbanization, energy and water development, and other environmental and human-caused disturbances (Neal et al., 2009). One major management concern is the impact of prolonged drought on freshwater ecosystems. Drought impacts streamflow, dissolved oxygen content, water quality, stream connectivity, available habitat, and other important freshwater habitat characteristics necessary for sustaining fish and invertebrate populations (Covich et al., 2006). These changes can impact species interactions, abundance, life history events, and the presence of native and non-native species (Larsen, 2000; Covich et al., 2006; Ramírez et al., 2018).
Drought impacts aquatic ecosystems and species both in the short-term and long-term, depending on the severity and duration of the event (e.g. Covich et al., 2006). In Puerto Rico, all native freshwater fish, shrimp, and snail species spend part of their lives in estuarine and marine ecosystems and depend on being able to move between these habitats to survive, so maintaining connectivity is key (e.g., Engman et al., 2017). Freshwater ecosystems also provide recreational, cultural, and ecological value to humans (Kwak et al., 2007; Neal et al., 2009). For example, some communities in Puerto Rico engage in artisanal shrimp and freshwater crab fishing (Neal et al., 2009). Artisanal fishing for postlarvae gobioids, known colloquially as “cetí” also occurs at the river mouths of large drainages and has strong cultural significance in parts of Puerto Rico, such as Arecibo (Kwak et al., 2016).
The U.S. Virgin Islands (USVI) is particularly sensitive to drought, because almost all streams are ephemeral and typically only flow after rainfall. These intermittent channels, known locally as “ghuts”, run down the surface of steep slopes, rather than through the ground, and are important sources of freshwater. Natural springs are often located in ghuts and can form pools of freshwater that serve as habitat for wetland and migratory birds, freshwater shrimp and fish, and amphibians (Nemeth and Platenburg, 2007; Gardner, 2008).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"U.S. Caribbean drought workshop","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"U.S. Caribbean Drought Workshop","conferenceDate":"May 30-31, 2018","conferenceLocation":"Rio Piedras, PR","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Myers, B., 2019, Drought in the U.S. Caribbean: Impacts to freshwater ecosystems, in U.S. Caribbean drought workshop, Rio Piedras, PR, May 30-31, 2018, 2 p.","productDescription":"2 p.","ipdsId":"IP-110663","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"links":[{"id":368254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368227,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/ecosystems/climate-adaptation-science-centers/drought-impacts-freshwater-ecosystems-us-caribbean"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.43408203124999,\n 17.748686651728807\n ],\n [\n -65.2587890625,\n 17.748686651728807\n ],\n [\n -65.2587890625,\n 18.60460138845525\n ],\n [\n -67.43408203124999,\n 18.60460138845525\n ],\n [\n -67.43408203124999,\n 17.748686651728807\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.9456787109375,\n 17.651873989224537\n ],\n [\n -64.51995849609375,\n 17.651873989224537\n ],\n [\n -64.51995849609375,\n 17.79707337422801\n ],\n [\n -64.9456787109375,\n 17.79707337422801\n ],\n [\n -64.9456787109375,\n 17.651873989224537\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.65042114257812,\n 18.35061520525845\n ],\n [\n -64.7211456298828,\n 18.376682358161855\n ],\n [\n -64.77195739746094,\n 18.383850134829828\n ],\n [\n -64.93091583251953,\n 18.429130557589243\n ],\n [\n -65.115966796875,\n 18.404700154006118\n ],\n [\n -65.07923126220703,\n 18.310203344724197\n ],\n [\n -64.88147735595703,\n 18.26326160374951\n ],\n [\n -64.68852996826172,\n 18.26195748515144\n ],\n [\n -64.65042114257812,\n 18.35061520525845\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Myers, Bonnie 0000-0002-3170-2633","orcid":"https://orcid.org/0000-0002-3170-2633","contributorId":219702,"corporation":false,"usgs":true,"family":"Myers","given":"Bonnie","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":772919,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70202703,"text":"sir20195018 - 2019 - Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams","interactions":[],"lastModifiedDate":"2019-10-11T06:32:47","indexId":"sir20195018","displayToPublicDate":"2019-10-10T15:41:08","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5018","displayTitle":"Flood-Frequency Estimates for Ohio Streamgages Based on Data through Water Year 2015 and Techniques for Estimating Flood-Frequency Characteristics of Rural, Unregulated Ohio Streams","title":"Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams","docAbstract":"Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty.
Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables.
The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates.
Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams.
Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November.
Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195018","collaboration":"Prepared in cooperation with the Ohio Department of Transportation","usgsCitation":"Koltun, G.F., 2019, Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams: U.S. Geological Survey Scientific Investigations Report 2019–5018, 25 p., https://doi.org/10.3133/sir20195018.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendices 1.1-1.8; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368118,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018.pdf","text":"Report ","size":"6.45 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737
The Arthur R. Marshall Loxahatchee National Wildlife Refuge (refuge), Boynton Beach, Florida, contains approximately 147,000 acres southeast of Lake Okeechobee. Water quality in the interior portion of the refuge is strongly influenced by rainfall, resulting in slightly acidic waters with low dissolved ions. Desmids, a unique, ornate group of green algae loosely associated with submerged vascular plants, were photo-documented for the first time in samples from the refuge. The canal system surrounding the refuge contains a high level of ions from agricultural runoff, and intrusion of this water into the refuge interior during high canal water levels may have altered some of the desmid population. A transect from the canal to the interior was sampled every 3 months, and the species present were photographed, identified, and catalogued. Approximately 260 unique taxa from 29 genera were encountered. The interior of the refuge had the greatest diversity of desmids; however, the areas of the refuge adjacent to the canals still contained a rich population of desmids. We postulate that the diversity of desmids indicates that the pristine portions of the refuge may be an important refugium for desmids, particularly for those species restricted to the subtropical parts of the United States. This collection of taxa, identified to species with most specimens, will allow a more detailed examination of water quality issues when co-located water quality data are collected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195074","usgsCitation":"Rosen, B.H., Stahlhut, K.N., and Hall, J.D., 2019, Catalog of microscopic organisms of the Everglades, part 2—The desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge: U.S. Geological Survey Scientific Investigations Report 2019–5074, 277 p., https://doi.org/10.3133/sir20195074.","productDescription":"xii, 277 p.","numberOfPages":"294","onlineOnly":"N","ipdsId":"IP-104022","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":368073,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5074/sir20195074.pdf","text":"Report","size":"73.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5074"},{"id":368077,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5074/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Arthur R. Marshall Loxahatchee 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 -80.35812377929688,\n 26.311881633667735\n ],\n [\n -80.17684936523438,\n 26.311881633667735\n ],\n [\n -80.17684936523438,\n 26.683048455216138\n ],\n [\n -80.35812377929688,\n 26.683048455216138\n ],\n [\n -80.35812377929688,\n 26.311881633667735\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st St.
Gainesville, Florida 32653
Letterkenny Army Depot in Chambersburg, Pennsylvania, built an Ammonium Perchlorate Rocket Motor Destruction (ARMD) facility in 2016. The ARMD Facility was designed to centralize rocket motor destruction and contain or capture all waste during the destruction process. Ideally, there would be no contaminant transport to air, soil, or water from the facility, but the Code of Federal Regulations requires that any hazardous waste disposal facility have an environmental monitoring program in place. In a study by the U.S. Geological Survey, in cooperation with the Letterkenny Army Depot, baseline characterization of constituents in groundwater, surface water, and soil was conducted from September to December 2016 to document site conditions prior to the beginning of operations at the facility in January 2017. Groundwater wells, surface water, and soils were sampled monthly during the baseline characterization period. No sediment transport from the site occurred on days when samples were collected from surface-water sites, so no sediment was collected from the retention basin at the facility during the baseline period. Data collected during the baseline period can be compared to data collected in future years to determine whether there is any contaminant transport from the ARMD Facility to the surrounding environment.
During the baseline characterization period, monthly samples were collected from 4 groundwater wells and 9 soil sites near the ARMD Facility. The only surface-water site sampled monthly during the baseline period was upgradient from the facility. There was no streamflow at surface-water sites downgradient from the facility on days when surface-water samples were collected during the baseline characterization period.
Groundwater results for the four wells sampled near the ARMD Facility during the baseline period did not show any major water-quality issues. Mean specific conductance (SC) and pH in groundwater ranged from 220 to 771 microsiemens per centimeter at 25 degrees Celsius (μS/cm) and 6.45 to 6.98, respectively. No constituents in groundwater samples exceeded any U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL). Dissolved iron (Fe) was the only groundwater constituent that exceeded a Secondary Maximum Contaminant Level (SMCL) established by the EPA. The SMCL for Fe is 300 micrograms per liter (μg/L); samples from three wells had mean dissolved Fe concentrations ranging from 1,100 to 2,600 μg/L. The only volatile organic compounds (VOCs) detected in groundwater samples were bromomethane, acetone, and chloromethane. All VOC detections in groundwater samples were less than the Reporting Detection Levels (RDLs). These three compounds also were detected in blank samples submitted for groundwater samples. Perchlorate was not detected in any groundwater sample collected during the baseline period.
Surface-water data collected during the baseline period were strictly representative of a stream reach upgradient from the ARMD Facility. Stream discharge ranged from 0.03 to 0.08 cubic feet per second during sample collection. The mean SC and pH were 310 μS/cm and 7.6, respectively. No EPA established MCLs or SMCLs were exceeded for any constituents in samples collected from this upgradient stream. Some VOCs were detected in surface water at less than the RDLs. The VOCs detected in surface water were generally the same VOCs as those detected at less than the RDLs for groundwater. Perchlorate was detected in each sample collected from the stream; the mean concentration was 0.07 μg/L. All perchlorate results were less than the RDL of 0.2 μg/L.
Soil samples collected during the baseline period did not have any constituent concentrations that exceeded any medium-specific concentrations (MSC) or soil screening levels (SSL) established by either the Commonwealth of Pennsylvania or the EPA. The Commonwealth of Pennsylvania calculates MSCs based on either a function of acceptable concentrations in groundwater or based on health concerns if the soil is directly contacted. The EPA derives acceptable concentrations of constituents (SSLs) in soil based on standardized equations combining exposure information assumptions with EPA toxicity data. The EPA calculates SSLs for residential and industrial sites. Soil sites at the ARMD Facility were considered “industrial” for comparative purposes. There was at least one order of magnitude difference between any inorganic constituent concentration detected in soil and the MSC and (or) SSL for that constituent. Four VOCs were detected in soil samples collected during the baseline period. None of the VOCs detected in the soils were within three orders of magnitude of any established MSCs or SSLs. The VOCs detected in soil were dichloromethane (also known as methylene chloride), methyl tert-butyl ether (MTBE), tetrachloroethene, and acetone (only detected once). Dichloromethane was the only VOC detected at greater than the RDLs; concentrations in all soil samples were greater than the RDLs. Dichloromethane concentrations ranged from 1.9 to 50.1 micrograms per kilogram (μg/kg). MTBE was detected in 50 percent of samples collected but all results were less than the RDLs of 1.7 to 2.6 μg/kg. Tetrachloroethene was detected in 20 percent of soil samples collected, with a maximum estimated value of 1.5 μg/kg. Inorganic constituents with the highest concentrations in soil were Fe and aluminum (Al); mean Fe and Al concentrations ranged from 28,700 to 52,400 and 10,300 to 19,800 milligrams per kilogram (mg/kg), respectively. Data collected during the baseline period in 2016 can be compared to future data to determine whether concentrations in water and soils surrounding the facility have shown any changes that could be caused by the facility operation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191094","collaboration":"Prepared in Cooperation with the Letterkenny Army Depot","usgsCitation":"Galeone, D.G., 2019, Baseline environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016: U.S. Geological Survey Open-File Report 2019–1094, 32 p., https://doi.org/10.3133/ofr20191094.","productDescription":"Report: vii; 32 p.; Appendices 1-4","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102807","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":437309,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P973YRPL","text":"USGS data release","linkHelpText":"Quality Control and Soil Quality Data in support of Baseline Environmental Monitoring at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016"},{"id":368210,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix3.xlsx","text":"Appendix 3","size":"16.8 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368211,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix4.xlsx","text":"Appendix 4","size":"32.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368208,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix1.xlsx","text":"Appendix 1","size":"15.5 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368212,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094.pdf","text":"Report","size":"20.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1094"},{"id":368107,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5be05a51e4b0b3fc5cf33502","text":"USGS data release","description":"OFR 2019-1094","linkHelpText":"Quality Control and Soil Quality Data in support of Baseline Environmental Monitoring at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016"},{"id":368209,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix2.xlsx","text":"Appendix 2","size":"22.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368190,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1094/coverthb.jpg"}],"country":"United States","state":"Pennsylvania ","county":"Franklin County","city":"Chambersburg","otherGeospatial":"Letterkenny Army Depot","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.71831512451172,\n 40.0013199623656\n ],\n [\n -77.67333984375,\n 40.0013199623656\n ],\n [\n -77.67333984375,\n 40.03471220877633\n ],\n [\n -77.71831512451172,\n 40.03471220877633\n ],\n [\n -77.71831512451172,\n 40.0013199623656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Digital flood-inundation maps for a 4-mile reach of Nimishillen Creek near North Industry, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping (FIM) Program website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Nimishillen Creek at North Industry, Ohio (station number 03118500). Near-real-time stages at this streamgage can be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated to the current stage-discharge relation at the streamgage on Nimishillen Creek at North Industry and documented high-water marks from the flood of January 12, 2017.
The hydraulic model was then used to compute seven water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 8 to 14 ft, which is from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a geographic information system (GIS) digital elevation model derived from light detection and ranging data to delineate the areas flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts. Forecasts for the USGS streamgage on Nimishillen Creek at North Industry, Ohio are issued as needed during times of high water, but are not routinely available (National Weather Service, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195083","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners","usgsCitation":"Whitehead, M.T., 2019, Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5083, 11 p., https://doi.org/10.3133/sir20195083.\n","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104812","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOVN2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Nimishillen Creek near North Industry, Ohio:"},{"id":368075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5083/sir20195083.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5083"},{"id":368074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5083/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard
Columbus OH 43229–1753
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Threats to our recreational and drinking waters include disease-causing (pathogenic) organisms from fecal contamination and toxins produced by some species of cyanobacteria (cyanotoxins) that can cause acute and (or) chronic illnesses. Because traditional laboratory methods for detecting these threats take too long for prompt public health protection, tools for real-time assessments are needed to protect public health. To address this need, the U.S. Geological Survey is collaborating with State and local partners to develop models that provide real-time estimates of Escherichia coli (E. coli) (for pathogens) and (or) microcystin (for freshwater cyanotoxins) levels at inland and Great Lakes beaches and drinking-water intakes. Model results are then used to inform the public of water-quality conditions in near-real time through the Great Lakes NowCast (https://ny.water.usgs.gov/maps/nowcast/). Behind the scenes, the NowCast provides speed and efficiency for managers by automating data management and standardizing methods among agencies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193061","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A.M., and Zimmerman, T.M., 2019, Real-time assessments of water quality—A nowcast for Escherichia coli and cyanobacterial toxins: U.S. Geological Survey Fact Sheet 2019–3061, 4 p., https://doi.org/10.3133/fs20193061.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111133","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3061/coverthb.jpg"},{"id":368189,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3061/fs20193061.pdf","text":"Report","size":"970 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3061"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard, Suite 100
Columbus, OH 43229
To improve the suitability of the Darwin Core standard for the research and management of alien species, the standard needs to express the native status of organisms, how well established they are and how they came to occupy a location. To facilitate this, we propose: 1. To adopt a controlled vocabulary for the existing Darwin Core term dwc:establishmentMeans 2. To elevate the pathway term from the Invasive Species Pathways extension to become a new Darwin Core term dwc:pathway maintained as part of the Darwin Core standard 3. To adopt a new Darwin Core term dwc:degreeOfEstablishment with an associated controlled vocabulary These changes to the standard will allow users to clearly state whether an occurrence of a species is native to a location or not, how it got there (pathway), and to what extent the species has become a permanent feature of the location. By improving Darwin Core for capturing and sharing these data, we aim to improve the quality of occurrence and checklist data in general and to increase the number of potential uses of these data.
","language":"English","publisher":"Pensoft","doi":"10.3897/biss.3.38084","usgsCitation":"Groom, Q.J., Desmet, P., Reyserhove, L., Adriaens, T., Oldoni, D., Vanderhoeven, S., Baskauf, S.J., Chapman, A., McGeoch, M., Walls, R., Wieczorek, J., Wilson, J.R., Zermoglio, P.F., and Simpson, A., 2019, Improving Darwin Core for research and management of alien species: Biodiversity Information Science and Standards, p. 1-24, https://doi.org/10.3897/biss.3.38084.","productDescription":"38084, 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-103524","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":459572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/biss.3.38084","text":"Publisher Index Page"},{"id":368312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368282,"type":{"id":15,"text":"Index 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