Study region
The Southern Hills aquifer system in the Louisiana Capital Area Groundwater Conservation District (CAGCD), USA.
Study focus
The Southern Hills aquifer system provides abundant groundwater for public and industrial supplies in the CAGCD. Groundwater depletion, saltwater intrusion, and land subsidence are potential concerns due to prolonged excessive groundwater withdrawals. This study develops a high-fidelity groundwater flow model utilizing a complex unstructured grid to investigate groundwater flow and storage responses to excessive groundwater withdrawals for the Southern Hills aquifer system in the CAGCD. The groundwater model incorporates the Mississippi River alluvial aquifer down to the Miocene sands extending to depths around 1 km.
New hydrological insights
Groundwater modeling results indicate large cones of depression in the Evangeline and Jasper formations in the Baton Rouge area due to prolonged groundwater withdrawals. Low-permeability faults are inferred by significant groundwater level difference across the faults. While local groundwater storage depletion in deeper aquifers is evident, overall estimated groundwater storage changes of the Southern Hills aquifer system in the CAGCD are close to zero in the past two decades, indicating insignificant groundwater storage changes. This is attributed to dominant interactions between the major rivers and the shallower alluvial aquifer. In addition, the simulated groundwater storage changes exhibit patterns similar to those derived by the Gravity Recovery and Climate Experiment (GRACE) model that has been used in evaluation of groundwater depletion in many regional studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2023.101342","usgsCitation":"Chen, Y., Vahdat-Aboueshagh, H., Tsai, F.T., Dausman, A., and Runge, M.C., 2023, Unstructured-grid approach to develop high-fidelity groundwater model to understand groundwater flow and storage responses to excessive groundwater withdrawals in the Southern Hills aquifer system in southeastern Louisiana (USA): Journal of Hydrology: Regional Studies, v. 46, 101342, 22 p., https://doi.org/10.1016/j.ejrh.2023.101342.","productDescription":"101342, 22 p.","ipdsId":"IP-137603","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":444389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2023.101342","text":"Publisher Index Page"},{"id":417579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Southern Hills aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.91720564327883,\n 31.007470903702682\n ],\n [\n -91.91720564327883,\n 30.261535321867598\n ],\n [\n -90.71125532149338,\n 30.261535321867598\n ],\n [\n -90.71125532149338,\n 31.007470903702682\n ],\n [\n -91.91720564327883,\n 31.007470903702682\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Ye-Hong","contributorId":305936,"corporation":false,"usgs":false,"family":"Chen","given":"Ye-Hong","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":874253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vahdat-Aboueshagh, Hamid","contributorId":305937,"corporation":false,"usgs":false,"family":"Vahdat-Aboueshagh","given":"Hamid","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":874254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tsai, Frank T.-C.","contributorId":305938,"corporation":false,"usgs":false,"family":"Tsai","given":"Frank","email":"","middleInitial":"T.-C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":874255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":874256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":874257,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240217,"text":"ofr20221121 - 2023 - Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017– March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs","interactions":[],"lastModifiedDate":"2023-02-23T11:58:55.644522","indexId":"ofr20221121","displayToPublicDate":"2023-02-22T09:06:04","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1121","displayTitle":"Observations of Coastal Circulation, Waves, and Sediment Transport Along West Maui, Hawaiʻi (November 2017– March 2018), and Modeling Effects of Potential Watershed Restoration on Decreasing Sediment Loads to Adjacent Coral Reefs","title":"Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017– March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs","docAbstract":"Terrestrial sediment discharging from watersheds off West Maui, Hawaiʻi, has been documented as a primary stressor to local coral reefs, causing coral reef health to decline. The U.S. Geological Survey acquired and analyzed physical oceanographic and sedimentologic field data off the coast of West Maui to calibrate and validate physics-based, numerical hydrodynamic and sediment transport models of the study area developed by Deltares. These models simulated terrestrial sediment transport and dispersal from West Maui watersheds into coastal waters and how terrestrial sediment affects nearby coral reefs under different oceanographic forcing and watershed restoration scenarios.
Wave energy and near-bed turbidity are positively correlated in the field observations, illustrating a process not captured by the model simulations in which sediment already deposited on the seabed is resuspended by wave action and subsequently transported by prevailing currents. In the model simulations, large waves during flood events led to a decrease in suspended-sediment concentrations. Notably, however, the model results only consider sediment entering coastal waters from five stream sources and do not simulate sediment already present on the seabed.
The model simulations project that the Honokeana and Māhinahina coral reefs would experience the greatest reduction in sediment impacts from theoretical watershed restoration. Additionally, when large waves coincide with flood events, post-storm sedimentation generally decreases in the nearshore region, but increases in the region offshore of the reefs. The measured and modeled sediment dynamics demonstrate a demarcation between the coral reefs sheltered within embayments (Honolua reef) or behind points (Wahikuli reef) and those along the relatively open coastline between Kapalua and Kāʻanapali (Kapalua, Honokeana, Māhinahina, and Honokōwai reefs). The sheltered sites are affected by terrestrial sediment from single stream mouths, where most sediment is delivered within hours of a flood (rain) event. Once this sediment enters the nearshore, it settles out and remains within the reef area for a prolonged period owing to a lack of wave or current-driven bed shear stress. Thus, the primary effect of sediment on the reefs within these sheltered areas is sedimentation. In contrast, coral reefs along the unsheltered (or “open”) section of coastline (between Kapalua and Kāʻanapali) are more exposed to waves and terrestrial sediment from multiple stream sources. At these reefs, fine-grained terrestrial sediment can rarely settle but instead remains in suspension. Thus, even long after a flood event has occurred, these sites chronically experience light attenuation from suspended sediment.
These analyses underscore the importance of understanding how coastal ocean waves and circulation can lead to different sediment dynamics and stressors for coral reefs along the same region of the West Maui coastline. These differing factors indicate that the most effective watershed restoration and mitigation strategies may vary among the different coral reefs and streams. An important next step is to determine how the science of this study can support management goals for these coral reefs: what are target reductions of sedimentation, suspended-sediment concentrations, or the resulting light attenuation? Then, using the coupled hydrodynamic-sediment model, we can examine which watershed restoration scenarios in each stream will best achieve those targets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221121","collaboration":"Prepared in cooperation with the Deltares Impacts of Extreme Weather Strategic Research Program","programNote":"Coastal and the Marine Hazards and Resources Program","usgsCitation":"Storlazzi, C.D., Cheriton, O.M., Cronin, K.M., van der Heijden, L.H., Winter, G., Rosenberger, K.J., Logan, J.B., and McCall, R.T., 2023, Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017–March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs: U.S. Geological Survey Open-File Report 2022–1121, 73 p., https://doi.org/10.3133/ofr20221121.","productDescription":"Report: ix, 73 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-138761","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":412766,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P914LMK2","text":"USGS data release","description":"USGS data release","linkHelpText":"Model parameter input files to compare effects of stream discharge scenarios on sediment deposition and concentrations around coral reefs off west Maui, Hawaii"},{"id":412765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DK9O60","text":"USGS data release","description":"USGS data release","linkHelpText":"Time series data of oceanographic conditions from West Maui, Hawaii, 2017-2018 Coral Reef Circulation and Sediment Dynamics Experiment"},{"id":412764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1121/ofr20221121.pdf","text":"Report","size":"17.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1121"},{"id":412763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1121/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"West Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.72475140864907,\n 20.922050876041368\n ],\n [\n -156.5888533113449,\n 20.922050876041368\n ],\n [\n -156.5888533113449,\n 21.0514971765583\n ],\n [\n -156.72475140864907,\n 21.0514971765583\n ],\n [\n -156.72475140864907,\n 20.922050876041368\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission Street
Santa Cruz, CA 95060
Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post-wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post-wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under-represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post-wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community.
Growing demand for renewable energy has resulted in expansion of energy infrastructure across sagebrush ecosystems of western North America. Geothermal power is an increasingly popular renewable energy source, especially within remote areas, but little is known about the impacts it may have on local wildlife populations. Investigations are warranted given similarities to more conventional surface disturbance activities with well-documented impacts. Using a novel 2-pronged analytical approach, we estimated effects of geothermal energy production activities (hereafter, geothermal) on populations of greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), a species of high conservation concern. First, we applied a before-after-control-impact paired series design at two geothermal sites in Nevada, USA, to estimate absence rates of male sage-grouse from lek sites (breeding grounds) and changes in predicted apparent abundance (
“The Bay Connects us, the Bay reflects us” writes Tom Horton in the book “Turning the Tide—Saving the Chesapeake Bay”. The Chesapeake Bay watershed contains the largest estuary in the United States. The watershed stretches north to Cooperstown, New York, south to Lynchburg and Virginia Beach, Virginia, west to Pendleton County, West Virginia, and east to Seaford, Delaware, and Scranton, Pennsylvania. The watershed is more than 64,000 square miles that contain 150 major rivers and streams, hereafter referred to collectively as streams, that total more than 100,000 miles in length. The watershed contains thousands of smaller creeks and tributaries, large numbers of plants and animals, and, in 2020, more than 18.4 million people. As changes occur in population, land use, and climate within the watershed, so too do the diversity and health of the Bay's ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233003","usgsCitation":"Austin, S.H., Cashman, M.J., Clune, J., Colgin, J.E., Fanelli, R.M., Krause, K.P., Majcher, E.H., Maloney, K.O., Mason, C.A., Moyer, D.L., and Zimmerman, T.M., 2023, Tracking status and trends in seven key indicators of stream health in the Chesapeake Bay watershed: U.S. Geological Survey Fact Sheet 2023–3003, 6 p., https://doi.org/10.3133/fs20233003.","productDescription":"Report: 6 p.; 2 Data Releases","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-139165","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":435438,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98O2HQJ","text":"USGS data release","linkHelpText":"Compilation of multi-agency specific conductance observations for streams within the Chesapeake Bay watershed"},{"id":418602,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92SHG66","text":"USGS data release","linkHelpText":"Compilation of multi-agency water temperature observations for streams within the Chesapeake Bay watershed"},{"id":418601,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3003/images/"},{"id":418600,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3003/fs20233003.XML"},{"id":418599,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233003/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3003"},{"id":418598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3003/fs20233003.pdf","text":"Report","size":"2.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3003"},{"id":418597,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3003/coverthb3.jpg"},{"id":499563,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114382.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1904296875,\n 38.41916639395372\n ],\n [\n -75.223388671875,\n 38.64261790634527\n ],\n [\n -75.35522460937499,\n 38.79690830348427\n ],\n [\n -75.498046875,\n 38.87392853923629\n ],\n [\n -75.5419921875,\n 39.0533181067413\n ],\n [\n -75.662841796875,\n 39.30029918615029\n ],\n [\n -75.750732421875,\n 39.70718665682654\n ],\n [\n -75.6298828125,\n 40.052847601823984\n ],\n [\n -75.69580078125,\n 40.07807142745009\n ],\n [\n -75.95947265625,\n 40.052847601823984\n ],\n [\n -76.0693359375,\n 40.069664523297774\n ],\n [\n -76.058349609375,\n 40.18726672309203\n ],\n [\n -75.9375,\n 40.29628651711716\n ],\n [\n -75.91552734375,\n 40.3549167507906\n ],\n [\n -75.89355468749999,\n 40.47202439692057\n ],\n [\n -76.09130859375,\n 40.56389453066509\n ],\n [\n -76.190185546875,\n 40.64730356252251\n ],\n [\n -76.0693359375,\n 40.75557964275589\n ],\n [\n -75.83862304687499,\n 40.871987756697415\n ],\n [\n -75.76171875,\n 40.91351257612758\n ],\n [\n -75.706787109375,\n 40.95501133048621\n ],\n [\n -75.7177734375,\n 41.071069130806414\n ],\n [\n -75.662841796875,\n 41.1455697310095\n ],\n [\n -75.5419921875,\n 41.13729606112276\n ],\n [\n -75.322265625,\n 41.104190944576466\n ],\n [\n -75.377197265625,\n 41.22824901518529\n ],\n [\n -75.377197265625,\n 41.28606238749825\n ],\n [\n -75.377197265625,\n 41.43449030894922\n ],\n [\n -75.399169921875,\n 41.6154423246811\n ],\n [\n -75.34423828125,\n 41.68111756290652\n ],\n [\n -75.2783203125,\n 41.91045347666418\n ],\n [\n -75.38818359375,\n 42.00848901572399\n ],\n [\n -75.377197265625,\n 42.09007006868398\n ],\n [\n -75.223388671875,\n 42.17968819665961\n ],\n [\n -74.970703125,\n 42.26917949243506\n ],\n [\n -74.8388671875,\n 42.32606244456202\n ],\n [\n -74.520263671875,\n 42.415346114253616\n ],\n [\n -74.278564453125,\n 42.54498667313236\n ],\n [\n -74.322509765625,\n 42.64204079304426\n ],\n [\n -74.410400390625,\n 42.80346172417078\n ],\n [\n -74.68505859374999,\n 42.924251753870685\n ],\n [\n -75.069580078125,\n 42.98053954751642\n ],\n [\n -75.38818359375,\n 42.96446257387128\n ],\n [\n -75.684814453125,\n 42.93229601903058\n ],\n [\n -75.9375,\n 42.87596410238256\n ],\n [\n -76.201171875,\n 42.827638636242284\n ],\n [\n -76.26708984375,\n 42.72280375732727\n ],\n [\n -76.2890625,\n 42.601619944327965\n ],\n [\n -76.2890625,\n 42.52069952914966\n ],\n [\n -76.343994140625,\n 42.415346114253616\n ],\n [\n -76.46484375,\n 42.382894009614034\n ],\n [\n -76.640625,\n 42.431565872579185\n ],\n [\n -76.7724609375,\n 42.39912215986002\n ],\n [\n -76.80541992187499,\n 42.24478535602799\n ],\n [\n -76.88232421875,\n 42.285437007491545\n ],\n [\n -76.9482421875,\n 42.415346114253616\n ],\n [\n -77.04711914062499,\n 42.44778143462245\n ],\n [\n -77.14599609375,\n 42.415346114253616\n ],\n [\n -77.2998046875,\n 42.382894009614034\n ],\n [\n -77.222900390625,\n 42.54498667313236\n ],\n [\n -77.442626953125,\n 42.69858589169842\n ],\n [\n -77.574462890625,\n 42.60970621339408\n ],\n [\n -77.640380859375,\n 42.48830197960227\n ],\n [\n -77.728271484375,\n 42.439674178149424\n ],\n [\n -77.6513671875,\n 42.31793945446847\n ],\n [\n -77.596435546875,\n 42.22851735620852\n ],\n [\n -77.5634765625,\n 42.09007006868398\n ],\n [\n -77.6953125,\n 41.92680320648791\n ],\n [\n -77.9150390625,\n 41.83682786072714\n ],\n [\n -78.0908203125,\n 41.795888098191426\n ],\n [\n -78.453369140625,\n 41.599013054830216\n ],\n [\n -78.453369140625,\n 41.50857729743935\n ],\n [\n -78.42041015625,\n 41.376808565702355\n ],\n [\n -78.3984375,\n 41.21172151054787\n ],\n [\n -78.519287109375,\n 41.054501963290505\n ],\n [\n -78.541259765625,\n 40.9218144123785\n ],\n [\n -78.409423828125,\n 40.713955826286046\n ],\n [\n -78.299560546875,\n 40.55554790286311\n ],\n [\n -78.343505859375,\n 40.48873742102282\n ],\n [\n -78.475341796875,\n 40.30466538259176\n ],\n [\n -78.64013671875,\n 40.06125658140474\n ],\n [\n -78.826904296875,\n 39.9434364619742\n ],\n [\n -78.848876953125,\n 39.80853604144591\n ],\n [\n -78.85986328125,\n 39.715638134796336\n ],\n [\n -78.99169921875,\n 39.69873414348139\n ],\n [\n -79.046630859375,\n 39.64799732373418\n ],\n [\n -79.266357421875,\n 39.436192999314095\n ],\n [\n -79.420166015625,\n 39.2832938689385\n ],\n [\n -79.354248046875,\n 39.26628442213066\n ],\n [\n -79.266357421875,\n 39.232253141714885\n ],\n [\n -79.2333984375,\n 39.155622393423215\n ],\n [\n -79.244384765625,\n 39.01918369029134\n ],\n [\n -79.27734374999999,\n 38.89103282648846\n ],\n [\n -79.398193359375,\n 38.74551518488265\n ],\n [\n -79.661865234375,\n 38.54816542304656\n ],\n [\n -79.683837890625,\n 38.47079371120379\n ],\n [\n -79.727783203125,\n 38.34165619279595\n ],\n [\n -79.815673828125,\n 38.20365531807149\n ],\n [\n -80.04638671875,\n 38.013476231041935\n ],\n [\n -80.17822265625,\n 37.779398571318765\n ],\n [\n -80.2880859375,\n 37.59682400108367\n ],\n [\n -80.4638671875,\n 37.47485808497102\n ],\n [\n -80.694580078125,\n 37.38761749978395\n ],\n [\n -80.771484375,\n 37.23032838760387\n ],\n [\n -80.57373046875,\n 37.26530995561875\n ],\n [\n -80.44189453125,\n 37.309014074275915\n ],\n [\n -80.255126953125,\n 37.31775185163688\n ],\n [\n -80.013427734375,\n 37.3002752813443\n ],\n [\n -79.8486328125,\n 37.23907530202184\n ],\n [\n -79.771728515625,\n 37.18657859524883\n ],\n [\n -79.6728515625,\n 37.07271048132943\n ],\n [\n -79.541015625,\n 37.09900294387622\n ],\n [\n -79.354248046875,\n 37.142803443716836\n ],\n [\n -79.1455078125,\n 37.10776507118514\n ],\n [\n -79.112548828125,\n 37.055177106660814\n ],\n [\n -78.936767578125,\n 36.932330061503144\n ],\n [\n -78.837890625,\n 36.94111143010769\n ],\n [\n -78.662109375,\n 37.055177106660814\n ],\n [\n -78.486328125,\n 37.03763967977139\n ],\n [\n -78.42041015625,\n 36.94111143010769\n ],\n [\n -78.20068359374999,\n 36.96744946416934\n ],\n [\n -77.904052734375,\n 37.03763967977139\n ],\n [\n -77.750244140625,\n 37.081475648860525\n ],\n [\n -77.53051757812499,\n 37.081475648860525\n ],\n [\n -77.354736328125,\n 37.07271048132943\n ],\n [\n -77.069091796875,\n 37.081475648860525\n ],\n [\n -76.959228515625,\n 37.01132594307015\n ],\n [\n -76.893310546875,\n 36.932330061503144\n ],\n [\n -76.871337890625,\n 36.83566824724438\n ],\n [\n -76.849365234375,\n 36.677230602346214\n ],\n [\n -76.7724609375,\n 36.527294814546245\n ],\n [\n -76.629638671875,\n 36.55377524336089\n ],\n [\n -76.46484375,\n 36.589068371399115\n ],\n [\n -76.35498046875,\n 36.48314061639213\n ],\n [\n -76.256103515625,\n 36.57142382346277\n ],\n [\n -76.190185546875,\n 36.66841891894786\n ],\n [\n -76.0693359375,\n 36.65079252503471\n ],\n [\n -75.9375,\n 36.66841891894786\n ],\n [\n -75.948486328125,\n 36.76529191711624\n ],\n [\n -75.904541015625,\n 37.01132594307015\n ],\n [\n -75.926513671875,\n 37.17782559332976\n ],\n [\n -75.882568359375,\n 37.42252593456307\n ],\n [\n -75.618896484375,\n 37.640334898059486\n ],\n [\n -75.509033203125,\n 37.82280243352756\n ],\n [\n -75.38818359375,\n 38.013476231041935\n ],\n [\n -75.16845703124999,\n 38.272688535980976\n ],\n [\n -75.1904296875,\n 38.41916639395372\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Introduction: Understanding how abundance, productivity and distribution of individual species may respond to climate change is a critical first step towards anticipating alterations in marine ecosystem structure and function, as well as developing strategies to adapt to the full range of potential changes.
Methods: This study applies the NOAA (National Oceanic and Atmospheric Administration) Fisheries Climate Vulnerability Assessment method to 64 federally-managed species in the California Current Large Marine Ecosystem to assess their vulnerability to climate change, where vulnerability is a function of a species’ exposure to environmental change and its biological sensitivity to a set of environmental conditions, which includes components of its resiliency and adaptive capacity to respond to these new conditions.
Results: Overall, two-thirds of the species were judged to have Moderate or greater vulnerability to climate change, and only one species was anticipated to have a positive response. Species classified as Highly or Very Highly vulnerable share one or more characteristics including: 1) having complex life histories that utilize a wide range of freshwater and marine habitats; 2) having habitat specialization, particularly for areas that are likely to experience increased hypoxia; 3) having long lifespans and low population growth rates; and/or 4) being of high commercial value combined with impacts from non-climate stressors such as anthropogenic habitat degradation. Species with Low or Moderate vulnerability are either habitat generalists, occupy deep-water habitats or are highly mobile and likely to shift their ranges.
Discussion: As climate-related changes intensify, this work provides key information for both scientists and managers as they address the long-term sustainability of fisheries in the region. This information can inform near-term advice for prioritizing species-level data collection and research on climate impacts, help managers to determine when and where a precautionary approach might be warranted, in harvest or other management decisions, and help identify habitats or life history stages that might be especially effective to protect or restore.
Conservation decisions are often made in the face of uncertainty because the urgency to act can preclude delaying management while uncertainty is resolved. In this context, adaptive management is attractive, allowing simultaneous management and learning. An adaptive program design requires the identification of critical uncertainties that impede the choice of management action. Quantitative evaluation of critical uncertainty, using the expected value of information, may require more resources than are available in the early stages of conservation planning. Here, we demonstrate the use of a qualitative index to the value of information (QVoI) to prioritize which sources of uncertainty to reduce regarding the use of prescribed fire to benefit Eastern Black Rails (Laterallus jamaicensis jamaicensis), Yellow Rails (Coterminous noveboracensis), and Mottled Ducks (Anas fulvigula; hereafter, focal species) in high marshes of the U.S. Gulf of Mexico. Prescribed fire has been used as a management tool in Gulf of Mexico high marshes throughout the last 30+ years; however, effects of periodic burning on the focal species and the optimal conditions for burning marshes to improve habitat remain unknown. We followed a structured decision-making framework to develop conceptual models, which we then used to identify sources of uncertainty and articulate alternative hypotheses about prescribed fire in high marshes. We used QVoI to evaluate the sources of uncertainty based on their magnitude, relevance for decision making, and reducibility. We found that hypotheses related to the optimal fire return interval and season were the highest priorities for study, whereas hypotheses related to predation rates and interactions among management techniques were lowest. These results suggest that learning about the optimal fire frequency and season to benefit the focal species might produce the greatest management benefit. In this case study, we demonstrate that QVoI can help managers decide where to apply limited resources to learn which specific actions will result in a higher likelihood of achieving the desired management objectives. Further, we summarize the strengths and limitations of QVoI and outline recommendations for its future use for prioritizing research to reduce uncertainty about system dynamics and the effects of management actions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2824","usgsCitation":"Stantial, M.L., Lawson, A.J., Fournier, A., Kappes, P.J., Kross, C.S., Runge, M.C., Woodrey, M.S., and Lyons, J.E., 2023, Qualitative value of information provides a transparent and repeatable method for identifying critical uncertainty: Ecological Applications, v. 33, no. 4, e2824, 15 p., https://doi.org/10.1002/eap.2824.","productDescription":"e2824, 15 p.","ipdsId":"IP-138568","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":444400,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2824","text":"Publisher Index Page"},{"id":435440,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95OCH4K","text":"USGS data release","linkHelpText":"Qualitative value of information for the effects of prescribed fire in Gulf of Mexico marshes: Expert judgment scores from a 2020 adaptive management workshop"},{"id":413613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Stantial, Michelle L 0000-0003-1112-2903","orcid":"https://orcid.org/0000-0003-1112-2903","contributorId":291453,"corporation":false,"usgs":true,"family":"Stantial","given":"Michelle","email":"","middleInitial":"L","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":865361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawson, Abigail Jean 0000-0002-2799-8750","orcid":"https://orcid.org/0000-0002-2799-8750","contributorId":276319,"corporation":false,"usgs":true,"family":"Lawson","given":"Abigail","email":"","middleInitial":"Jean","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fournier, Auriel 0000-0002-8530-9968","orcid":"https://orcid.org/0000-0002-8530-9968","contributorId":261669,"corporation":false,"usgs":false,"family":"Fournier","given":"Auriel","email":"","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":865363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kappes, Peter J.","contributorId":275193,"corporation":false,"usgs":false,"family":"Kappes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":865364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kross, Chelsea S. 0000-0003-4959-2556","orcid":"https://orcid.org/0000-0003-4959-2556","contributorId":302753,"corporation":false,"usgs":false,"family":"Kross","given":"Chelsea","email":"","middleInitial":"S.","affiliations":[{"id":65542,"text":"Forbes Biological Station–Bellrose Waterfowl Research Center, Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":865365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865366,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woodrey, Mark S.","contributorId":259212,"corporation":false,"usgs":false,"family":"Woodrey","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":865367,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865368,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243283,"text":"70243283 - 2023 - An examination of soil crusts on the floor of Jezero crater, Mars","interactions":[],"lastModifiedDate":"2023-10-11T15:24:01.859907","indexId":"70243283","displayToPublicDate":"2023-02-21T07:11:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5718,"text":"Journal of Geophysical Research: Planets","onlineIssn":"2169-9100","active":true,"publicationSubtype":{"id":10}},"title":"An examination of soil crusts on the floor of Jezero crater, Mars","docAbstract":"Martian soils are critically important for understanding the history of Mars, past potentially habitable environments, returned samples, and future human exploration. This paper examines soil crusts on the floor of Jezero crater encountered during initial phases of the Mars 2020 mission. Soil surface crusts have been observed on Mars at other locations, starting with the two Viking Lander missions. Rover observations show that soil crusts are also common across the floor of Jezero crater, revealed in 45 of 101 locations where rover wheels disturbed the soil surface, 2 out of 7 helicopter flights that crossed the wheel tracks, and 4 of 8 abrasion/drilling sites. Most soils measured by the SuperCam laser-induced breakdown spectroscopy (LIBS) instrument show high hydrogen content at the surface, and fine-grained soils also show a visible/near infrared (VISIR) 1.9 µm H2O absorption feature. The Planetary Instrument for X-ray Lithochemistry (PIXL) and SuperCam observations suggest the presence of salts at the surface of rocks and soils. The correlation of S and Cl contents with H contents in SuperCam LIBS measurements suggests that the salts present are likely hydrated. On the “Naltsos” target, magnesium and sulfur are correlated in PIXL measurements, and Mg is tightly correlated with H at the SuperCam points, suggesting hydrated Mg-sulfates. Mars Environmental Dynamics Analyzer (MEDA) observations indicate possible frost events and potential changes in the hydration of Mg-sulfate salts. Jezero crater soil crusts may therefore form by salts that are hydrated by changes in relative humidity and frost events, cementing the soil surface together.
To constrain fault processes and hazard, fault slip rates may be extrapolated over different fault lengths or time intervals. Here, we investigate slip rates for the Cucamonga Fault (CF). The CF is located at the junction of the Transverse Range fault system with the San Andreas and San Jacinto Faults, and it is hypothesized to connect with these faults, promoting the propagation of large, multi-fault earthquakes. Previous work has shown that CF displacements on late Quaternary alluvial fan surfaces are highly variable along strike. We present two new 10Be surface exposure ages from depth profiles on the alluvial fans. Slip rates are consistent with a rate of 1.4 ± 0.3 m/kyr over time intervals of ∼20, ∼30, and ∼40 kyr. If the CF participates in multi-fault ruptures, then these earthquakes occur either rarely or with sufficient regularity to maintain apparently steady rates over multiple intervals. We also explore along-strike fault displacement variability using a calibrated morphological model. The model successfully reproduces scarp profiles and indicates that fault displacement variability can be explained in part by scarp age but not uplift rate. We infer that both erosion by ephemeral gullying and distributed deformation contribute to fault displacement variability, although both are difficult to detect confidently without excavations across the scarp. These investigations show that better characterization of cumulative-slip variability along strike may improve accuracy and precision of slip rates. Slip rates that do not consider epistemic uncertainties may not be suitable for extrapolation over longer fault sections.
The Kirtland’s Warbler (Setophaga kirtlandii) is a formerly endangered habitat specialist that breeds mainly in young jack pine (Pinus banksiana) forests in northern Lower Michigan, USA. The species is conservation-reliant and depends on habitat management. Management actions have primarily focused on creating jack pine plantations, but the species also breeds in red pine (Pinus resinosa) plantations in central Wisconsin, USA. However, the plantations were not intended as breeding habitat and have suboptimal pine densities. While nesting success is similar between low-density red pine plantations and optimal jack pine habitat, it is not clear if low-density red pine plantations support high fledging survival. If high-quality nesting and post-fledging habitat are not synonymous, fledgling survival and breeding population recruitment may be low. We characterized survival, habitat use, and movement patterns of dependent Kirtland’s Warbler fledglings in Wisconsin red pine plantations and compared fledgling survival between Wisconsin and Michigan. Mayfield cumulative survival estimates at 30 days post-fledging were 0.20 for Wisconsin fledglings and 0.43–0.78 for Michigan fledglings. Logistic exposure cumulative survival estimates for Wisconsin fledglings were 0.23–0.34 at 30 days post-fledging. Fledglings in Wisconsin used areas where vegetation cover and density of red and jack pine were high relative to available areas but not at greater proportions than what was available. Our findings demonstrate that red pine plantations with low pine densities were not equally suitable as nesting and post-fledging habitat, as fledgling survival rates were low. We hypothesize that reduced habitat structure, and not particular pine species, likely contributed to reduced fledgling survival in Wisconsin. Thus, we recommend including red pine as a component in managed Kirtland’s Warbler habitat only if tree densities approach optimal levels.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duad007","usgsCitation":"Olah, A., Ribic, C., Grveles, K., Sarah Warner, Lopez, D., and Pidgeon, A., 2023, Low Kirtland’s Warbler fledgling survival in Wisconsin plantations relative to Michigan plantations: Ornithological Applications, v. 125, no. 2, duad007, 14 p., https://doi.org/10.1093/ornithapp/duad007.","productDescription":"duad007, 14 p.","ipdsId":"IP-112246","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502487,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":497500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"UnitedStates","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.8647407408813,\n 46.932668741089174\n ],\n [\n -92.99728980951639,\n 45.271242799734125\n ],\n [\n -90.7763900544374,\n 42.51884657701703\n ],\n [\n -87.9367256610007,\n 42.480677770787175\n ],\n [\n -87.40779088771117,\n 41.730422434317475\n ],\n [\n -83.06900912932046,\n 41.73440594813022\n ],\n [\n -82.1865330449237,\n 43.461406990458656\n ],\n [\n -83.90901134263669,\n 46.70834799532852\n ],\n [\n -87.92805157633859,\n 47.84390383588131\n ],\n [\n -91.8647407408813,\n 46.932668741089174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Olah, Ashley","contributorId":363795,"corporation":false,"usgs":false,"family":"Olah","given":"Ashley","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":952030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grveles, Kim","contributorId":348828,"corporation":false,"usgs":false,"family":"Grveles","given":"Kim","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":952032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sarah Warner","contributorId":363797,"corporation":false,"usgs":false,"family":"Sarah Warner","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":952033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez, Davin","contributorId":363798,"corporation":false,"usgs":false,"family":"Lopez","given":"Davin","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":952034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pidgeon, Anna M.","contributorId":298926,"corporation":false,"usgs":false,"family":"Pidgeon","given":"Anna M.","affiliations":[{"id":64735,"text":"Univ of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952035,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240758,"text":"70240758 - 2023 - The 2013−2020 seismic activity at Sabancaya Volcano (Peru): Long lasting unrest and eruption","interactions":[],"lastModifiedDate":"2023-02-21T01:45:59.585247","indexId":"70240758","displayToPublicDate":"2023-02-20T19:36:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The 2013−2020 seismic activity at Sabancaya Volcano (Peru): Long lasting unrest and eruption","docAbstract":"Sabancaya volcano is the youngest and second most active volcano in Peru. It is part of the Ampato-Sabancaya volcanic complex which sits to the south of the ancient Hualca Hualca volcano and several frequently active faults, thus resulting in complex volcano-tectonic interactions. After 15 years of repose, in 2013, a series of 4 earthquakes with magnitude >4.5 occurred within 24 h, marking the beginning of a new episode of unrest. Several additional swarms of earthquakes occurred in the following years until magmatic eruptive activity started on 6 November 2016. This activity is ongoing as of this writing, with an average of 50 explosions per day. In this study, we present results of multiparametric monitoring of Sabancaya's activity observed during 2013–2020. Seismic data are used to create a one-dimensional seismic velocity model, to catalog, locate, and characterize earthquakes, to detect repeating earthquake families, and to monitor seismic velocity variations by ambient noise cross-correlation. These analyses are complemented by visual and remote sensing observations and ground deformation measurements. All monitored parameters showed significant changes on 6 November 2016, the day of eruption onset, thus dividing the eruptive activity into pre-eruptive and eruptive stages.
The unrest is characterized by high levels of seismic activity with hundreds of events detected per day. Volcano-tectonic (VT) earthquakes were dominant during the pre-eruptive period while long-period (LP) events and explosions have been most numerous since the eruption onset. Earthquake locations highlight long-lasting seismogenic zones along multiple previously active regional faults, as well as along newly identified faults. This VT seismicity is mainly distributed in a sector from the northwest to the east of the volcanic complex at distances of up to 30 km from the crater. We focus our analysis on two eruptive episodes: the eruption onset and subsequent crater migration from south to north, and the increase of lava dome extrusion rate in 2019. Both episodes are accompanied by seismic velocity decreases of up to 0.2% and are preceded by a few weeks by bursts of distal VT activity, including numerous repeating earthquakes. These repeated events were located on several remote tectonic faults (5–25 km from the vent). We suggest that these phenomena could be due to the injection of a batch of magma in the deep reservoir and/or conduit, which would generate 1) a pressure wave propagating in the hydrothermal system, triggering the bursts of seismic activity and 2) slow rising of magma by melting old material filling the conduit that eventually produced the eruptive and dome growth acceleration events.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2023.107767","usgsCitation":"Machacca, R., Lesage, P., Tavera, H., Pesicek, J., Caudron, C., Torres, J., Puma, N., Vargas, K., Lazarte, I., Rivera, M., and Burgisser, A., 2023, The 2013−2020 seismic activity at Sabancaya Volcano (Peru): Long lasting unrest and eruption: Journal of Volcanology and Geothermal Research, v. 435, 107767, 21 p., https://doi.org/10.1016/j.jvolgeores.2023.107767.","productDescription":"107767, 21 p.","ipdsId":"IP-149045","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444409,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2023.107767","text":"Publisher Index Page"},{"id":413229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Andes Mountains, Sabancaya Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.88759125278847,\n -15.796325861968512\n ],\n [\n -71.87969027972629,\n -15.806737845832032\n ],\n [\n -71.86380245345819,\n -15.818058204597975\n ],\n [\n -71.82223646473676,\n -15.826238215481283\n ],\n [\n -71.81244612854955,\n -15.800540300892976\n ],\n [\n -71.82300938601416,\n -15.781120098585575\n ],\n [\n -71.84559586335715,\n -15.77459118709939\n ],\n [\n -71.87101638538533,\n -15.778971366114547\n ],\n [\n -71.88759125278847,\n -15.796325861968512\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"435","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Machacca, Roger","contributorId":302572,"corporation":false,"usgs":false,"family":"Machacca","given":"Roger","email":"","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lesage, P.","contributorId":302573,"corporation":false,"usgs":false,"family":"Lesage","given":"P.","email":"","affiliations":[{"id":63992,"text":"Université Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":864725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tavera, H.","contributorId":302574,"corporation":false,"usgs":false,"family":"Tavera","given":"H.","email":"","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pesicek, J.D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":72233,"corporation":false,"usgs":true,"family":"Pesicek","given":"J.D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":864727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caudron, C.","contributorId":302575,"corporation":false,"usgs":false,"family":"Caudron","given":"C.","affiliations":[{"id":65511,"text":"Université libre de Bruxelles","active":true,"usgs":false}],"preferred":false,"id":864728,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torres, J.L.","contributorId":302576,"corporation":false,"usgs":false,"family":"Torres","given":"J.L.","email":"","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864729,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Puma, N.","contributorId":302577,"corporation":false,"usgs":false,"family":"Puma","given":"N.","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864730,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vargas, K.","contributorId":302578,"corporation":false,"usgs":false,"family":"Vargas","given":"K.","email":"","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864731,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lazarte, I.","contributorId":302579,"corporation":false,"usgs":false,"family":"Lazarte","given":"I.","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864732,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rivera, M.","contributorId":302580,"corporation":false,"usgs":false,"family":"Rivera","given":"M.","email":"","affiliations":[{"id":65510,"text":"Instituto Geofísico del Perú","active":true,"usgs":false}],"preferred":false,"id":864733,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Burgisser, Alain","contributorId":152269,"corporation":false,"usgs":false,"family":"Burgisser","given":"Alain","email":"","affiliations":[{"id":18894,"text":"Universite de Savoie- CNRS, ISTerre","active":true,"usgs":false}],"preferred":false,"id":864753,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240759,"text":"70240759 - 2023 - Pelagic food web interactions in a large invaded ecosystem: Implications for reintroducing a native top predator","interactions":[],"lastModifiedDate":"2023-06-09T15:08:17.577235","indexId":"70240759","displayToPublicDate":"2023-02-20T19:24:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Pelagic food web interactions in a large invaded ecosystem: Implications for reintroducing a native top predator","docAbstract":"A series of species introductions, overexploitation, and habitat modification preceded the extirpation of Lahontan cutthroat trout (Oncorhynchus clarkii henshawi; LCT), historically the apex predator, from Lake Tahoe, California-Nevada, USA. Studies evaluating limiting factors for LCT emphasise the need to elucidate food web interactions, yet important knowledge gaps regarding trophic interactions among nonnative pelagic fishes and invertebrates remain. We quantified the abundance and consumption demand of planktivores with an emphasis on kokanee (Oncorhynchus nerka) and Mysis diluviana. We synthesised this new information with existing information for lake trout (Salvelinus namaycush). The seasonal supply of copepods satisfied the consumption demand of kokanee, but only supported low feeding and growth rates. Kokanee relied heavily on Mysis as prey, an unusual result. Mysis exhibited a high degree of herbivory initially followed by heavier consumption on copepods by larger individuals. Consumption demand for Mysis on copepods exceeded that of kokanee during all seasons. Mysis contributed to over 50% of the annual energy budget for lake trout up to 625 mm. Consumption of Mysis by lake trout and kokanee represented a significant source of mortality when compared to the production of Mysis. Predation on kokanee was sustainable, only involved lake trout >625 mm, and was focused on prespawning aggregations. Despite the presence of Mysis-fueled lake trout, kokanee have persisted; a noteworthy pattern when considering the negative responses of kokanee to nonnative lake trout and Mysis observed elsewhere. This pattern suggests that there may still be an effective niche for LCT in the invaded Lake Tahoe ecosystem.
","language":"English","publisher":"John Wiley & Sons, Inc.","doi":"10.1111/eff.12706","usgsCitation":"Hansen, A.G., McCoy, A., Thiede, G., and Beauchamp, D., 2023, Pelagic food web interactions in a large invaded ecosystem: Implications for reintroducing a native top predator: Ecology of Freshwater Fish, v. 32, no. 3, p. 552-570, https://doi.org/10.1111/eff.12706.","productDescription":"19 p.","startPage":"552","endPage":"570","ipdsId":"IP-147438","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499263,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12706","text":"Publisher Index Page"},{"id":413228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.9928593695287,\n 38.91420593218916\n ],\n [\n -119.94067431093485,\n 38.9505270086465\n ],\n [\n -119.9420476019504,\n 38.99003174862847\n ],\n [\n -119.93655443788818,\n 39.05404664405532\n ],\n [\n -119.92831469179416,\n 39.065776420173194\n ],\n [\n -119.9310612738256,\n 39.10521681856693\n ],\n [\n -119.94479418398184,\n 39.1148070943255\n ],\n [\n -119.92144823671603,\n 39.15102524650783\n ],\n [\n -119.91870165468492,\n 39.17977344901294\n ],\n [\n -119.92419481874748,\n 39.245743362733606\n ],\n [\n -119.99560595155981,\n 39.25850457367247\n ],\n [\n -120.00933886171606,\n 39.23829825056396\n ],\n [\n -120.02169848085674,\n 39.243616268460954\n ],\n [\n -120.06976366640359,\n 39.245743362733606\n ],\n [\n -120.09585619570052,\n 39.22553336277909\n ],\n [\n -120.10684252382566,\n 39.19893238916717\n ],\n [\n -120.15078783632549,\n 39.174450594299515\n ],\n [\n -120.15353441835695,\n 39.15528498086624\n ],\n [\n -120.17413378359132,\n 39.13504894619777\n ],\n [\n -120.16726732851319,\n 39.11587259996395\n ],\n [\n -120.17413378359132,\n 39.096691033476816\n ],\n [\n -120.16314745546619,\n 39.068975111804775\n ],\n [\n -120.13156176210668,\n 39.060444945335746\n ],\n [\n -120.13293505312258,\n 39.041248302223124\n ],\n [\n -120.12606859804447,\n 39.00710805468577\n ],\n [\n -120.10684252382566,\n 38.99323386992151\n ],\n [\n -120.10272265077865,\n 38.96761284332854\n ],\n [\n -120.12606859804447,\n 38.951594994011174\n ],\n [\n -120.09722948671609,\n 38.924890532506595\n ],\n [\n -120.07113695741916,\n 38.93557352390823\n ],\n [\n -120.03543139101299,\n 38.9291639221442\n ],\n [\n -120.01620531679418,\n 38.91741148121298\n ],\n [\n -119.9928593695287,\n 38.91420593218916\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Adam G.","contributorId":197415,"corporation":false,"usgs":false,"family":"Hansen","given":"Adam","email":"","middleInitial":"G.","affiliations":[{"id":34919,"text":"Colorado Parks and Wildlife, 317 West Prospect Road, Fort Collins, Colorado 80526, USA","active":true,"usgs":false}],"preferred":false,"id":864734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Allison","contributorId":302581,"corporation":false,"usgs":false,"family":"McCoy","given":"Allison","email":"","affiliations":[{"id":65512,"text":"Washington Cooperative Fish and Wildlife Research Unit, School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, Washington 98195, USA","active":true,"usgs":false}],"preferred":false,"id":864735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiede, Gary P.","contributorId":302582,"corporation":false,"usgs":false,"family":"Thiede","given":"Gary P.","affiliations":[{"id":65513,"text":"Department of Watershed Science and The Ecology Center, Utah State University, Logan, Utah 84322, USA","active":true,"usgs":false}],"preferred":false,"id":864736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":864737,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240761,"text":"70240761 - 2023 - Flow–recruitment relationships for Shoal Chub and implications for managing environmental flows","interactions":[],"lastModifiedDate":"2023-11-07T14:56:20.584618","indexId":"70240761","displayToPublicDate":"2023-02-20T16:04:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Flow–recruitment relationships for Shoal Chub and implications for managing environmental flows","docAbstract":"Regulation of river flow regimes by dams and diversions impacts aquatic biota and ecosystems globally. However, our understanding of the ecological consequences of flow alteration and ecological benefits of flow restoration lags behind our ability to manipulate flows, and there is a need for broader development of flow–ecology relationships. Approaches for establishing flow–ecology relationships have recently shifted away from state-based methods that analyze snapshots of ecological conditions and towards rate-based methods focused on mechanisms that link hydrology with dynamics of important ecological components and processes.
We used a rate-based approach to validate environmental flow standards developed for the lower Brazos River, Texas, by analyzing the relationship between flow regime components and recruitment strength of imperiled Shoal Chub Macrhybopsis hyostoma, a fluvial specialist and pelagic-broadcast-spawning fish. We collected 254 age-0 Shoal Chub (9–40 mm total length), extracted their otoliths to estimate age in days, and used a generalized additive model to regress the number of captured recruits that hatched on a calendar date against flow regime metrics, such as pulse magnitude, flow rate of change, and pulse timing in relation to environmental flow standards proposed by a science advisory committee (Brazos Basin and Bay Area Expert Science Team).
The model revealed that flow magnitude, rate of change, and timing were all significant predictors that collectively explained 60% of variation in the recruitment strength index. Hindcasting for 1919–2020 showed a general reduction in recruitment strength following commencement of flow regulation in the lower Brazos River and revealed that high recruitment correlated with years in which most or all proposed flow tiers were attained, whereas low recruitment correlated with years when less than half of the targeted tiers were attained.
Our work represents an effective validation method for environmental flow recommendations and reveals specific flow regimes that benefit an imperiled fish species.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10837","usgsCitation":"Perkin, J., Acre, M.R., Ellard, J.K., Rodger, A.W., Trungale, J., Winemiller, K.O., and Yancy, L.E., 2023, Flow–recruitment relationships for Shoal Chub and implications for managing environmental flows: North American Journal of Fisheries Management, v. 43, no. 5, p. 1260-1275, https://doi.org/10.1002/nafm.10837.","productDescription":"16 p.","startPage":"1260","endPage":"1275","ipdsId":"IP-137319","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":413226,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Brazos River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.55664204856129,\n 34.891337132196966\n ],\n [\n -103.67919574341136,\n 34.90431733940822\n ],\n [\n -103.67460945809466,\n 33.88709425240866\n ],\n [\n -100.07733765481305,\n 32.32672893624452\n ],\n [\n -97.58389153733157,\n 30.525918073387786\n ],\n [\n -96.49599579297191,\n 28.1805962671085\n ],\n [\n -95.17251974775999,\n 28.869046167859096\n ],\n [\n -96.08662137570019,\n 30.375252554772146\n ],\n [\n -96.7579178547347,\n 31.664902948076772\n ],\n [\n -99.43674912606912,\n 33.62884152650054\n ],\n [\n -101.21968168739465,\n 34.14353705447044\n ],\n [\n -103.55664204856129,\n 34.891337132196966\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":864741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":864742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellard, Johnathan K.","contributorId":302585,"corporation":false,"usgs":false,"family":"Ellard","given":"Johnathan","email":"","middleInitial":"K.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":864743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodger, Anthony W.","contributorId":302586,"corporation":false,"usgs":false,"family":"Rodger","given":"Anthony","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":864744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trungale, Joe","contributorId":302587,"corporation":false,"usgs":false,"family":"Trungale","given":"Joe","email":"","affiliations":[{"id":65515,"text":"Texas Conservation Science","active":true,"usgs":false}],"preferred":false,"id":864745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winemiller, Kirk O.","contributorId":265134,"corporation":false,"usgs":false,"family":"Winemiller","given":"Kirk","email":"","middleInitial":"O.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":864746,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yancy, Lauren E.","contributorId":302588,"corporation":false,"usgs":false,"family":"Yancy","given":"Lauren","email":"","middleInitial":"E.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":864747,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240754,"text":"70240754 - 2023 - Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region","interactions":[],"lastModifiedDate":"2023-02-20T22:04:07.85217","indexId":"70240754","displayToPublicDate":"2023-02-20T15:55:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region","docAbstract":"Accurate mapping of streams that maintain surface flow during annual baseflow periods in mountain headwater streams is important for informing water availability for human consumption and is a fundamental determinant of in-channel conditions for stream-dwelling organisms. Yet accurate mapping that captures local spatial variability and associated local controls on surface flow presence is limited. An empirical random-forest model was developed to predict streamflow permanence (late summer surface-flow presence) for Mount Rainier National Park and the surrounding mountainous area in western Washington, USA. This model was developed to improve upon the existing multi-state, regional-scale probability of stream permanence developed for the greater Pacific Northwest Region (PROSPERPNW). The model was trained on 544 wet/dry observations collected during the late summer, baseflow period from 2018 to 2020 using the crowd-source mobile application, FLOwPER. Final model accuracy was 0.74 with drainage area and covariates describing geology, topography, and land cover as top predictors of streamflow permanence compared to coarser resolution climatic covariates. The prevalence of static covariates over climatic covariates as top ranked important covariates highlights the importance of scale when evaluating controls on streamflow permanence. Cross validation of the model indicates that streamflow permanence probabilities from this model is an improvement over the regional-scale PROSPERPNW model demonstrating the utility of relatively simple, crowd-sourced data to address water resource needs, and that determination of important predictors of streamflow permanence is influenced by the spatial and temporal resolution of analysis.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14813","usgsCitation":"Jaeger, K.L., Sando, R., Dunn, S., and Gendaszek, A.S., 2023, Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region: Hydrological Processes, v. 37, no. 2, e14813, 20 p., https://doi.org/10.1002/hyp.14813.","productDescription":"e14813, 20 p.","ipdsId":"IP-141066","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":444412,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14813","text":"Publisher Index Page"},{"id":435442,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P942QL23","text":"USGS data release","linkHelpText":"Supporting data for and predictions from streamflow permanence modeling in Mount Rainier National Park and surrounding area, Washington, 2018-2020"},{"id":413225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mt. Rainier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.91425095542269,\n 47.01752002020956\n ],\n [\n -122.78795134745224,\n 46.62862820386181\n ],\n [\n -122.55339493264981,\n 46.550098584041734\n ],\n [\n -122.65563747243549,\n 46.34497384739839\n ],\n [\n -122.89620815428425,\n 46.09474739160191\n ],\n [\n -122.7909584809756,\n 45.92766699262768\n ],\n [\n -122.74585147812789,\n 45.67282216496895\n ],\n [\n -122.66766600652696,\n 45.61185277936909\n ],\n [\n -122.30380285023114,\n 45.54239311283217\n ],\n [\n -121.81664721948816,\n 45.70223191477319\n ],\n [\n -121.67531194390217,\n 45.68332742209245\n ],\n [\n -121.45879833023858,\n 45.69383070693286\n ],\n [\n -121.1580849779278,\n 45.630781413305954\n ],\n [\n -120.98066410006487,\n 45.679125555918176\n ],\n [\n -121.06185670518839,\n 46.012189334319174\n ],\n [\n -121.07388523928094,\n 46.11234505718508\n ],\n [\n -120.77317188697053,\n 46.28718041984081\n ],\n [\n -120.78219328754065,\n 46.50050734721816\n ],\n [\n -120.60477240967734,\n 46.628693092975396\n ],\n [\n -120.65589367957018,\n 46.69473497860815\n ],\n [\n -120.90548576198775,\n 46.88004533998597\n ],\n [\n -121.37760572511556,\n 47.1445446352175\n ],\n [\n -122.05120363429107,\n 47.246711543948805\n ],\n [\n -122.36695265421739,\n 47.369052224585346\n ],\n [\n -122.54136639855736,\n 47.309956252224225\n ],\n [\n -122.67368027357409,\n 47.14249929055501\n ],\n [\n -122.9052295548532,\n 47.16294919605744\n ],\n [\n -122.91425095542269,\n 47.01752002020956\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":864707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":864708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Sarah B. 0000-0003-4463-0074","orcid":"https://orcid.org/0000-0003-4463-0074","contributorId":291768,"corporation":false,"usgs":false,"family":"Dunn","given":"Sarah B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":864709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":864710,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254725,"text":"70254725 - 2023 - Survival rates of band-tailed pigeons estimated using passive integrated transponder tags","interactions":[],"lastModifiedDate":"2024-06-07T12:23:07.129432","indexId":"70254725","displayToPublicDate":"2023-02-20T07:20:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Survival rates of band-tailed pigeons estimated using passive integrated transponder tags","docAbstract":"Obtaining survival estimates on the Interior population of band-tailed pigeons (Patagioenas fasciata) is challenging because they are trap shy, but the joint use of passive integrated transponder (PIT) tags and bands is a potential solution. We investigated the use of PIT tags to passively recapture band-tailed pigeon at 3 locations in New Mexico, USA, to estimate survival. From 2013–2015, we captured, banded, and marked >600 individual band-tailed pigeons with PIT tags. To estimate annual survival rates, we used a Barker multi-state joint live and dead encounters and resighting model. Survival models excluding transience had survival estimates across site, sex, and year of 0.86 (95% CI = 0.84–0.88) for after hatch year birds and 0.63 (95% CI = 0.48–0.76) for hatch year birds. These results are consistent with other survival estimates reported for the Interior population of band-tailed pigeons using band return data and potentially provide an effective alternative method of monitoring survival of this population.
While the effects of barriers to dispersal such as population declines, habitat fragmentation, and geographic distance have been well-documented in terrestrial wildlife, factors impeding the dispersal of highly vagile taxa such as seabirds are less well understood. The roseate tern (Sterna dougallii) is a globally distributed seabird species, but populations tend to be both fragmented and small, and the species is declining across most of its range. We evaluated structuring of roseate tern populations in the Northwestern Atlantic, the Caribbean, and the Azores using both microsatellite markers and single-nucleotide polymorphisms generated through targeted sequencing of Ultra-conserved Elements. For both marker types, we found significant genetic differentiation among all 3 populations and evidence for moderate contemporary unidirectional gene flow from the Caribbean to the Azores, but not between other populations. Within the Caribbean population, we found high rates of unidirectional migration from the Virgin Islands to Florida, potentially indicative of movement from source population to sink or an artifact of dispersal among other unsampled populations in the Caribbean region. These observations have significance for species persistence in the Atlantic, as our results indicate that loss of genetic diversity within populations is unlikely to be buffered by inflow of new alleles from other breeding populations.
Invasive species science has focused heavily on the invasive agent. However, management to protect native species also requires a proactive approach focused on resident communities and the features affecting their vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. Here, we introduce an analytical framework that quantifies the scale-dependent impact of biological invasions on native richness from the shape of the native species–area relationship (SAR). We leveraged newly available, biogeographically extensive vegetation data from the U.S. National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of non-native plant cover. Decreases in native richness were consistently associated with non-native species cover, but native richness was compromised only at relatively high levels of non-native cover. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that were colder and wetter were most vulnerable to losses of native plant species at the local level, while warmer and wetter areas were most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogeneous spatial pattern of vulnerability. This pattern could not be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape, and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.
Draining mine tunnels contribute contaminants to groundwater and surface water, but remediation strategies may be hindered as hydrogeologic characterization and modeling of these heterogeneous features generally relies on sparse data sets. The Captain Jack mine site in Colorado, USA, presents a unique data set allowing for temporal evaluation of groundwater connectivity in the vicinity of an abandoned mine, where a hydraulic bulkhead is impounding water within the mine workings. This study applied statistical analysis of system pressure responses to bulkheading and used an analytic-element modeling approach to characterize heterogeneity and groundwater flow. Groundwater-level elevation data collected over a period of 4 years, both prior to and after bulkheading, indicate that the mine workings act as a sink to the local groundwater system. Despite groundwater flow being generally oriented towards the mine workings, there are also large vertical and horizontal hydraulic gradients which persist through time. Although the groundwater system is highly compartmentalized, statistical analysis using Kendall’s Tau indicates correlations between hydraulic head changes in the mine workings and several wells completed in crystalline bedrock, indicating the influence of fracture flow. An analytic-element model was parameterized to account for uncertainty in hydraulic conductivity, recharge, and discharge. Model results reproduced the range of observed hydraulic heads in the mine workings and adjacent igneous dikes but failed to closely simulate hydraulic heads in several wells located distal from the mine workings in granitic bedrock. The modeling approach shows potential promise, however, for conducting preliminary modeling to guide data collection at other similar mine sites.
This Memo to All Banders (MTAB 103) was released in February 2023. Subjects in this this memo are 1. The Chief’s Chirp; 2. Alerts – Highly Pathogenic Avian Influenza and reminder that banders cannot submit data through Bandit, only manage data; 3. Staff updates – BBL Thanks Intern from Smithsonian-Mason School of Conservation and BBL Welcomes New Intern, Mary Woodruff; 4. News – Mary Gustafson Obituary, Bird Banding Office Celebrates its Centennial, Play Bander's Bingo and Contribute to Piranga, and Belted Kingfisher Banding; 5. A note from the permitting shelves - you can now add a Data Manger to your permit; 6. A note from the supply room, including 1C bands available and a reminder for banding Northern Saw-whet Owls; 7. Data management -- WRP Codes Updates, Bander Portal Template Update, Portal Training Schedule and Office Hours; 8. Frequently asked questions - what status code should I use for cloacal swabbing? and will there be video tutorials on the Bander Portal?; 9. Banding and encounter highlights; 10. Auxiliary marker corner; 11. Message from the Banding Associations; 12. Message to the Flyways; 13. Moments in history; 14. Recent Publications; 15. Upcoming events; and 16. Request for information.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., and McKay, J.L., 2023, MTAB 103, February 2023: Memo to All Banders (MTAB), 13 p.","productDescription":"13 p.","ipdsId":"IP-149984","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":413939,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-103-february-2023"},{"id":413953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":866104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Jennifer L. 0000-0002-8893-0231","orcid":"https://orcid.org/0000-0002-8893-0231","contributorId":296562,"corporation":false,"usgs":true,"family":"McKay","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":866105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240815,"text":"70240815 - 2023 - Outlining potential biomarkers of exposure and effect to critical minerals: Nutritionally essential trace elements and the rare earth elements","interactions":[],"lastModifiedDate":"2023-02-23T13:06:15.936835","indexId":"70240815","displayToPublicDate":"2023-02-17T07:05:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7597,"text":"Toxics","active":true,"publicationSubtype":{"id":10}},"title":"Outlining potential biomarkers of exposure and effect to critical minerals: Nutritionally essential trace elements and the rare earth elements","docAbstract":"Neotoma magister (Allegheny Woodrat) is a nocturnal, emergent rock-habitat specialist (i.e., inhabits rocky outcrops, boulderfields, and caves). Woodrat populations have declined range-wide due to habitat fragmentation, endoparasites, and interspecific competition. We estimated the diel activity curves of Allegheny Woodrats and assessed the effects of habitat type (exposed rock habitat/cave-exterior vs. cave-interior) and season (spring, summer, and fall) on curve shape. We also investigated the effect of 2 granivorous competitors' presence and activity curves (Peromyscus spp. and Tamias striatus [Eastern Chipmunk]) on woodrat activity. Additionally, we investigated whether the presence or absence of Procyon lotor (Raccoon), a primary carrier of Baylisascaris procyonis (Raccoon Roundworm), significantly affects the presence or absence of Allegheny Woodrats. We used remote-detecting cameras to document the diel cycles of Allegheny Woodrats and 2 competitors across 83 sites in western Virginia and 2 sites in West Virginia from 2017 to 2022. For 13,002 recorded events, we detected woodrats at 36 of 85 sites (3778 camera events). We observed a higher proportion of daytime activity by woodrats within cave interiors than cave exteriors. Allegheny Woodrat activity curves differed among seasons, with the greatest differences observed between summer and fall and with ∼80% activity overlap. These activity curves differed significantly when co-occurring with versus not co-occurring with a competitor. Additionally, Allegheny Woodrats showed an inverse activity rate with Peromyscus spp. Thus, our results suggest that competition avoidance via temporal partitioning occurs between these species. Allegheny Woodrats and Raccoons occurred together more often than expected suggesting the presence of woodrats is currently not reduced by the presence of Raccoons. Our remote-detecting camera data help elucidate relationships of Allegheny Woodrats with presumptive competitors, and open avenues for further investigation in Virginia.
Sediment fingerprinting of fluvial targets has proven useful to guide conservation management and prioritize sediment sources for Federal and State supported programs in the United States. However, the collection and analysis of source samples can make these studies unaffordable, especially when needed for multiple drainage basins. We investigate the potential use of source samples from a basin with similar physiography (using samples from one of a “pair” to evaluate samples from the other) or combined from multiple basins (a “library”).
Source samples from eight basins across six ecoregions were harvested from existing, published studies. Individual source samples were fingerprinted using a mixing model derived from source samples from other basins. The ability to identify source category was evaluated both as part of source verification and by classifying source samples as “targets.”
Approximately half of cropland samples were identified as targets, both as pairs and with the multi-basin source dataset, indicating that cropland samples could be shared for basins in similar ecoregions and be combined for larger stream systems. Streambank samples were better identified with the multi-basin analysis relative to the pairs, and those from mixed land-use basins improved this differentiation except for samples from basins with a dominant land-use type. Inconsistent identification of pasture samples highlighted the need for local samples. Inconsistent identification of forest samples indicated that upland- and riparian-forest samples are distinct. Road samples were identified as both sources and targets, and other source types were rarely apportioned as road: these may have the best potential to supplement local source samples. This source-sample library was then used to improve the accuracy of sediment-source apportionment for a previously studied basin.
Ultimately, the source verification process already used in individual basin studies to evaluate the accuracy of sediment-fingerprinting apportionments was useful for determining how to supplement local source samples with those from other basins. This study shows that supplementing local source samples with those from basins with similar physiography has the potential to both improve fingerprinting accuracy and decrease the cost of this type of study.
Understanding relationships between infection and wildlife movement patterns is important for predicting pathogen spread, especially for multispecies pathogens and those that can spread to humans and domestic animals, such as avian influenza viruses (AIVs). Although infection with low pathogenic AIVs is generally considered asymptomatic in wild birds, prior work has shown that influenza-infected birds occasionally delay migration and/or reduce local movements relative to their uninfected counterparts. However, most observational research to date has focused on a few species in northern Europe; given that influenza viruses are widespread globally and outbreaks of highly pathogenic strains are increasingly common, it is important to explore influenza–movement relationships across more species and regions. Here, we used telemetry data to investigate relationships between influenza infection and movement behavior in 165 individuals from four species of North American waterfowl that overwinter in California, USA. We studied both large-scale migratory and local overwintering movements and found that relationships between influenza infection and movement patterns varied among species. Northern pintails (Anas acuta) with antibodies to avian influenza, indicating prior infection, made migratory stopovers that averaged 12 days longer than those with no influenza antibodies. In contrast, greater white-fronted geese (Anser albifrons) with antibodies to avian influenza made migratory stopovers that averaged 15 days shorter than those with no antibodies. Canvasbacks (Aythya valisineria) that were actively infected with influenza upon capture in the winter delayed spring migration by an average of 28 days relative to birds that were uninfected at the time of capture. At the local scale, northern pintails and canvasbacks that were actively infected with influenza used areas that were 7.6 and 4.9 times smaller than those of uninfected ducks, respectively, during the period of presumed active influenza infection. We found no evidence for an influence of active influenza infection on local movements of mallards (Anas platyrhynchos). These results suggest that avian influenza can influence waterfowl movements and illustrate that the relationships between avian influenza infection and wild bird movements are context- and species-dependent. More generally, understanding and predicting the spread of multihost pathogens requires studying multiple taxa across space and time.
Interactions among species can strongly affect how plant communities reassemble after disturbances, and variability among native and invasive species across environmental gradients must be known in order to manage plant-community recovery. The stress-gradient hypothesis (SGH) predicts species interactions will be more positive in abiotically stressful conditions and conversely, more negative in benign conditions, and the resistance-resilience concept (RRC) may predict where and when invasions will complicate ecosystem recovery. We evaluated how abiotic stress and biotic interactions determine native bunchgrass abundances across environmental gradients using additive models of cover data from over 500 plots re-measured annually for 5 years as they recovered naturally (untreated) after a megafire (>100,000 ha) in sagebrush steppe threated by the invasive-grass and fire cycle. The species included native bunchgrasses, bluebunch wheatgrass (Pseudoroegneria spicata) and Sandberg bluegrass (Poa secunda), and the exotic and invasive annual cheatgrass (Bromus tectorum). We asked whether associations between native bunchgrasses and cheatgrass were context dependent and if the SGH could help predict interspecific associations between species in a semiarid environment. The association of cover of each native bunchgrass to cheatgrass was not uniform, and instead varied from neutral to negative across environmental gradients in both space and time (i.e., weather), to which the species had nonlinear and sometimes threshold-like responses. Consistent with the SGH, bunchgrasses were generally more negatively related to cheatgrass (i.e., putative competition) in conditions which increased the cover of each bunchgrass – which were higher elevations and temperatures and lower solar heatload, and, for Sandberg bluegrass, drier conditions. There were few indications of positive interactions (i.e., putative facilitation) in stressful conditions, and instead associations were again negative, albeit weaker, in some of the conditions evaluated. Synthesis. These findings demonstrate that the negative association among native bunchgrasses and cheatgrass is context dependent and is determined by the abundances of both interacting species which is driven by environmental stress. This led to a hypothesis that together Sandberg bluegrass and bluebunch wheatgrass provide complementary resistance to cheatgrass at the landscape level, despite their different ecology and contrary to the management preference for bluebunch wheatgrass. Sandberg bluegrass might be critical for providing resistance against cheatgrass where invasion potential is greatest, i.e., at lower elevations, where bluebunch wheatgrass is scarce.