Context
Movement of prey on hydrologically pulsed, spatially heterogeneous wetlands can result in transient, high prey concentrations, when changes in landscape features such as connectivity between flooded areas alternately facilitate and impede prey movement. Predators track and exploit these concentrations, depleting them as they arise.
Objectives
We sought to describe how prey pulses of fish rapidly form and persist on wetland landscapes, while enduring constant consumption by wading birds, without being fully depleted. Specifically, we questioned how is the predator–prey relationship mediated by interactions between animal movement and dynamic landscape connectivity?
Methods
Two models were developed of the predator–prey-landscape system with qualitatively different representations of space, to identify and quantify prey pulsing dynamics that were robust across modeled assumptions. The first included a homogeneous landscape described by simple geometry, and implicit fish movement as wetland volume contracts. The second modeled transverse movement across a heterogeneous landscape, with isolated drying patches.
Results
Both models produced rapid fish prey concentrations as the wetland dried to shallow water depths. These conditions are critical for making prey available to wading birds. Fish were also rapidly depleted by birds, representing daily caloric intake supporting birds. Model 1 provided average estimates across the modeled domain. Model 2 mapped locations of emerging prey hotspots on the landscape through time.
Conclusions
Our models tracked predator, prey, and landscape dynamics in parallel, inducing systems dynamics from empirical observations. Explicit inclusion of dynamic wetland hydrologic connectivity, a key landscape mechanism, allowed for a comprehensive picture of links between landscape dynamics and the adapted predator–prey system.
We apply the Community Ice Sheet Model (CISM2) to determine the extent to which the Last Glacial Maximum (LGM) temperature and precipitation climatologies from the Paleoclimate Modelling Intercomparison Project 3 (PMIP3) simulations support the large North American ice sheets that were prescribed as a boundary condition. We force CISM2 with eight PMIP3 general circulation models (GCMs), and an additional model, GENMOM. Seven GCMs simulate LGM climatologies that support positive surface mass balances of the Laurentide and Cordilleran ice sheets (LIS, CIS) consistent with where ice was prescribed in the GCMs. Two GCMs simulate July temperatures that are too warm to support the ice sheets. Four of the nine CISM2 simulations support ice sheets in Beringia, in absence of prescribed ice in the driving GCMs and in disagreement with geologic evidence that indicates the area remained ice-free during the LGM. We test the sensitivity of our results to a range of snow and ice positive degree-day factors, daily, monthly, and climatological temperature and precipitation inputs, and we evaluate the role of albedo and snow in the simulations. Areas with perennial snow in the GCM simulations correspond well to the presence of ice in the CISM2 simulation. GCMs with unrealistically low surface albedos over the LIS yield simulations that fail to simulate realistic ice sheets.
","language":"English","publisher":"Springer","doi":"10.1007/s00382-019-04663-x","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, Applying the Community Ice Sheet Model to evaluate PMIP3 LGM climatologies over the North American ice sheets: Climate Dynamics, v. 53, p. 2807-2824, https://doi.org/10.1007/s00382-019-04663-x.","productDescription":"18 p.","startPage":"2807","endPage":"2824","ipdsId":"IP-088834","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":366848,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","noUsgsAuthors":false,"publicationDate":"2019-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":769152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":769153,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202540,"text":"70202540 - 2019 - Flooding regimes increase avian predation on wildlife prey in tidal marsh ecosystems","interactions":[],"lastModifiedDate":"2019-03-11T13:00:05","indexId":"70202540","displayToPublicDate":"2019-03-08T10:14:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Flooding regimes increase avian predation on wildlife prey in tidal marsh ecosystems","docAbstract":"Within isolated and fragmented populations, species interactions such as predation can cause shifts in community structure and demographics in tidal marsh ecosystems. It is critical to incorporate species interactions into our understanding when evaluating the effects of sea‐level rise and storm surges on tidal marshes. In this study, we hypothesize that avian predators will increase their presence and hunting activities during high tides when increased inundation makes their prey more vulnerable. We present evidence that there is a relationship between tidal inundation depth and time of day on the presence, abundance, and behavior of avian predators. We introduce predation pressure as a combined probability of predator presence related to water level. Focal surveys were conducted at four tidal marshes in the San Francisco Bay, California where tidal inundation patterns were monitored across 6 months of the winter. Sixteen avian predator species were observed. During high tide at Tolay Slough marsh, ardeids had a 29‐fold increase in capture attempts and 4 times greater apparent success rate compared with low tide. Significantly fewer raptors and ardeids were found on low tides than on high tides across all sites. There were more raptors in December and January and more ardeids in January than in other months. Ardeids were more prevalent in the morning, while raptors did not exhibit a significant response to time of day. Modeling results showed that raptors had a unimodal response to water level with a peak at 0.5 m over the marsh platform, while ardeids had an increasing response with water level. We found that predation pressure is related to flooding of the marsh surface, and short‐term increases in sea levels from high astronomical tides, sea‐level rise, and storm surges increase vulnerability of tidal marsh wildlife.
In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain.
As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study.
Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ (https://doi.org/10.5066/F7P55KJN) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/.
Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000.
Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs.
The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., 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 post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185169","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., and Hayes, L., 2019, Flood-inundation maps for Lake Champlain in Vermont and New York: U.S. Geological Survey Scientific Investigations Report 2018–5169, 14 p., https://doi.org/10.3133/sir20185169. [Supersedes USGS Scientific Investigations Report 2016–5060.]","productDescription":"Report: v, 14 p.; Application Site; Data release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101452","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437545,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S","text":"USGS data release","linkHelpText":"Flood-Inundation Shapefiles and Grids for Lake Champlain in Vermont and New York"},{"id":361774,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html ","linkHelpText":"- Flood Inundation Mapper"},{"id":361771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5169/coverthb.jpg"},{"id":361772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5169/sir20185169.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5169"},{"id":361773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S ","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation shapefiles and grids for Lake Champlain in Vermont and New York"}],"country":"United States","state":"New York, Vermont","county":"Addison, Chittenden, Clinton, Franklin, Grand Isle ","otherGeospatial":"Lake Champlain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.7081,43.5785 ], [ -73.7081,45.0891 ], [ -72.8948,45.0891 ], [ -72.8948,43.5785 ], [ -73.7081,43.5785 ] ] ] } } ] }","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The Florida Coastal Mapping Program is a nascent but highly relevant program that has the potential to greatly enhance the “Blue Economy” of Florida by coordinating and facilitating sea-floor mapping efforts and aligning partner and stakeholder activities for increased efficiency and cost reduction. Sustained acquisition of modern coastal mapping information for Florida may improve management of resources and reduce costs by eliminating redundancy. Economic growth could be aided by improved data to support emerging sectors such as aquaculture and renewable energy.
The present focus of the Florida Coastal Mapping Program is on modern, high-resolution bathymetric and coastal topobathymetric data, which can be immediately used to update navigational charts and identify navigation hazards, provide fundamental baseline data for scientific research, and provide information for use by emergency managers and responders. Derivative products include identifying sand resources for beach nourishment, creating vastly improved models for coastal erosion and flooding, identifying coastal springs, and creating benthic habitat maps. The uses and applications of the data generated could grow over time. The process of creating a steering committee and technical team, conducting an inventory and gaps analysis, soliciting feedback from the stakeholder and partner communities, and developing a prioritization process has provided a framework on which a successful program can develop a sustainable funding strategy that may be an investment the citizens of Florida could benefit from for decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191017","collaboration":"Prepared in cooperation with the Florida Institute of Oceanography, Florida Fish and Wildlife Research Institute, and Florida Department of Environmental Protection","usgsCitation":"Hapke, C.J., Kramer, P.A., Fetherston-Resch, E.H., Baumstark, R.D., Druyor, R., Fredericks, X., and Fitos, E., 2019, Florida Coastal Mapping Program—Overview and 2018 workshop report: U.S. Geological Survey Open-File Report 2019–1017, 19 p., https://doi.org/10.3133/ofr20191017.","productDescription":"vii, 19 p.","numberOfPages":"28","ipdsId":"IP-099357","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":361829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1017/ofr20191017.pdf","text":"Report","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1017"},{"id":361828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1017/coverthb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.00,\n 24.5\n ],\n [\n -80,\n 24.5\n ],\n [\n -80,\n 30.75\n ],\n [\n -88.00,\n 30.75\n ],\n [\n -88.00,\n 24.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 Fourth Street South
St. Petersburg, FL 33701
We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2018.09.004","usgsCitation":"Boyte, S.P., Wylie, B.K., and Major, D.J., 2019, Validating a time series of annual grass percent cover in the sagebrush ecosystem: Rangeland Ecology and Management, v. 72, no. 2, p. 347-359, https://doi.org/10.1016/j.rama.2018.09.004.","productDescription":"13 p.","startPage":"347","endPage":"359","ipdsId":"IP-101002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467831,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2018.09.004","text":"Publisher Index Page"},{"id":437546,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71J98QK","text":"USGS data release","linkHelpText":"A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem"},{"id":361853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":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":758988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Major, Donald J.","contributorId":83405,"corporation":false,"usgs":false,"family":"Major","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":758990,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202528,"text":"70202528 - 2019 - Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars","interactions":[],"lastModifiedDate":"2019-07-23T12:29:25","indexId":"70202528","displayToPublicDate":"2019-03-07T11:15:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars","docAbstract":"Recent robotic missions to Mars have offered new insights into the extent, diversity and habitability of the Martian sedimentary rock record. Since the Curiosity rover landed in Gale crater in August 2012, the Mars Science Laboratory Science Team has explored the origins and habitability of ancient fluvial, deltaic, lacustrine and aeolian deposits preserved within the crater. This study describes the sedimentology of a ca 13 m thick succession named the Pahrump Hills member of the Murray formation, the first thick fine‐grained deposit discovered in situ on Mars. This work evaluates the depositional processes responsible for its formation and reconstructs its palaeoenvironmental setting. The Pahrump Hills succession can be sub‐divided into four distinct sedimentary facies: (i) thinly laminated mudstone; (ii) low‐angle cross‐stratified mudstone; (iii) cross‐stratified sandstone; and (iv) thickly laminated mudstone–sandstone. The very fine grain size of the mudstone facies and abundant millimetre‐scale and sub‐millimetre‐scale laminations exhibiting quasi‐uniform thickness throughout the Pahrump Hills succession are most consistent with lacustrine deposition. Low‐angle geometric discordances in the mudstone facies are interpreted as ‘scour and drape’ structures and suggest the action of currents, such as those associated with hyperpycnal river‐generated plumes plunging into a lake. Observation of an overall upward coarsening in grain size and thickening of laminae throughout the Pahrump Hills succession is consistent with deposition from basinward progradation of a fluvial‐deltaic system derived from the northern crater rim into the Gale crater lake. Palaeohydraulic modelling constrains the salinity of the ancient lake in Gale crater: assuming river sediment concentrations typical of floods on Earth, plunging river plumes and sedimentary structures like those observed at Pahrump Hills would have required lake densities near freshwater to form. The depositional model for the Pahrump Hills member presented here implies the presence of an ancient sustained, habitable freshwater lake in Gale crater for at least ca 103 to 107 Earth years.
","language":"English","publisher":"Wiley","doi":"10.1111/sed.12558","usgsCitation":"Stack, K.M., Grotzinger, J.P., Lamb, M.P., Gupta, S., Rubin, D.M., Kah, L.C., Edgar, L.A., Fey, D.M., Hurowitz, J.A., McBride, M.J., Rivera-Hernandez, F., Sumner, D.Y., Van Beek, J.K., Williams, R.M., and Yingst, R.A., 2019, Evidence for plunging river plume deposits in the Pahrump Hills member of the Murray formation, Gale crater, Mars: Sedimentology, v. 66, no. 5, p. 1768-1801, https://doi.org/10.1111/sed.12558.","productDescription":"34 p.","startPage":"1768","endPage":"1801","ipdsId":"IP-101054","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467833,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/sed.12558","text":"Publisher Index Page"},{"id":361830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stack, Kathryn M. 0000-0003-3444-6695","orcid":"https://orcid.org/0000-0003-3444-6695","contributorId":146791,"corporation":false,"usgs":false,"family":"Stack","given":"Kathryn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":758957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grotzinger, John P.","contributorId":181502,"corporation":false,"usgs":false,"family":"Grotzinger","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":758958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, Michael P.","contributorId":214027,"corporation":false,"usgs":false,"family":"Lamb","given":"Michael","email":"","middleInitial":"P.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":758959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gupta, Sanjeev","contributorId":172302,"corporation":false,"usgs":false,"family":"Gupta","given":"Sanjeev","email":"","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":758960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubin, David M.","contributorId":206587,"corporation":false,"usgs":false,"family":"Rubin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":758961,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kah, Linda C.","contributorId":181497,"corporation":false,"usgs":false,"family":"Kah","given":"Linda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":758962,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":758956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fey, Deirdra M.","contributorId":214028,"corporation":false,"usgs":false,"family":"Fey","given":"Deirdra","email":"","middleInitial":"M.","affiliations":[{"id":36716,"text":"Malin Space Science Systems","active":true,"usgs":false}],"preferred":false,"id":758963,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hurowitz, Joel A.","contributorId":200390,"corporation":false,"usgs":false,"family":"Hurowitz","given":"Joel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":758964,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McBride, Marie J.","contributorId":167693,"corporation":false,"usgs":false,"family":"McBride","given":"Marie","email":"","middleInitial":"J.","affiliations":[{"id":24734,"text":"Malin Space Science Systems, San Diego","active":true,"usgs":false}],"preferred":false,"id":758965,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rivera-Hernandez, Frances","contributorId":203793,"corporation":false,"usgs":false,"family":"Rivera-Hernandez","given":"Frances","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":758966,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sumner, Dawn Y.","contributorId":200403,"corporation":false,"usgs":false,"family":"Sumner","given":"Dawn","email":"","middleInitial":"Y.","affiliations":[],"preferred":false,"id":758967,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Van Beek, Jason K.","contributorId":200399,"corporation":false,"usgs":false,"family":"Van Beek","given":"Jason","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":758968,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Williams, Rebecca M. E.","contributorId":214029,"corporation":false,"usgs":false,"family":"Williams","given":"Rebecca","email":"","middleInitial":"M. E.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":758969,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yingst, R. Aileen","contributorId":214030,"corporation":false,"usgs":false,"family":"Yingst","given":"R.","email":"","middleInitial":"Aileen","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":758970,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70202489,"text":"70202489 - 2019 - Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules","interactions":[],"lastModifiedDate":"2019-06-18T10:39:36","indexId":"70202489","displayToPublicDate":"2019-03-06T11:26:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules","docAbstract":"Atlantic salmon farming is one of the largest aquaculture sectors in the world. A major impact on farm economics, fish welfare, and potentially nearby wild salmonid populations, is the sea louse ectoparasite Lepeophtheirus salmonis. Sea louse infestations are most often controlled through application of chemicals, but in most farming regions sea lice have evolved resistance to the small set of available chemicals. Therefore, alternative treatment methodologies are becoming more widely used. One increasingly common alternative treatment involves the co-culture of farmed salmon with cleaner fish, which prey on sea lice. However, despite their wide use, little is understood about the situations in which cleaner fish are most effective. For example, previous work suggests that a low parasite density results in sea lice finding it difficult to acquire mates, reducing fecundity and population growth. Other work suggests that environmental conditions such as temperature and external sea louse pressure have substantial impact on this mate limitation threshold and may even remove the effect entirely. We use an Agent-Based Model (ABM) to simulate cleaner fish on a salmon farm to explore interactions between sea louse mating behaviour, cleaner fish feeding rate, temperature, and external sea lice pressure. We found that sea louse mating has a substantial effect on sea louse infestations under a variety of environmental conditions. Our results suggest that cleaner fish can control sea louse infestations most effectively by maintaining the population below critical density thresholds.
","language":"English","publisher":"Inter-Research","doi":"10.3354/dao03329","usgsCitation":"McEwan, G.F., Groner, M.L., Cohen, A.A., Imsland, A.K., and Revie, C.W., 2019, Modelling sea lice control by lumpfish on Atlantic salmon farms: interactions with mate limitation, temperature, and treatment rules: Diseases of Aquatic Organisms, v. 133, no. 1, p. 69-82, https://doi.org/10.3354/dao03329.","productDescription":"14 p.","startPage":"69","endPage":"82","ipdsId":"IP-099338","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":467835,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://strathprints.strath.ac.uk/68921/1/McEwan_etal_DoAO_2019_Modelling_sea_lice_control.pdf","text":"External Repository"},{"id":361798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"133","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McEwan, Gregor F.","contributorId":213961,"corporation":false,"usgs":false,"family":"McEwan","given":"Gregor","email":"","middleInitial":"F.","affiliations":[{"id":38940,"text":"Department of Health Management, University of Prince Edward Island, Charlottetown, PE, Canada, C1A 4P3","active":true,"usgs":false}],"preferred":false,"id":758813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":213541,"corporation":false,"usgs":true,"family":"Groner","given":"Maya","email":"","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":758814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cohen, Allegra A. B.","contributorId":213963,"corporation":false,"usgs":false,"family":"Cohen","given":"Allegra","email":"","middleInitial":"A. B.","affiliations":[{"id":38941,"text":"Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA 32611-0570","active":true,"usgs":false}],"preferred":false,"id":758815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imsland, Albert K. D.","contributorId":213964,"corporation":false,"usgs":false,"family":"Imsland","given":"Albert","email":"","middleInitial":"K. D.","affiliations":[{"id":38942,"text":"Akvaplan-niva Iceland Office, Akralind 4, 201 Kópavogur, Iceland","active":true,"usgs":false}],"preferred":false,"id":758816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Revie, Crawford W.","contributorId":213965,"corporation":false,"usgs":false,"family":"Revie","given":"Crawford","email":"","middleInitial":"W.","affiliations":[{"id":38940,"text":"Department of Health Management, University of Prince Edward Island, Charlottetown, PE, Canada, C1A 4P3","active":true,"usgs":false}],"preferred":false,"id":758817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202490,"text":"70202490 - 2019 - Not so normal normals: Species distribution model results are sensitive to choice of climate normals and model type","interactions":[],"lastModifiedDate":"2019-03-06T11:22:40","indexId":"70202490","displayToPublicDate":"2019-03-06T11:22:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5811,"text":"Climate","active":true,"publicationSubtype":{"id":10}},"title":"Not so normal normals: Species distribution model results are sensitive to choice of climate normals and model type","docAbstract":"Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.
","language":"English","publisher":"MDPI","doi":"10.3390/cli7030037","usgsCitation":"Jarnevich, C.S., and Young, N.E., 2019, Not so normal normals: Species distribution model results are sensitive to choice of climate normals and model type: Climate, v. 7, no. 3, p. 1-15, https://doi.org/10.3390/cli7030037.","productDescription":"Article 37; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-073502","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/cli7030037","text":"Publisher Index Page"},{"id":361797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":758818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":189060,"corporation":false,"usgs":false,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":758819,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202492,"text":"70202492 - 2019 - Patterns of mercury and selenium exposure in Minnesota common loons","interactions":[],"lastModifiedDate":"2019-03-06T11:18:40","indexId":"70202492","displayToPublicDate":"2019-03-06T11:18:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of mercury and selenium exposure in Minnesota common loons","docAbstract":"Common loons (Gavia immer) are at risk of elevated dietary mercury (Hg) exposure in portions of their breeding range. To assess the level of risk among loons in Minnesota (USA), we investigated loon blood Hg concentrations in breeding lakes across Minnesota. Loon blood Hg concentrations were regressed on predicted Hg concentrations in standardized 12‐cm whole‐organism yellow perch (Perca flavescens), based on fish Hg records from Minnesota lakes, using the US Geological Survey National Descriptive Model for Mercury in Fish. A linear model, incorporating common loon sex, age, body mass, and log‐transformed standardized perch Hg concentration representative of each study lake, was associated with 83% of the variability in observed common loon blood Hg concentrations. Loon blood Hg concentration was positively related to standardized perch Hg concentrations; juvenile loons had lower blood Hg concentrations than adult females, and blood Hg concentrations of juveniles increased with body mass. Blood Hg concentrations of all adult common loons and associated standardized prey Hg for all loon capture lakes included in the study were well below proposed thresholds for adverse effects on loon behavior, physiology, survival, and reproductive success. The fish Hg modeling approach provided insights into spatial patterns of dietary Hg exposure risk to common loons across Minnesota. We also determined that loon blood selenium (Se) concentrations were positively correlated with Hg concentration. Average common loon blood Se concentrations exceeded the published provisional threshold.
","language":"English","publisher":"Society for Environmental Toxicology and Chemistry (SETAC)","doi":"10.1002/etc.4331","usgsCitation":"Kenow, K.P., Houdek, S.C., Fara, L., Erickson, R.A., Gray, B.R., Harrison, T.J., Monson, B., and Henderson, C.L., 2019, Patterns of mercury and selenium exposure in Minnesota common loons: Environmental Toxicology and Chemistry, v. 38, no. 3, p. 524-532, https://doi.org/10.1002/etc.4331.","productDescription":"9 p.","startPage":"524","endPage":"532","ipdsId":"IP-099038","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437550,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QHT2DG","text":"USGS data release","linkHelpText":"Code to analyze loon blood mercury"},{"id":437549,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96XLGLP","text":"USGS data release","linkHelpText":"Loon mercury models"},{"id":437548,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TDCH3F","text":"USGS data release","linkHelpText":"Patterns of mercury and selenium exposure in Minnesota common loons: Data"},{"id":361796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fara, Luke J. 0000-0002-1143-4395","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":202973,"corporation":false,"usgs":true,"family":"Fara","given":"Luke J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758827,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":758828,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monson, Bruce 0000-0002-3632-2875","orcid":"https://orcid.org/0000-0002-3632-2875","contributorId":211998,"corporation":false,"usgs":false,"family":"Monson","given":"Bruce","email":"","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":758829,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Carrol L.","contributorId":213967,"corporation":false,"usgs":false,"family":"Henderson","given":"Carrol","email":"","middleInitial":"L.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":758830,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202493,"text":"70202493 - 2019 - The area under the precision‐recall curve as a performance metric for rare binary events","interactions":[],"lastModifiedDate":"2019-06-18T10:36:48","indexId":"70202493","displayToPublicDate":"2019-03-06T11:16:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The area under the precision‐recall curve as a performance metric for rare binary events","docAbstract":"Management activities to restore endangered fish species, such as dam removals, fishway installations, and periodic turbine shutdowns, usually decrease hydropower generation capacities at dams. Quantitative analysis of the tradeoffs between energy production and fish population recovery related to dam decision-making is still lacking. In this study, an integrated hydropower generation and age-structured fish population model was developed using a system dynamics modeling method to assess basin-scale energy-fish tradeoffs under eight dam management scenarios. This model ran across 150 years on a daily time step, applied to five hydroelectric dams located in the main stem of the Penobscot River, Maine. We used alewife (Alosa pseudoharengus) to be representative of the local diadromous fish populations to link projected hydropower production with theoretical influences on migratory fish populations on the model river system. Our results show that while the five dams can produce around 427 GWh/year of energy, without fishway installations they would contribute to a 90% reduction in the alewife spawner abundance. The effectiveness of fishway installations is largely influenced by the size of reopened habitat areas and the actual passage rate of the fishways. Homing to natal habitat has an insignificant effect on the growth of the simulated spawner abundance. Operating turbine shutdowns during alewives' peak downstream migration periods, in addition to other dam management strategies, can effectively increase the spawner abundance by 480–550% while also preserving 65% of the hydropower generation capacity. These data demonstrate that in a river system where active hydropower dams operate, a combination of dam management strategies at the basin scale can best balance the tradeoff between energy production and the potential for migratory fish population recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.03.042","usgsCitation":"Song, C., O’Malley, A., Roy, S.G., Zydlewski, J.D., Barber, B.L., and Mo, W., 2019, Managing dams for energy and fish tradeoffs: What does a win-win solution take?: Science of the Total Environment, v. 669, p. 833-843, https://doi.org/10.1016/j.scitotenv.2019.03.042.","productDescription":"11 p.","startPage":"833","endPage":"843","ipdsId":"IP-105717","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467840,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.03.042","text":"Publisher Index Page"},{"id":389809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.7744140625,\n 44.53959000445632\n ],\n [\n -68.3184814453125,\n 44.69599298172069\n ],\n [\n -68.2635498046875,\n 44.883120442385646\n ],\n [\n -68.1317138671875,\n 45.20913363773731\n ],\n [\n -68.18115234375,\n 45.38301927899065\n ],\n [\n -68.40087890624999,\n 45.57175504130605\n ],\n [\n -68.54919433593749,\n 45.69850658738846\n ],\n [\n -68.97216796875,\n 45.67932023569538\n ],\n [\n -69.01611328125,\n 45.57944511437787\n ],\n [\n -69.14794921875,\n 45.17041997262664\n ],\n [\n -69.169921875,\n 44.94536144236941\n ],\n [\n -69.41162109375,\n 44.68818283842486\n ],\n [\n -69.12597656249999,\n 44.453388800301774\n ],\n [\n -68.9117431640625,\n 44.469071224701096\n ],\n [\n -68.7744140625,\n 44.52392653654213\n ],\n [\n -68.7744140625,\n 44.53959000445632\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"669","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Cuihong","contributorId":265998,"corporation":false,"usgs":false,"family":"Song","given":"Cuihong","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":824000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Malley, Andrew","contributorId":169716,"corporation":false,"usgs":false,"family":"O’Malley","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":824001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Samuel G.","contributorId":266000,"corporation":false,"usgs":false,"family":"Roy","given":"Samuel","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":824002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":824003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Betsy L.","contributorId":207173,"corporation":false,"usgs":false,"family":"Barber","given":"Betsy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":824004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mo, Weiwei","contributorId":266002,"corporation":false,"usgs":false,"family":"Mo","given":"Weiwei","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":824005,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202336,"text":"sir20185166 - 2019 - Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016","interactions":[],"lastModifiedDate":"2019-03-06T14:01:08","indexId":"sir20185166","displayToPublicDate":"2019-03-06T07:46:29","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":"2018-5166","displayTitle":"Spatial and Temporal Variability of Harmful Algal Blooms in Milford Lake, Kansas, May through November 2016","title":"Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016","docAbstract":"The U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment (KDHE), completed a study to quantify the spatial and temporal variability of cyanobacterial blooms in Milford Lake, Kansas, over a range of environmental conditions at various time scales (hours to months). A better understanding of the spatial and temporal variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with cyanobacterial harmful algal bloom (CyanoHAB) issues throughout the Nation. Spatial and temporal variability were assessed in the upstream one-third of Milford Lake (designated as “Zone C” by KDHE) during May through November 2016 using a combination of time-lapse photography, continuous water-quality monitors, discrete phytoplankton, chlorophyll, and microcystin samples, and spatially dense near-surface data. Combined, these data were used to characterize variability of cyanobacterial abundance, algal biomass, and microcystin concentrations in Zone C of Milford Lake before, during, and after cyanobacterial blooms in 2016.
Temporal patterns were evaluated during May through November 2016 using time-lapse photography at six locations in Zone C and at a single point location (the Wakefield site) using a combination of discrete and continuously measured water-quality data (including the cyanobacterial pigment phycocyanin). Based on time-lapse photography, CyanoHABs developed in Zone C of Milford Lake in early July and persisted through the end of November. Bloom accumulations at individual sites were dependent on wind direction. After a change in wind direction, it would take about 1 day for accumulations to become visible at different locations. During periods with low wind, accumulations were widespread and visible at all sites. Cyanobacteria were absent from the algal community at the Wakefield site in late May and were a minor component of the community in June; however, by mid-July the cyanobacteria were dominant and remained dominant until early November.
Chlorophyll and microcystin concentrations at the Wakefield site were estimated using sensor-measured phycocyanin based on regression models developed for Zone C. Regression-estimated concentrations likely are more indicative of seasonal patterns in algal biomass (as indicated by chlorophyll concentrations) and microcystin than discretely collected samples because regression-estimated data have a much higher temporal resolution. Based on regression estimates, algal biomass and microcystin concentrations at the Wakefield site steadily increased from May through August. After August, concentrations decreased but remained relatively high compared to May and June. Daily chlorophyll maxima were as much as 400 times higher than daily minima, and daily microcystin maxima were as many as several orders of magnitude higher than daily minima. The extreme variability in algal biomass and microcystin concentrations at the Wakefield site reflects the development and dissipation of blooms, as indicated by the time-lapse cameras.
Based on regression-estimated microcystin concentrations, the KDHE watch and warning thresholds for microcystin were exceeded during mid-June through late November. Exceedance of KDHE advisory thresholds often changed from no advisory to watch or warning over the course of the day because of the variability in algal biomass and microcystin concentrations caused by bloom development and dissipation. Continuous water-quality monitors may be useful in informing public-health decisions in lakes with variable CyanoHAB conditions; however, site-specific models need to be developed, and best practices for using continuous water-quality monitors to inform CyanoHAB management strategies need to be established.
Spatial data were collected on May 26, July 21, and September 15, 2016, using a combination of a boat-mounted array and discrete water-quality samples analyzed for phytoplankton community composition and chlorophyll and microcystin concentrations. Spatial patterns were described using regression-estimated chlorophyll and microcystin concentrations. During the May 26, 2016, spatial surveys, cyanobacterial abundances were relatively low throughout Zone C and did not exceed KDHE guidance values compared to spatial surveys on July 21 and September 15. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass uplake in Zone C, and decreases in the downlake direction towards Zone B. Regression-estimated chlorophyll concentrations also were more variable uplake than downlake. Based on regression estimates, microcystin concentrations did not exceed KDHE guidance values anywhere in Zone C on May 26. Spatial patterns in microcystin throughout Zone C did not match patterns in regression-estimated chlorophyll concentrations, likely because the algal community was not dominated by cyanobacteria at most locations in May.
During the July 21, 2016, spatial surveys, cyanobacterial abundances in Zone C exceeded KDHE guidance values in 50 percent of samples. The algal community in Zone C was dominated by cyanobacteria at all locations except two, where cyanobacteria codominated with diatoms. Both locations where cyanobacteria and diatoms codominated were north of the causeway. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass north of the causeway and on the eastern shore of Zone C. On July 21, algal biomass did not always decrease in the downlake direction. There was a decrease just south of the causeway but an increase shortly after with higher concentrations into Zone B. Spatial maps indicated changes in algal distribution at a 0.5-meter depth, with algae moving to the central part of the lake north of the causeway and along the eastern shore south of the causeway. Most regression-estimated microcystin concentrations on July 21 exceeded KDHE guidance values, reflecting the pervasive bloom conditions in Zone C during this period. Spatial patterns in regression-estimated microcystin concentrations throughout Zone C were similar to patterns seen in discrete samples and regression-estimated chlorophyll concentrations, with higher concentrations north of the causeway and on the east shore of Zone C.
During the September 15, 2016, spatial surveys, cyanobacterial abundances did not exceed KDHE guidance values. The algal community north of the causeway was dominated by diatoms. The algal community throughout the rest of Zone C was dominated by cyanobacteria. Of regression-estimated microcystin concentrations on September 15, 80 percent did not exceed KDHE guidance values. Spatial patterns indicated northward movement of the cyanobacterial bloom consistent with a wind shift noted the previous day. On September 14, winds were generally from the north to northwest, shifting to the south by September 15. There was a northward progression of chlorophyll and microcystin during the spatial surveys. These data, along with the camera data and spatial and wind data from May and July, indicate that wind can be a major driver of the spatial and temporal variability of cyanobacterial blooms in Milford Lake and likely plays a role in the extent and duration of near-shore accumulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185166","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment","usgsCitation":"Foster, G.M., Graham, J.L., and King, L.R., 2019, Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016: U.S. Geological Survey Scientific Investigations Report 2018–5166, 36 p., https://doi.org/10.3133/sir20185166.","productDescription":"Report: vi, 36 p.; Appendixes: 28 p.; Data Releases: 4","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-093516","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":361764,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78S4P4M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, May 25–26, June 8–10, July 20–21, and September 14–15, 2016"},{"id":361765,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KCV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Time-lapse photography of Milford Lake, Kansas, June through November 2016"},{"id":361760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5166/coverthb.jpg"},{"id":361763,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DVX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Milford Lake, Kansas spatial water-quality data, May 26, June 9, July 14, July 21, and September 15, 2016"},{"id":361761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166.PDF","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166"},{"id":361762,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166_appendixes.pdf","text":"Appendix 1 and 2","size":"571 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166 Appendixes 1 and 2"},{"id":361766,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7513XFN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, May through November 2016"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}\n\n\n\n","contact":"Director, Kansas Water Science Center
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1217 Biltmore Drive
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The chaparral-dominated national forests of southern California were in part established to provide water provision services to the surrounding urban populations and irrigation for agriculture. However, water provision in the form of groundwater recharge and surface runoff depends on the climatological conditions of any given year and also landscape-scale disturbances such as fire. Fire is increasing in frequency in southern California and understanding its impacts both immediately postfire and as vegetation recovers, and the interactions between fire and hydrology, are key components to managing federal lands effectively. In this study we focus on nine fires in a study area that encompasses the four southern California national forests (Los Padres, Angeles, San Bernardino, and Cleveland) and use a water balance model to investigate the effects of water provision services post-fire at a regional scale. We found that runoff and recharge increased post-fire, with increases in recharge being greater with recovery times ranging from 2 to 4 y post-fire. Vegetation recovery occurred 2 y post-fire for all basins as indicated by remotely sensed imagery measuring vegetation greenness having returned to or exceeded pre-fire values for the basin. We found that runoff and recharge were more sensitive to the effects of climate than to length of time post-fire. Findings from these modeling tools allow users to anticipate the impact of fire on water provision services in the region and develop management strategies that help reduce the impacts of wildfire.
In the U.S., about 44 million people rely on self-supplied groundwater for drinking water. Because most self-supplied homeowners do not treat their water to control corrosion, drinking water can be susceptible to lead (Pb) contamination from metal plumbing. To assess the types and locations of susceptible groundwater, a geochemical reaction model that included pure Pb minerals and solid solutions of calcite (CaxPb1–xCO3) and apatite [CaxPb5-x(PO4)3(OH; Cl; F)] was developed to estimate the lead solubility potential (LSP) for over 8300 untreated groundwater samples collected from domestic and public-supply sites between 2000 and 2016 in the U.S. The LSP is the calculated amount of Pb metal that could dissolve at 25 °C before a Pb-bearing mineral precipitates. About 33% of untreated groundwater samples had LSP greater than 15 μg/L—the USEPA action level for dissolved plus particulate forms of Pb. Five percent of samples had high LSP (above 300 μg/L) and tended to occur in the eastern and southeastern U.S. Measured Pb concentrations above 15 μg/L were rarely detected (<1%) but always coincided with high LSP values. Future work will provide a better understanding of the relation between water chemistry, Pb-mineral formation, and dissolved Pb concentrations in tap water.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.8b04475","usgsCitation":"Jurgens, B., Parkhurst, D.L., and Belitz, K., 2019, Assessing the lead solubility potential of untreated groundwater of the United States: Environmental Science & Technology, v. 53, no. 6, p. 3095-3103, https://doi.org/10.1021/acs.est.8b04475.","productDescription":"Article: 9 p.; Data Release ","startPage":"3095","endPage":"3103","ipdsId":"IP-083634","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":467842,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.8b04475","text":"Publisher Index 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]\n}","volume":"53","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parkhurst, David L. 0000-0003-3348-1544 dlpark@usgs.gov","orcid":"https://orcid.org/0000-0003-3348-1544","contributorId":1088,"corporation":false,"usgs":true,"family":"Parkhurst","given":"David","email":"dlpark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":770837,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":213728,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":770838,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206406,"text":"70206406 - 2019 - An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions","interactions":[],"lastModifiedDate":"2020-03-27T08:34:48","indexId":"70206406","displayToPublicDate":"2019-03-05T11:51:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions","docAbstract":"Peat cores are valuable archives of past environmental change because they accumulate plant organic matter over millennia. While studies have primarily focused on physical, ecological, and some biogeochemical proxies, cores from peatlands have increasingly been used to interpret hydroclimatic change using stable isotope analyses of cellulose preserved in plant remains. Previous studies indicate that the stable oxygen isotope compositions (δ18O) preserved in alpha cellulose extracted from specific plant macrofossils reflect the δ18O values of past peatland water and thereby provide information on long-term changes in hydrology in response to climate. Oxygen isotope analyses of peat cellulose (δ18Ocellulose) have been successfully developed from peat cores that accumulate the same species for millennia. However, to fully exploit the potential of this proxy in species-diverse fens, studies are needed that account for the isotopic variations caused by changes in dominant species composition. This study assesses variation in δ18O values among peatland plant species and how they relate to environmental waters in two fens informally named Horse Trail and Goldfin, located on the leeward (dry) and windward (wet) side, respectively, of the climatic gradient across the Kenai Peninsula, Alaska. Environmental water δ18O values at both fens reflect unmodified δ18O values of mean annual precipitation, although at Goldfin standing pools were slightly influenced by evaporation. Modern plant [mosses and Carex spp. (sedges)] δ18Ocellulose values indicate that all Carex spp. are higher (~2.5‰) than those of mosses, likely driven by their vascular structure and ecophysiological difference from non-vascular mosses. Moss δ18Ocellulose values within each peatland are similar among the species, and differences appear related to evaporation effects on environmental waters within hummocks and hollows. The plant taxa-environmental water δ18O differences are applied to the previously determined Horse Trail Fen untreated bulk δ18O record. Results include significant changes to inferred millennial-to-centennial scale hydroclimatic trends where dominant taxa shift from moss to Carex spp., indicating that modern calibration datasets are necessary for interpreting stable isotopes from fens, containing a mix of vascular and nonvascular plants. Accounting for isotopic offsets through macrofossil analysis and modern plant-water isotope measurements opens new opportunities for hydroclimatic reconstructions from fen peatlands.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00025","usgsCitation":"Jones, M., Anderson, L., Keller, K., Nash, B., Littell, V., Wooller, M.J., and Jolley, C., 2019, An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions: Frontiers in Earth Science, v. 7, 25, 16 p., https://doi.org/10.3389/feart.2019.00025.","productDescription":"25, 16 p.","ipdsId":"IP-102651","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00025","text":"Publisher Index Page"},{"id":368887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arc Lake, Bear Lake, Bear Mountain Lake, Browse Lake, Headquarters Lake, Horse Trail clearing, Kenai Lake, Lower Ohmer Lake, Portage Lake, Skilak Lake, Summit Lake, Tern Lake, Upper Ohmer Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.578369140625,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 59.9274956808828\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -173.485107421875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.10319489936693\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Miriam 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":201994,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":774422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Katherine 0000-0001-6915-5455","orcid":"https://orcid.org/0000-0001-6915-5455","contributorId":218048,"corporation":false,"usgs":false,"family":"Keller","given":"Katherine","email":"","affiliations":[{"id":39732,"text":"Natural Systems Analysts, Harvard University","active":true,"usgs":false}],"preferred":false,"id":774424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nash, Bailey 0000-0001-6423-2773 bnash@usgs.gov","orcid":"https://orcid.org/0000-0001-6423-2773","contributorId":220192,"corporation":false,"usgs":true,"family":"Nash","given":"Bailey","email":"bnash@usgs.gov","affiliations":[{"id":40146,"text":"Iowa State University, Ames, IA","active":true,"usgs":false},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":774425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Virginia","contributorId":220193,"corporation":false,"usgs":false,"family":"Littell","given":"Virginia","email":"","affiliations":[{"id":40147,"text":"University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":774426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolley, Chelsea","contributorId":220194,"corporation":false,"usgs":false,"family":"Jolley","given":"Chelsea","email":"","affiliations":[{"id":26916,"text":"Brigham Young University, Provo, UT","active":true,"usgs":false}],"preferred":false,"id":774428,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215498,"text":"70215498 - 2019 - Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","interactions":[],"lastModifiedDate":"2020-10-21T15:39:49.079734","indexId":"70215498","displayToPublicDate":"2019-03-05T10:36:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","docAbstract":"Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.
Background
Spatio-temporal patterns of movement can characterize relationships between organisms and their surroundings, and address gaps in our understanding of species ecology, activity budgets, bioenergetics, and habitat resource management. Highly mobile waterfowl, which can exploit resources over large spatial extents, are excellent models to understand relationships between movements and resource usage, landscape interactions and specific habitat needs.
Methods
We tracked 3 species of dabbling ducks with GPS-GSM transmitters in 2015–17 to examine fine-scale movement patterns over 24 h periods (30 min interval), dividing movement pathways into temporally continuous segments and spatially contiguous patches. We quantified distances moved, area used and time allocated across the day, using linear and generalized linear mixed models. We investigated behavior through relationships between these variables.
Results
Movements and space-use were small, and varied by species, sex and season. Gadwall (Mareca strepera) generally moved least (FFDs: 0.5–0.7 km), but their larger foraging patches resulted from longer within-area movements. Pintails (Anas acuta) moved most, were more likely to conduct flights > 300 m, had FFDs of 0.8–1.1 km, used more segments and patches per day that they revisited more frequently, resulting in the longest daily total movements. Females and males differed only during the post-hunt season when females moved more. 23.6% of track segments were short duration (1–2 locations), approximately 1/3 more than would be expected if they occurred randomly, and were more dispersed in the landscape than longer segments. Distance moved in 30 min shortened as segment duration increased, likely reflecting phases of non-movement captured within segments.
Conclusions
Pacific Flyway ducks spend the majority of time using smaller foraging and resting areas than expected or previously reported, implying that foraging areas may be highly localized, and nutrients obtainable from smaller areas. Additionally, movement reductions over time demonstrates behavioral adjustments that represent divergent energetic demands, the detection of which is a key advantage of higher frequency data. Ducks likely use less energy for movement than currently predicted and management, including distribution and configuration of essential habitat, may require reconsideration. Our study illustrates how fine-scale movement data from tracking help understand and inform various other fields of research.
The earthquake activity in Oklahoma and Kansas that began in 2008 reflects the most widespread instance of induced seismicity observed to date. We develop a reservoir model to calculate the hydrologic conditions associated with the activity of 902 saltwater disposal wells injecting into the Arbuckle aquifer. Estimates of basement fault stressing conditions inform a rate‐and‐state friction earthquake nucleation model to forecast the seismic response to injection. Our model replicates many salient features of the induced earthquake sequence, including the onset of seismicity, the timing of the peak seismicity rate, and the reduction in seismicity following decreased disposal activity. We present evidence for variable time lags between changes in injection and seismicity rates, consistent with the prediction from rate‐and‐state theory that seismicity rate transients occur over timescales inversely proportional to stressing rate. Given the efficacy of the hydromechanical model, as confirmed through a likelihood statistical test, the results of this study support broader integration of earthquake physics within seismic hazard analysis.
Catchment models are useful tools to help describe and quantify the sources, transport, and fate of sediment, nutrients, and other constituents in a landscape. Results from catchment models are used to quantify and understand existing conditions and used in restoration efforts by defining areas with highest contributions (hotspots, where actions would be most beneficial) and describing the relative importance of various sources (what types of actions would be most beneficial). In practice, a continuum of models exists from simple empirical models to complex process-driven models, each requiring different types and amounts of information. Each of these models has its strengths and weaknesses, which should be considered when deciding which model to apply to a specific area. In many applications, a combination of models can be either coupled or run in series to help describe how nutrients and sediment are transported from the field to downstream receiving water bodies. In this chapter, we describe the continuum of catchment models that exist and provide information for choosing specific models for various management applications. We then provide examples of catchment models used to address a wide range of scientific and policy driven issues: two models commonly applied in New Zealand (CLUES and GLEAMS) and one model (SPARROW) applied to a large river basin in the United States (Mississippi River Basin).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Lake restoration handbook","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-93043-5_2","usgsCitation":"Parshotam, A., and Robertson, D.M., 2019, Modelling for catchment management, chap. of Lake restoration handbook, p. 25-65, https://doi.org/10.1007/978-3-319-93043-5_2.","productDescription":"41 p.","startPage":"25","endPage":"65","ipdsId":"IP-074204","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-30","publicationStatus":"PW","contributors":{"editors":[{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":758761,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Collier, Kevin J.","contributorId":213943,"corporation":false,"usgs":false,"family":"Collier","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":758762,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Quinn, John M.","contributorId":47469,"corporation":false,"usgs":true,"family":"Quinn","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":758763,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Howard-Williams, Clive","contributorId":213944,"corporation":false,"usgs":false,"family":"Howard-Williams","given":"Clive","email":"","affiliations":[],"preferred":false,"id":758764,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Parshotam, Aroon","contributorId":213925,"corporation":false,"usgs":false,"family":"Parshotam","given":"Aroon","email":"","affiliations":[{"id":38930,"text":"Waikato University, New Zealand","active":true,"usgs":false}],"preferred":false,"id":758703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758702,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}