While the period from fledging through first breeding for waterbird species such as terns (e.g., genus Sterna, Sternula) is of great interest to researchers and conservationists, this period remains understudied due in large part to the difficulty of marking growing juveniles with radio transmitters that remain attached for extended periods.
In an effort to facilitate such research, we examined the impact of various combinations of harness types (backpack, leg-loop, and 3D-printed harnesses), harness materials (Automotive ribbon, Elastic cord, and PFTE ribbon), and transmitter types (center-weighted and rear-weighted) on a surrogate for juvenile terns, 28-day-old Japanese quail (Coturnix japonica; selected due to similarities in adult mass and downy feathering of juveniles), in a 30-day experiment. We monitored for abrasion at points of contact and tag gap issues via daily exams while also recording mass and wing cord as indices of growth. This study was designed to serve as an initial examination of the impacts of marking on the growth and development of young birds and does not account for any impacts of tags on movement or behavior.
While we found that treatment (the specific combination of the transmitter type, harness type, and harness material) had no impact on bird growth relative to unmarked control birds (P ≥ 0.05), we did observe differences in abrasion and tag gap between treatments (P ≤ 0.05). Our results suggest that leg-loop harnesses constructed from elastic cord and backpack harnesses from PFTE ribbon are suitable options for long-term attachment to growing juveniles. Conversely, we found that automotive ribbon led to extensive abrasion with these small-bodied birds, and that elastic cord induced blisters when used to make a backpack harness.
While these results indicate that long-term tagging of juvenile birds is possible with limited impacts on growth, this work does not preclude the need for small-scale studies with individual species. Instead, we hope this provides an informed starting point for further exploration of this topic.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-021-00257-9","usgsCitation":"Buck, E., Sullivan, J.D., Kent, C.M., Mullinax, J.M., and Prosser, D., 2021, A comparison of methods for the long-term harness-based attachment of radio-transmitters to juvenile Japanese quail (Coturnix japonica): Animal Biotelemetry, v. 9, 32, 16 p., https://doi.org/10.1186/s40317-021-00257-9.","productDescription":"32, 16 p.","ipdsId":"IP-126974","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":450759,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-021-00257-9","text":"Publisher Index Page"},{"id":436197,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LZD1V0","text":"USGS data release","linkHelpText":"Testing transmitter types, harness types, and harness materials for attachment of radio transmitters onto avian chicks"},{"id":389472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":823482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":823483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":823484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":823485,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823486,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226195,"text":"70226195 - 2021 - USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition","interactions":[],"lastModifiedDate":"2021-11-16T13:11:54.650412","indexId":"70226195","displayToPublicDate":"2021-09-20T07:11:31","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"displayTitle":"USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) Newsletter – Summer 2021 Edition","title":"USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Munson, S.M., and McCormick, M.L., 2021, USGS RAMPS (Restoration Assessment and Monitoring Program for the Southwest) newsletter – Summer 2021 edition, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-133237","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":391726,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/center-news/ramps-newsletter-summer-2021-edition"},{"id":391745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":826841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Molly L. 0000-0002-4361-7567 mmccormick@usgs.gov","orcid":"https://orcid.org/0000-0002-4361-7567","contributorId":196257,"corporation":false,"usgs":true,"family":"McCormick","given":"Molly","email":"mmccormick@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":826840,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224270,"text":"sir20215075 - 2021 - Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","interactions":[],"lastModifiedDate":"2021-09-20T14:34:44.807541","indexId":"sir20215075","displayToPublicDate":"2021-09-20T06:57:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5075","displayTitle":"Development of a Screening Tool To Examine Lake and Reservoir Susceptibility to Eutrophication in Selected Watersheds of the Eastern and Southeastern United States","title":"Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","docAbstract":"This report describes a new screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States using estimated nutrient loading and flushing rates with measures of waterbody morphometry. To that end, the report documents the compiled data and methods (R-script) used to categorize waterbodies by Carlson’s Trophic State Index. Assessments were completed for 232 lakes and reservoirs having a surface area greater than or equal to 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Waterbodies were categorized by type—natural lakes, headwater reservoirs, and downstream reservoirs—and were assessed independently. Recursive partitioning and the model-based boosting routine were used to create four-node regression trees to group waterbodies into five endpoints from low-to-high measures of Secchi depth, and concentrations of chlorophyll a and microcystin according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned based on the average value within each of the five endpoints. An application (procedure) is provided using the tool to examine the susceptibility of a given waterbody of interest to eutrophication. Results of this study can aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication relative to nutrient loading, flushing rate, and morphometric characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215075","usgsCitation":"Green, W.R., Hoos, A.B., Wilson, A.E., and Heal, E.N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5075, 59 p., https://doi.org/10.3133/sir20215075.","productDescription":"Report: vi, 59 p.; Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-097274","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5075/coverthb.jpg"},{"id":389349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5075/sir20215075.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5075"},{"id":389350,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7EOH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.916015625,\n 40.51379915504413\n ],\n [\n -73.71826171874999,\n 40.896905775860006\n ],\n [\n -73.05908203125,\n 41.19518982948959\n ],\n [\n -71.60888671875,\n 41.343824581185686\n ],\n [\n -70.48828125,\n 41.21172151054787\n ],\n [\n -69.7412109375,\n 41.83682786072714\n ],\n [\n -70.48828125,\n 42.69858589169842\n ],\n [\n -70.09277343749999,\n 43.54854811091286\n ],\n [\n -67.1044921875,\n 44.574817404670306\n ],\n [\n -66.884765625,\n 44.824708282300236\n ],\n [\n -67.236328125,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.259422036351694\n ],\n [\n -67.39013671875,\n 45.583289756006316\n ],\n [\n -67.8515625,\n 45.706179285330855\n ],\n [\n -67.87353515625,\n 47.15984001304432\n ],\n [\n -68.22509765625,\n 47.338822694822\n ],\n [\n -68.90625,\n 47.234489635299184\n ],\n [\n -69.08203125,\n 47.45780853075031\n ],\n [\n -69.2138671875,\n 47.487513008956554\n ],\n [\n -70.048828125,\n 46.694667307773116\n ],\n [\n -70.224609375,\n 46.17983040759436\n ],\n [\n -70.55419921875,\n 45.66012730272194\n ],\n [\n -70.90576171875,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.321254361171476\n ],\n [\n -71.47705078125,\n 44.99588261816546\n ],\n [\n -74.86083984375,\n 45.01141864227728\n ],\n [\n -75.73974609375,\n 44.5435052132082\n ],\n [\n -74.8828125,\n 44.18220395771566\n ],\n [\n -74.33349609375,\n 43.91372326852401\n ],\n [\n -75.12451171875,\n 43.46886761482925\n ],\n [\n -75.9375,\n 43.29320031385282\n ],\n [\n -76.88232421875,\n 42.4234565179383\n ],\n [\n -77.431640625,\n 42.65012181368022\n ],\n [\n -77.9150390625,\n 42.22851735620852\n ],\n [\n -77.76123046875,\n 41.902277040963696\n ],\n [\n -78.57421875,\n 40.64730356252251\n ],\n [\n -79.4970703125,\n 39.791654835253425\n ],\n [\n -79.95849609375,\n 38.013476231041935\n ],\n [\n -80.52978515625,\n 37.24782120155428\n ],\n [\n -81.40869140625,\n 35.37113502280101\n ],\n [\n -83.43017578125,\n 35.35321610123823\n ],\n [\n -85.10009765625,\n 35.38904996691167\n ],\n [\n -86.90185546874999,\n 35.38904996691167\n ],\n [\n -87.802734375,\n 35.17380831799959\n ],\n [\n -89.07714843749999,\n 34.63320791137959\n ],\n [\n -89.05517578125,\n 33.37641235124676\n ],\n [\n -88.87939453125,\n 32.80574473290688\n ],\n [\n -89.8681640625,\n 31.85889704445453\n ],\n [\n -89.93408203124999,\n 31.316101383495624\n ],\n [\n -89.736328125,\n 30.694611546632277\n ],\n [\n -89.45068359374999,\n 30.14512718337613\n ],\n [\n -86.8359375,\n 30.088107753367257\n ],\n [\n -85.62744140625,\n 29.935895213372444\n ],\n [\n -84.9462890625,\n 29.401319510041485\n ],\n [\n -84.287109375,\n 29.878755346037977\n ],\n [\n -82.85888671875,\n 29.036960648558267\n ],\n [\n -83.0126953125,\n 27.97499795326776\n ],\n [\n -81.5625,\n 25.403584973186703\n ],\n [\n -80.88134765625,\n 24.607069137709683\n ],\n [\n -79.89257812499999,\n 25.005972656239187\n ],\n [\n -79.5849609375,\n 26.92206991673282\n ],\n [\n -81.2109375,\n 30.939924331023445\n ],\n [\n -77.431640625,\n 34.21634468843463\n ],\n [\n -75.65185546874999,\n 34.97600151317588\n ],\n [\n -75.60791015625,\n 37.142803443716836\n ],\n [\n -75.12451171875,\n 38.35888785866677\n ],\n [\n -73.80615234375,\n 39.99395569397331\n ],\n [\n -73.916015625,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.
Louisiana contains nearly 40% of estuarine herbaceous wetlands in the contiguous United States, supporting valuable ecosystem services and providing significant economic benefits to the state and the entire United States. However, coastal Louisiana is a hotspot for rapid land loss from factors including hurricanes, land use change, and high subsidence rates contributing to high relative sea-level rise. The Coastal Protection and Restoration Authority (CPRA) was established after major hurricanes in 2005 to coordinate coastal restoration in Louisiana and develop the Louisiana Coastal Master Plan. The LA Coastal Master Plan uses numerical modeling of multiple scenarios to select a suite of restoration projects based on maximum land area created and flood reduction (as proxies for ecosystem value). Using potential value to aquatic, terrestrial, and social resources, our work compared habitat value of shallow open water areas to emergent wetland. While potential resource benefits varied by emergent wetland salinity type and emergent wetland versus water, they were similar, suggesting that restoration planning based primarily on wetland land area may not achieve the maximum possible ecosystem benefits. After nearly 20 years of integrated restoration planning in coastal Louisiana, a reassessment of restoration planning decision drivers may be beneficial to ensure maximum benefits from coastal restoration. As a result of the Deepwater Horizon oil spill, settlement funds will be a major support to coastal restoration in Louisiana for many years. Assessing potential habitat value to multiple natural and social resources in Louisiana has potential to maximize synergy with large northern Gulf of Mexico restoration programs.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13564","usgsCitation":"Carruthers, T., Kiskaddon, E.P., Baustian, M., Darnell, K.M., Moss, L.C., Perry, C.L., and Stagg, C., 2021, Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?: Restoration Ecology, v. 30, e13564, 8 p., https://doi.org/10.1111/rec.13564.","productDescription":"e13564, 8 p.","ipdsId":"IP-074120","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13564","text":"Publisher Index Page"},{"id":395227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.922119140625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 28.94086176940557\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Carruthers, Tim J. 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B.","affiliations":[],"preferred":false,"id":832364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiskaddon, Erin P.","contributorId":272886,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"Erin","email":"","middleInitial":"P.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa M.","contributorId":189569,"corporation":false,"usgs":false,"family":"Baustian","given":"Melissa M.","affiliations":[],"preferred":false,"id":832366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Darnell, Kelly M.","contributorId":272888,"corporation":false,"usgs":false,"family":"Darnell","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":832367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Leland C.","contributorId":272644,"corporation":false,"usgs":false,"family":"Moss","given":"Leland","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832368,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Carey L.","contributorId":189570,"corporation":false,"usgs":false,"family":"Perry","given":"Carey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":832369,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":832370,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229723,"text":"70229723 - 2021 - Fishing gear performance nearshore is substantiated by spatial analyses","interactions":[],"lastModifiedDate":"2022-03-16T15:31:45.435187","indexId":"70229723","displayToPublicDate":"2021-09-18T10:17:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3278,"text":"Reviews in Fish Biology and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Fishing gear performance nearshore is substantiated by spatial analyses","docAbstract":"We estimated whether the fish assemblages nearshore represented by electrofishing and gillnetting indexed location of reservoirs in a river basin. We expected that location in the basin would reflect a multiplicity of factors that determine fish habitat and fish assemblage composition, and therefore also anticipated a correlation between fish species composition and spatial variables if the gear type reflected legitimate differences in fish assemblages. We collected 1.6 million fish of 129 species in 22 reservoirs of the Tennessee River basin, USA. Standardized electrofishing represented different aspects of the fish assemblages than standardized gillnetting. Nevertheless, the assemblages documented by each gear type were correlated with the spatial location of the reservoirs in the river basin. Thus, even as these gear types reflected different aspects of existing fish assemblages, they each tracked spatial differences, suggesting that they reflected standing fish assemblages. Our study supports the use of standardized boat electrofishing and gillnetting as proper means for monitoring fish assemblages at large spatial scales. Our results further suggest that a well-designed and standardized sampling protocol can in fact provide an informative bird’s eye view of fish assemblages at regional, national, or continental scales suitable for informing conservation programs.
","language":"English","publisher":"Springer","doi":"10.1007/s11160-021-09683-7","usgsCitation":"Miranda, L.E., Faucheux, N.M., and Lakin, K.M., 2021, Fishing gear performance nearshore is substantiated by spatial analyses: Reviews in Fish Biology and Fisheries, v. 31, p. 977-987, https://doi.org/10.1007/s11160-021-09683-7.","productDescription":"11 p.","startPage":"977","endPage":"987","ipdsId":"IP-130241","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee, Virginia","otherGeospatial":"Tennessee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.43994140625,\n 37.28279464911045\n ],\n [\n -88.74755859375,\n 34.97600151317588\n ],\n [\n -88.17626953125,\n 33.94335994657882\n ],\n [\n -85.45166015624999,\n 34.07086232376631\n ],\n [\n -82.880859375,\n 34.45221847282654\n ],\n [\n -83.0126953125,\n 35.04798673426734\n ],\n [\n -82.37548828125,\n 35.191766965947394\n ],\n [\n -81.93603515625,\n 35.191766965947394\n ],\n [\n -81.62841796875,\n 37.21283151445594\n ],\n [\n -82.02392578125,\n 37.49229399862877\n ],\n [\n -82.37548828125,\n 37.71859032558816\n ],\n [\n -84.9462890625,\n 36.914764288955936\n ],\n [\n -85.49560546875,\n 35.60371874069731\n ],\n [\n -87.62695312499999,\n 37.28279464911045\n ],\n [\n -88.43994140625,\n 37.28279464911045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2021-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faucheux, Nicky M.","contributorId":271194,"corporation":false,"usgs":false,"family":"Faucheux","given":"Nicky","email":"","middleInitial":"M.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":838099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lakin, Kurt M.","contributorId":288574,"corporation":false,"usgs":false,"family":"Lakin","given":"Kurt","email":"","middleInitial":"M.","affiliations":[{"id":61800,"text":"Tennessee Valley Authority, Chattanooga","active":true,"usgs":false}],"preferred":false,"id":838100,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225531,"text":"70225531 - 2021 - Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","interactions":[],"lastModifiedDate":"2021-12-10T17:01:34.289166","indexId":"70225531","displayToPublicDate":"2021-09-18T08:12:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","docAbstract":"Groundwater total dissolved solids (TDS) distribution was mapped with a three-dimensional (3D) model, and it was found that TDS variability is largely controlled by stratigraphy and geologic structure. General TDS patterns in the San Joaquin Valley of California (USA) are attributed to predominantly connate water composition and large-scale recharge from the adjacent Sierra Nevada. However, in smaller areas, stratigraphy and faulting play an important role in controlling TDS. Here, the relationship of stratigraphy and structure to TDS concentration was examined at Poso Creek Oil Field, Kern County, California. The TDS model was constructed using produced water TDS samples and borehole geophysics. The model was used to predict TDS concentration at discrete locations in 3D space and used a Gaussian process to interpolate TDS over a volume. In the overlying aquifer, TDS is typically <1,000 mg/L and increases with depth to ~1,200–3,500 mg/L in the hydrocarbon zone below the Macoma claystone—a regionally extensive, fine-grained unit—and reaches ~7,000 mg/L in isolated places. The Macoma claystone creates a vertical TDS gradient in the west where it is thickest, but control decreases to the east where it pinches out and allows freshwater recharge. Previously mapped normal faults were found to exhibit inconsistent control on TDS. In one case, high-density faulting appears to prevent recharge from flushing higher-TDS connate water. Elsewhere, the high-throw segments of a normal fault exhibit variable behavior, in places blocking lower-TDS recharge and in other cases allowing flushing. Importantly, faults apparently have differential control on oil and groundwater.
Changes in winter conditions, such as decreased ice coverage and duration, have been observed in the Laurentian Great Lakes for more than 20 years. Such changes have been hypothesized to be linked to low Coregonus spp. survival to age-1 as most cisco (Coregonus artedi) populations are autumn spawners whose embryos incubate under ice throughout the winter. The quantity of light during winter is regulated by ice coverage, and light affects embryo survival and development in some teleosts. We experimentally evaluated how cisco embryos from lakes Superior and Ontario respond to three light treatments that represented day-light intensity under 0–10, 40–60, and 90–100% ice coverage. Embryonic response measures included two developmental factors (embryo survival and incubation period) and two morphological traits (length-at-hatch and yolk-sac volume). Embryo survival was highest at the medium light treatment and decreased at high and low treatments for both populations, suggesting cisco may be adapted to withstand some light exposure from inter-annual variability in ice coverage. Light intensity had no overall effect on length of incubation. Increasing light intensity decreased length-at-hatch in Lake Superior but had no effect in Lake Ontario. Yolk-sac volume was positively correlated with increasing light in Lake Superior and negatively correlated in Lake Ontario. Contrasting responses in embryo development between lakes suggests differences in populations’ response to light is flexible. Our results provide a step towards better understanding the high variability observed in coregonine recruitment and may help predict what the future of this species may look like under current climate trends.
In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km2 with most locations having abundances between 20,000 to 50,000 particles/km2. The average abundance in Lake Superior was ~30,000 particles/km2 which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.
Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.
The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.
This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.
Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.
The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.
Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.
Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.
The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.
More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.
The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Since 2011, seafloor temperatures, pressures, and seismic ground motions have been measured by the seafloor cabled Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) on the Nankai margin. These measurements, high-resolution bathymetry, and abundant contextual information make the DONET region seem ideally suited to provide constraints on seismic shaking-triggered sediment slope failures and gravity flows, particularly since numerous published studies have linked paleo- to modern earthquakes to failures and flows within the DONET. The occurrences of the local 2016 M6.0 Mie-ken and regional M7.0 Kumamoto earthquakes within and at regional distances, respectively, from the DONET data set provided an opportunity to explore this potential. We used DONET seismic recordings of the posited triggering shaking and to search for submarine slide signals and continuous temperature and pressure data to detect pulses of warm and densified water indicative of passing flows. We developed and applied a variety of analytical methods to eliminate signals generated by water column processes, while leaving slope failures and sediment gravity flow anomalies as residuals. Our explorations yielded no evidence that earthquake shaking initiated either phenomenon, which we suggest reflects the finicky nature both of the detection of and the physical processes that contribute to slope failures and flows (i.e., both require satisfying precise suites of conditions). Nonetheless, this negative result, our analyses, and the estimates of physical properties we derived for them, provide useful lessons and inputs for future studies.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB022588","usgsCitation":"Gomberg, J.S., Ariyoshi, K., Hautala, S., and Johnson, H., 2021, The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows: Journal of Geophysical Research, v. 126, e2021JB022588, 26 p., https://doi.org/10.1029/2021JB022588.","productDescription":"e2021JB022588, 26 p.","ipdsId":"IP-123242","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 135.61973524947484,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 34.29410264461973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":924643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, H.P.","contributorId":349727,"corporation":false,"usgs":false,"family":"Johnson","given":"H.P.","affiliations":[],"preferred":false,"id":924644,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224538,"text":"70224538 - 2021 - Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","interactions":[],"lastModifiedDate":"2022-01-24T16:16:48.709305","indexId":"70224538","displayToPublicDate":"2021-09-17T10:11:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5020,"text":"Microorganisms","active":true,"publicationSubtype":{"id":10}},"title":"Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve","docAbstract":"Freshwater mussels (Unionida) are suffering mass mortality events worldwide, but the causes remain enigmatic. Here, we describe an analysis of bacterial loads, community structure, and inferred metabolic pathways in the hemolymph of pheasantshells (Actinonaias pectorosa) from the Clinch River, USA, during a multi-year mass mortality event. Bacterial loads were approximately 2 logs higher in moribund mussels (cases) than in apparently healthy mussels (controls). Bacterial communities also differed between cases and controls, with fewer sequence variants (SVs) and higher relative abundances of the proteobacteria Yokenella regensburgei and Aeromonas salmonicida in cases than in controls. Inferred bacterial metabolic pathways demonstrated a predominance of degradation, utilization, and assimilation pathways in cases and a predominance of biosynthesis pathways in controls. Only two SVs correlated with Clinch densovirus 1, a virus previously shown to be strongly associated with mortality in this system: Deinococcota and Actinobacteriota, which were associated with densovirus-positive and densovirus-negative mussels, respectively. Overall, our results suggest that bacterial invasion and shifts in the bacterial microbiome during unionid mass mortality events may result from primary insults such as viral infection or environmental stressors. If so, bacterial communities in mussel hemolymph may be sensitive, if generalized, indicators of declining mussel health.
","language":"English","publisher":"MDPI","doi":"10.3390/microorganisms9091976","usgsCitation":"Richard, J., Campbell, L., Leis, E., Agbalog, R., Dunn, C.D., Waller, D.L., Knowles, S., Putnam, J.G., and Goldberg, T., 2021, Mussel mass mortality and the microbiome: Evidence for shifts in the bacterial microbiome of a declining freshwater bivalve: Microorganisms, v. 9, no. 9, 1976, 15 p., https://doi.org/10.3390/microorganisms9091976.","productDescription":"1976, 15 p.","ipdsId":"IP-131640","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":450779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/microorganisms9091976","text":"Publisher Index Page"},{"id":389816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee, Virginia","otherGeospatial":"Clinch River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.2489013671875,\n 36.416862115300304\n ],\n [\n -81.485595703125,\n 37.413800350662896\n ],\n [\n -82.177734375,\n 37.71859032558816\n ],\n [\n -84.44091796875,\n 36.43012234551576\n ],\n [\n -83.2489013671875,\n 36.416862115300304\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richard, Jordan","contributorId":211789,"corporation":false,"usgs":false,"family":"Richard","given":"Jordan","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":823973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Lewis J. 0000-0002-7852-2250","orcid":"https://orcid.org/0000-0002-7852-2250","contributorId":244773,"corporation":false,"usgs":false,"family":"Campbell","given":"Lewis J.","affiliations":[],"preferred":false,"id":823974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Eric","contributorId":179325,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","affiliations":[],"preferred":false,"id":823975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agbalog, Rose","contributorId":239870,"corporation":false,"usgs":false,"family":"Agbalog","given":"Rose","affiliations":[{"id":48017,"text":"USFWS-Virginia Field Office","active":true,"usgs":false}],"preferred":false,"id":823976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Christopher D.","contributorId":225521,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":41155,"text":"Department of Pathobiological Sciences, University of Wisconsin-Madison,","active":true,"usgs":false}],"preferred":false,"id":823977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":823979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Putnam, Joel G. 0000-0002-5464-4587 jgputnam@usgs.gov","orcid":"https://orcid.org/0000-0002-5464-4587","contributorId":5783,"corporation":false,"usgs":true,"family":"Putnam","given":"Joel","email":"jgputnam@usgs.gov","middleInitial":"G.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":823980,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goldberg, Tony","contributorId":211788,"corporation":false,"usgs":false,"family":"Goldberg","given":"Tony","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":823981,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255182,"text":"70255182 - 2021 - Mechanistic invasive species management models and their application in conservation","interactions":[],"lastModifiedDate":"2024-06-13T13:39:08.914783","indexId":"70255182","displayToPublicDate":"2021-09-17T08:36:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Mechanistic invasive species management models and their application in conservation","docAbstract":"Management strategies to address the challenges associated with invasive species are critical for effective conservation. An increasing variety of mathematical models offer insight into invasive populations, and can help managers identify cost effective prevention, control, and eradication actions. Despite this, as model complexity grows, so does the inaccessibility of these tools to conservation practitioners making decisions about management. Here, we seek to narrow the science-practice gap by reviewing invasive species management models (ISMMs). We define ISMMs as mechanistic models used to explore invasive species management strategies, and include reaction-advection–diffusion models, integrodifference equations, gravity models, particle transport models, nonspatial and spatial discrete-time population growth models, cellular automata, and individual-based models. For each approach, we describe the model framework and its implementation, discuss strengths and weaknesses, and give examples of conservation applications. We conclude by discussing how ISMMs can be used in concert with adaptive management to address scientific uncertainties impeding action and with multiple objective decision processes to evaluate tradeoffs among management objectives. We undertook this review to support more effective decision-making involving invasive species by providing conservation practitioners with the information they need to identify tools most useful for their applications.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.533","usgsCitation":"Thompson, B.K., Olden, J., and Converse, S.J., 2021, Mechanistic invasive species management models and their application in conservation: Conservation Science and Practice, v. 3, no. 11, e533, 18 p., https://doi.org/10.1111/csp2.533.","productDescription":"e533, 18 p.","ipdsId":"IP-126294","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467225,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.533","text":"Publisher Index Page"},{"id":430128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":338912,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle","email":"","middleInitial":"K.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olden, Julian D.","contributorId":338914,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":903685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903683,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224582,"text":"70224582 - 2021 - Evaluation of SWIR crop residue bands for the Landsat Next mission","interactions":[],"lastModifiedDate":"2021-09-29T13:25:56.428904","indexId":"70224582","displayToPublicDate":"2021-09-17T08:22:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of SWIR crop residue bands for the Landsat Next mission","docAbstract":"Natural and cultural resource managers are increasingly working with the scientific community to create information on how best to adapt to the current and projected impacts of climate change. Engaging with these managers is a strategy that researchers can use to ensure that scientific outputs and findings are actionable (or useful and usable). In this article, the authors adapt Davidson’s wheel of participation to characterize and describe common stakeholder engagement strategies across the spectrum of Inform, Consult, Participate, and Empower. This adapted framework provides researchers with a standardized vocabulary for describing their engagement approach, guidance on how to select an approach, methods for implementing engagement, and potential barriers to overcome. While there is often no one “best” approach to engaging with stakeholders, researchers can use the objectives of their project and the decision context in which their stakeholders operate to guide their selection. Researchers can also revisit this framework over time as their project objectives shift and their stakeholder relationships evolve.
Global mean sea level (GMSL) has been rising since the last century, posing a serious challenge for the coastal areas. A variety of regression models have been utilized for determining GMSL rise over the past one hundred years, resulting in a large spread of sea level rise rates and multidecadal variations. In this study, we develop a new nonparametric noise model that is data-dependent and considers overfitting due to regression. The noise model is used to determine whether one regression model has significantly better skill than others over the period 1900–2010. The choices of background noise and GMSL reconstruction influence whether two sea level models can be statistically distinguished. With our new nonparametric noise spectra, the differences of model skills in explaining sea level variance are significant only in 34% of model comparisons. However, stepwise trends with three inflection points are significantly more skillful than the linear, quadratic, or exponential trend for most GMSL reconstructions, suggesting the importance of multidecadal variability of sea level rise in the twentieth century. Nevertheless, stepwise trend models cannot be distinguished from models with a long-term harmonic oscillation, indicating that the shape of multidecadal variability is not conclusive. The multidecadal variability is also significant in the steric and barystatic sea level contributions and is related to both natural and anthropogenic forcings. GMSL predictions based on regression fits in the twentieth century underestimate the sea level rise rate over the period 2011–2020 because the sea level acceleration in the recent decade (2011–2020) is not well represented.
Forest structure and topography can influence the ecohydrologic function and resiliency to drought and changing climate. It is, therefore, important to understand how forest restoration treatments alter snowpack distribution and design the treatments accordingly. We use a combination of aerial lidar, multi-temporal terrestrial mobile lidar, and UAV photogrammetry to estimate rapidly changing snow depth and cover in northern Arizona, USA. We then examine the impact of forest structure and topography on snow depth and snow cover persistence to inform forest restoration treatments. Our results show that mobile lidar data can be used to estimate snow depth with standard errors of 8 cm when differenced with snow-off airborne lidar data. UAV-based Structure-from-Motion data can be used to estimate snow cover persistence with 92–97% overall accuracies in forested ecosystems. Random forest models indicate spatially varying importance of forest structural and topographic variables in predicting snow depth and cover persistence, when summarized at different spatial scales (from 5 m to 250 m) and with variable directional location offsets. Forest snow depth was best explained (R2 ≈ 0.46) by canopy height metrics at summary scales of >75 m, while canopy cover was most important at summary scales of <40 m (R2 ≈ 0.3). Snow cover persistence was best explained at very local scales by canopy cover (R2 ≈ 0.38) and less so at larger scales (>75 m) by topographic and forest patch characteristics (R2 ≈ 0.34). Our results demonstrate that 3-dimensional datasets are critical in rapidly characterizing changing snowpack to better understand the impacts of forest structure and topography to inform forest restoration treatment designs. The relationships observed in our study can inform currently ongoing regional-scale forest restoration in the southwest to improve forest health and resiliency.
Assessing the long-term efficacy of control methods is a critical component of invasive species management. For example, if traits related to control have significant heritability or are influenced by maternal effects, control methods may lose efficacy over time. The potential for these effects can be evaluated via parent/offspring survival analysis, which concomitantly recasts adaptive management as an evolutionary force for invasive species. However, difficulties can arise when the life history of an invasive is cryptic, precluding direct observations of familial relationships. Genomic pedigree reconstruction can facilitate such analyses by assigning offspring to parents in invasive species for which mating and reproduction are difficult to study. Here, we use genomic pedigree reconstruction to quantify parental longevity and probability of offspring survival for brown treesnakes (Boiga irregularis) on Guam in a landscape treated with toxic baits simulating application via an aerial delivery system (ADS). To do so, we used 398 single nucleotide polymorphisms (SNPs) to update an existing multi-generational genomic pedigree for a geographically-closed population of brown treesnakes. This facilitated assignment of parents to juveniles born during three consecutive years of toxic bait application under a simulated aerial treatment program (N = 72). We found that the offspring of dams that persisted until the end of the study displayed greater survival probability (cox proportional hazard model, P < 0.001), yet there was no such effect for sires. This sex-specific relationship between parental longevity and offspring survival indicates that heritability of traits contributing to resistance to ADS is unlikely, but it supports a role of maternal effects that could undermine ADS. Our study identifies potential risks associated with control efforts and also highlights the utility of parent-offspring survival analyses informed by genomic pedigree reconstruction as a tool for adaptive management.
Protein derived from pollen is an essential component of healthy bee diets. Protein content in honey bee foraged-pollen varies temporally and spatially, but the drivers underlying this variation remain poorly characterized. We assessed the temporal and spatial variation in honey bee collected pollen in 12 Michigan apiaries over 3 summers (2015–2017). We simultaneously monitored forage in flowering habitats (uncultivated floristically-rich areas and conservation program land) near these apiaries throughout the growing season. We used these data, along with data from the literature on plant pollen protein content, to determine if honey bees collected a greater proportion of pollen from plant species growing in higher abundance or from plant species that have higher protein content. Protein content in honey bee collected pollen decreased from July to September every year, and there were among-year differences in pollen protein, highlighting the temporal variation in protein collected by these insects. Pollen protein was spatially consistent and broad-scale land use categories were not correlated with pollen protein content. Rather, our findings suggest flowering habitats found across land use categories can support honey bee foraging, which may confound broader land use effects. In early July and in early September, colonies collected a greater proportion of pollen from plants that grew in greater abundance in flowering habitats, but from late July through August, a greater proportion of pollen was collected from high-protein taxa, regardless of abundance. This suggests different factors may influence pollen forager decision-making throughout the season as colony needs and/or available forage communities change. Insights into the role of plant abundance and protein content on foraging could deepen our understanding of honey bee foraging behavior and help to inform habitat restoration programs for improved honey bee nutrition outcomes.
Upscaling groundwater flow is a fundamental challenge in hydrogeology. This study proposed time-fractional flow equations (t-FFEs) for upscaling long-term, transient groundwater flow and propagation of pressure heads in heterogeneous media. Monte Carlo simulations showed that, with increasing variance and correlation of the hydraulic conductivity (K), flow dynamics gradually deviated from Darcian flow and exhibit sub-diffusive, time-dependent evolution which can be separated into three major stages. At the early stage, the interconnected high-K zones dominated flow, while at intermediate times, the transverse flow due to mixed high- and low-K zones caused delayed rise of the piezometric head. At late times when flow in the relatively high-K domains reached stability, cells with very low-K continued to block the entry of water and generate “islands” with low piezometric head, significantly extending the temporal evolution of the piezometric head. The elongated water breakthrough curve cannot be quantified by the flow equation with an effective K, the space-fractional flow equation, or the multi-rate mass transfer (MRMT) flow model with a few rates, motivating the development of t-FFEs assuming temporally non-Darcian flow. Model applications showed that both the early and intermediate stages of flow dynamics can be captured by a single-index t-FFE (whose index is the exponent of the power-law probability density function of the random operational time for water parcels), but the overall evolution of flow dynamics, especially the enhanced retention of flow at later times, required a distributed-order t-FFE with variable indexes for different flow phases that can dominate flow dynamics at different stages. Therefore, transient groundwater flow in aquifers with spatially stationary heterogeneity can be temporally non-Darcian and non-stationary, due to the time-sensitive, combined effects of interconnected high-K channels and isolated low-K deposits on flow dynamics (which is the hydrogeological mechanism for the temporally non-Darcian flow and sub-diffusive pressure propagation), whose long-term behavior can be quantified by multi-index stochastic models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR029554","usgsCitation":"Xia, Y., Zhang, Y., Green, C., and Fogg, G., 2021, Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity: Water Resources Research, v. 57, no. 11, e2020WR029554, 30 p., https://doi.org/10.1029/2020WR029554.","productDescription":"e2020WR029554, 30 p.","ipdsId":"IP-119835","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450800,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/0pv0z4t0","text":"External Repository"},{"id":390598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xia, Yuan","contributorId":267790,"corporation":false,"usgs":false,"family":"Xia","given":"Yuan","email":"","affiliations":[{"id":55508,"text":"Guilin University of Technology","active":true,"usgs":false}],"preferred":false,"id":825278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Yong","contributorId":214040,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":825279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":825280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fogg, Graham 0000-0003-0676-1911","orcid":"https://orcid.org/0000-0003-0676-1911","contributorId":267791,"corporation":false,"usgs":false,"family":"Fogg","given":"Graham","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":825281,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226462,"text":"70226462 - 2021 - A new species of Helianthus (Asteracae) from Clark County, Nevada","interactions":[],"lastModifiedDate":"2021-11-18T12:39:36.836733","indexId":"70226462","displayToPublicDate":"2021-09-17T06:38:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2639,"text":"Madroño","active":true,"publicationSubtype":{"id":10}},"title":"A new species of Helianthus (Asteracae) from Clark County, Nevada","docAbstract":"Helianthus devernii T.M.Draper is described as a new endemic species from two small desert spring populations found within Red Rock Canyon National Conservation Area, Clark County, NV. Morphological data and nuclear ribosomal ITS marker data place it in section Ciliares series Pumili. Furthermore, the molecular data allies it most closely to H. pumilus Nutt. Helianthus devernii differs from H. pumilus by its sessile one nerved opposite and alternate leaves, glabrous glaucous stems, and overall smaller heads. The two known populations of H. devernii of approximately 400 individuals occur near the Las Vegas Valley and are threatened by heavy recreational use and exotic plants and animals. A key to the species of Helianthus of Nevada is presented.
The purpose of this report is to describe a possible approach to estimate changes in invertebrate taxa richness at sites with known water-quality trends but no invertebrate data. In this study, data from 1,322 sites were used to describe invertebrate response to changes in total nitrogen, total phosphorus, or specific conductance, and to estimate changes in invertebrate taxa richness at 259 sites with reported water-quality trends but no invertebrate data. Sites were stratified using propensity score analysis to control for confounding factors (for example, climate, land use, land cover). Generalized linear models were developed to describe changes in invertebrate taxa richness along gradients of total nitrogen, total phosphorus, and specific conductance values. The magnitude and direction of invertebrate response to gradients of water quality varied among parameters and strata, with changes in invertebrate taxa richness per natural log unit change in concentration ranging from –7 to +6. However, estimated changes in invertebrate taxa richness at sites with known water-quality trends were much less and did not exceed three taxa until changes in concentration were greater than 50 percent. Applying this approach provides (1) a first screening to identify where changes in invertebrate taxa richness are likely to occur and (2) the necessary groundwork to improve estimation of invertebrate response to trends in water quality where biological data are lacking.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215070","usgsCitation":"Zuellig, R.E., and Carlisle, D.M., 2021, Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable: U.S. Geological Survey Scientific Investigations Report 2021–5070, 24 p., https://doi.org/10.3133/sir20215070.","productDescription":"Report: v, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119660","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":389267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMFACO","text":"USGS data release","linkHelpText":"Datasets for estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable"},{"id":389266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5070/sir20215070.pdf","text":"Report","size":"5.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5070"},{"id":389265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5070/coverthb.jpg"}],"contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
The Rio Grande is the primary source of recharge to the Mesilla Basin/Conejos-Médanos aquifer system (“Mesilla Basin aquifer system”) in the Mesilla Valley of New Mexico and Texas. The Mesilla Basin aquifer system is the primary source of water supply to several large cities along the United States–Mexico border. Identifying gaining and losing reaches of the Rio Grande in the Mesilla Valley is therefore critical for managing the quality and quantity of surface and groundwater-resources available to stakeholders in the Mesilla Valley and downstream. A Waterborne gradient Self-Potential (WaSP) logging survey was completed in the Rio Grande across the Mesilla Valley between June 26 and July 2, 2020 to identify reaches where surface-water gains and losses were occurring by interpreting an estimate of the streaming-potential component of the electrostatic field in the river, measured during bank-full flow. The WaSP survey, completed as part of the Transboundary Aquifer Assessment Program, began at Leasburg Dam State Park, New Mexico near the northern terminus of the Mesilla Valley and ended ~72 kilometers (km) downstream in Canutillo, Texas. Electric potential data indicated a net losing condition for ~32 km between Leasburg Dam and Mesilla Diversion Dam in New Mexico, with one 200-m long reach showing a localized gaining condition. Downstream from Mesilla Diversion Dam, electric-potential data indicated a neutral-to-mild gaining condition for 12-km that transitioned to a mild-to-moderate gaining condition between 12 and ~22 km from the dam before transitioning back to a losing condition along the remaining 18 km of the survey reach. The interpreted gaining and losing reaches are substantiated by potentiometric surface mapping in hydrostratigraphic units of the Mesilla Basin aquifer system between 2010 and 2011 and streamflow gains and losses quantified from annual streamflow gaging at 16 stations along the survey reach between 1988 and 1998 and between 2004 and 2013. The gaining and losing reaches of the Rio Grande in the Mesilla Valley, interpreted from electric potential data, compare notably well with streamflow gains and losses quantified at 16 locations along the 72-km long survey reach.
","language":"English","publisher":"Environmental and Engineering Geophysical Society","usgsCitation":"Ikard, S., and Teeple, A., 2021, Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley: FastTIMES, v. 26, no. 3, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-132631","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":412505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412478,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://fasttimesonline.co/waterborne-gradient-self-potential-wasp-logging-in-the-rio-grande-to-map-localized-and-regional-surface-and-groundwater-exchanges-across-the-mesilla-valley/"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Mesilla Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.44524373222445,\n 31.760633532310962\n ],\n [\n -106.6117012652096,\n 32.435777766328556\n ],\n [\n -106.97488133717744,\n 32.96635814299131\n ],\n [\n -107.04549968450479,\n 33.44327742390567\n ],\n [\n -107.58018145712424,\n 33.388542989837234\n ],\n [\n -107.50956310979689,\n 32.79267559856237\n ],\n [\n -107.07576469050201,\n 32.350592719244176\n ],\n [\n -106.67223127720436,\n 31.854939592806915\n ],\n [\n -106.4502878998906,\n 31.709153308763334\n ],\n [\n -106.44524373222445,\n 31.760633532310962\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}