Hoary bats (Lasiurus cinereus) are among the bat species most commonly killed by wind turbine strikes in the midwestern United States. The impact of this mortality on species census size is not understood, due in part to the difficulty of estimating population size for this highly migratory and elusive species. Genetic effective population size (Ne) could provide an index of changing census population size if other factors affecting Ne are stable.
We used the NeEstimator package to derive effective breeding population size (Nb) estimates for two temporally spaced cohorts: 93 hoary bats collected in 2009–2010 and an additional 93 collected in 2017–2018. We sequenced restriction-site associated polymorphisms and generated a de novo genome assembly to guide the removal of sex-linked and multi-copy loci, as well as identify physically linked markers.
Analysis of the reference genome with psmc suggested at least a doubling of Ne in the last 100,000 years, likely exceeding Ne = 10,000 in the Holocene. Allele and genotype frequency analyses confirmed that the two cohorts were comparable, although some samples had unusually high or low observed heterozygosities. Additionally, the older cohort had lower mean coverage and greater variability in coverage, and batch effects of sampling locality were observed that were consistent with sample degradation. We therefore excluded samples with low coverage or outlier heterozygosity, as well as loci with sequence coverage far from the mode value, from the final data set. Prior to excluding these outliers, contemporary Nb estimates were significantly higher in the more recent cohort, but this finding was driven by high values for the 2018 sample year and low values for all other years. In the reduced data set, Nb did not differ significantly between cohorts. We found base substitutions to be strongly biased toward cytosine to thymine or the complement, and further partitioning loci by substitution type had a strong effect on Nb estimates. Minor allele frequency and base quality bias thresholds also had strong effects on Nb estimates. Instability of Nb with respect to common data filtering parameters and empirically identified factors prevented robust comparison of the two cohorts. Given that confidence intervals frequently included infinity as the stringency of data filtering increased, contemporary trends in Nb of North American hoary bats may not be tractable with the linkage disequilibrium method, at least using the protocol employed here.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.11285","usgsCitation":"Cornman, R.S., Fike, J., Oyler-McCance, S.J., and Cryan, P.M., 2021, Historical effective population size of North American hoary bat (Lasiurus cinereus) and challenges to estimating trends in contemporary effective breeding population size from archived samples: PeerJ, v. 9, e11285, 27 p., https://doi.org/10.7717/peerj.11285.","productDescription":"e11285, 27 p.","ipdsId":"IP-125432","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452631,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.11285","text":"Publisher Index Page"},{"id":436402,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VSG54Z","text":"USGS data release","linkHelpText":"Genetic variation in hoary bats (Lasiurus cinereus) assessed from archived samples"},{"id":385284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814605,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263927,"text":"70263927 - 2021 - Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry","interactions":[],"lastModifiedDate":"2025-02-28T15:50:17.631195","indexId":"70263927","displayToPublicDate":"2021-04-19T09:46:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry","docAbstract":"Minor changes in geometry along the length of mature strike-slip faults may act as conditional barriers to earthquake rupture, terminating some and allowing others to pass. This hypothesis remains largely untested because palaeoearthquake data that constrain spatial and temporal patterns of fault rupture are generally imprecise. Here we develop palaeoearthquake event data that encompass the last 20 major-to-great earthquakes along approximately 320 km of the Alpine Fault in New Zealand with sufficient temporal resolution and spatial coverage to reveal along-strike patterns of rupture extent. The palaeoearthquake record shows that earthquake terminations tend to cluster in time near minor along-strike changes in geometry. These terminations limit the length to which rupture can grow and produce two modes of earthquake behaviour characterized by phases of major (Mw 7–8) and great (Mw > 8) earthquakes. Physics-based simulations of seismic cycles closely resemble our observations when parameterized with realistic fault geometry. Switching between the rupture modes emerges due to heterogeneous stress states that evolve over multiple seismic cycles in response to along-strike differences in geometry. These geometric complexities exert a first-order control on rupture behaviour that is not currently accounted for in fault-source models for seismic hazard.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-021-00721-4","usgsCitation":"Howarth, J., Barth, N.C., Fitzsimons, S., Richards-Dinger, K.B., Clark, K., Biasi, G., Cochran, U., Langridge, R.M., Berryman, K., and Sutherland, R., 2021, Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry: Nature Geoscience, v. 14, p. 314-320, https://doi.org/10.1038/s41561-021-00721-4.","productDescription":"7 p.","startPage":"314","endPage":"320","ipdsId":"IP-123083","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"South Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 166.14130042117392,\n -45.855254614357875\n ],\n [\n 167.55703138687022,\n -47.72709088799331\n ],\n [\n 171.04329453684727,\n -45.90628854385326\n ],\n [\n 172.01003756617126,\n -44.16671517350856\n ],\n [\n 173.28236889170694,\n -43.93388994721674\n ],\n [\n 173.01583587447817,\n -43.26711906105943\n ],\n [\n 174.6610670093284,\n -41.74200766025002\n ],\n [\n 174.2430988469256,\n -40.59327885176259\n ],\n [\n 172.24758921841533,\n -40.37688312712664\n ],\n [\n 171.32279188614189,\n -41.531725778001864\n ],\n [\n 169.84538818345476,\n -43.00565520661165\n ],\n [\n 167.88893104162912,\n -43.84009881514205\n ],\n [\n 166.14130042117392,\n -45.855254614357875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Howarth, Jamie D.","contributorId":351620,"corporation":false,"usgs":false,"family":"Howarth","given":"Jamie D.","affiliations":[{"id":34109,"text":"Victoria University of Wellington, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Nicolas C.","contributorId":206132,"corporation":false,"usgs":false,"family":"Barth","given":"Nicolas","email":"","middleInitial":"C.","affiliations":[{"id":37254,"text":"University of California, Riverside, CA","active":true,"usgs":false}],"preferred":false,"id":929133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzsimons, Sean J.","contributorId":351621,"corporation":false,"usgs":false,"family":"Fitzsimons","given":"Sean J.","affiliations":[{"id":13378,"text":"University of Otago, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richards-Dinger, Keith B.","contributorId":198155,"corporation":false,"usgs":false,"family":"Richards-Dinger","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":929135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Kate","contributorId":295749,"corporation":false,"usgs":false,"family":"Clark","given":"Kate","email":"","affiliations":[],"preferred":false,"id":929136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cochran, Ursula A.","contributorId":351622,"corporation":false,"usgs":false,"family":"Cochran","given":"Ursula A.","affiliations":[{"id":26939,"text":"GNS Science, Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Langridge, Robert M.","contributorId":175117,"corporation":false,"usgs":false,"family":"Langridge","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929139,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Berryman, Kelvin R.","contributorId":351623,"corporation":false,"usgs":false,"family":"Berryman","given":"Kelvin R.","affiliations":[{"id":26939,"text":"GNS Science, Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929140,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sutherland, Rupert 0000-0001-7430-0055","orcid":"https://orcid.org/0000-0001-7430-0055","contributorId":278669,"corporation":false,"usgs":false,"family":"Sutherland","given":"Rupert","email":"","affiliations":[{"id":57245,"text":"School of Geography, Environment and Earth Sciences, Victoria University of Wellington","active":true,"usgs":false}],"preferred":false,"id":929141,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70222437,"text":"70222437 - 2021 - Organo-facies and mineral effects on sorption capacity of low-maturity Permian Barakar shales from the Auranga Basin, Jharkhand, India","interactions":[],"lastModifiedDate":"2021-07-30T14:14:46.655583","indexId":"70222437","displayToPublicDate":"2021-04-19T09:12:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1506,"text":"Energy & Fuels","active":true,"publicationSubtype":{"id":10}},"title":"Organo-facies and mineral effects on sorption capacity of low-maturity Permian Barakar shales from the Auranga Basin, Jharkhand, India","docAbstract":"Shales associated with the Lower Permian (Barakar Formation) sediments of the Auranga Coalfield, India, occur in the immature–early mature stage. The sorption capacity of Barakar shale samples has been studied through high-pressure methane (CH4) adsorption and low-pressure N2 gas adsorption (LPN2GA) methods, supported with proximate analyses, programmed pyrolysis, optical petrography, and with energy-dispersive spectroscopy, X-ray diffraction, and inductively coupled plasma mass spectrometry. The sorption capacity is a function of the organic and inorganic constituents present in the shale samples. The methane sorption capacity (MSC) and Langmuir volume of the shale samples vary from 0.217 to 0.314 and 0.315 to 0.429 mmol/g rock, respectively. The BET-calculated surface area of the studied shales varies from 8.12 to 30.36 m2/g. The sorption capacities show the importance of the total organic content (TOC) through weak but positive correlations with MSC (r2 = 0.45) and S1 values (mg hydrocarbons/g rock from programmed pyrolysis; r2 = 0.40). Moreover, apparent inverse relationships were observed between MSC and clay mineral abundances, suggesting that individual clay mineral types may influence MSC, although more work is needed. The TOC-normalized MSC (MSC*) of shale samples shows a positive trend with quartz plus clay mineral content and ash yield of r2 = 0.64 for both. In addition, MSC* shows a negative logarithmic relationship with S1 + S2 (r2 = 0.63) and a positive linear relationship with TOC-normalized total organic matter (TOM*) (r2 = 0.88, when 5 low TOM* samples are excluded) indicating complex relationships possibly including bitumen retention in the sample pore spaces. The micropore study of the samples through LPN2GA, applying Dubinin–Radushkevich, Dubinin–Astakhov, and density functional theory models, shows the dominance of micro-mesopore concentrations in the shale matrix of ∼2 nm pore diameter. However, these pores might be present as blind or closed pores. The presence of thorium and zirconium is reflective of terrigenous detrital matter, i.e., moderately to strongly recycled sediments. The fluviatile facies of deposited shales in the Auranga Coalfield are noted by the significant presence of kaolinite (32.5–78.3%), which suggests the importance of its effect on the sorption capacity of proximal terrigenous shales.
Coking coal, or metallurgical coal, has been produced in the United States for nearly 200 years. Coking coal is primarily used in the production of coke for use in the steel industry, and for other uses (for example, foundries, blacksmithing, heating buildings, and brewing). Currently, U.S. coking coal is produced in Alabama, Arkansas, Pennsylvania, Virginia , and West Virginia. Historically, coking coal has also been produced in 15 other states (Alaska, Colorado, Georgia, Illinois, Indiana, Kentucky, Maryland, Montana, New Mexico, Ohio, Oklahoma, Tennessee, Utah, Washington, and Wyoming), but currently is not. Coals from the Appalachian, Arkoma, and Illinois basins are Pennsylvanian in age, while coals in Alaska, Colorado, Montana, New Mexico, Utah, Washington, and Wyoming range in age from Early Cretaceous through Eocene.
This Open-File Report presents the geographic locations of current and historical coking coal deposits of the United States, with additional information about recent and historical mining and exploration activities. Chemical, rheological, petrographic, and other criteria for evaluating the coking potential of coals are discussed, and historical data for coking coals in the United States are presented. In addition, new coking coal samples from Alabama, Arkansas, Kentucky, and Oklahoma were collected and analyzed for this report, and the data are presented in multiple tables, including proximate and ultimate analyses; calorific value; sulfur forms; major-, minor-, and trace-element abundances; Free-Swelling Index; Gieseler Plastometer analyses; American Society for Testing and Materials (ASTM) dilatation; coal petrography; and predicted values of Coal Stability Factor and Coal Strength after Reaction with CO2 (pCSF and pCSR, respectively). Data from previously analyzed coking coal samples in Kentucky, Pennsylvania, Virginia, and West Virginia were supplied by three companies, including results from all the tests listed above, plus oxidation, Hardgrove Grindability Index, and ash fusion (in a reducing environment) temperatures are also presented in tables in the report.
Geographic Information System (GIS) data compiled for this project are available for download for public and private utilization and may be used to create maps for a variety of energy resource studies. These GIS data are in shapefile format, and metadata files are included describing all GIS processing. Additional geographic information about coking coal areas of the United States are also presented in tabular format in the report, including the following: names of coal basins, fields, regions, districts, and areas; coal beds or zones; geographic locations including States, counties, towns, rivers, mountains, etc.; stratigraphic hierarchy and age of the coal-bearing interval; coking characteristics including sulfur content, ash yield, volatile matter, moisture, calorific value, and Free-Swelling Index; coal rank; names of coal mines and coal-mining companies; current and past mining activity; and references for reports about the coal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201113","usgsCitation":"Trippi, M.H., Ruppert, L.F., Eble, C.F., and Hower, J.C., 2021, Coking coal of the United States—Modern and historical coking coal mining locations and chemical, rheological, petrographic, and other data from modern samples: U.S. Geological Survey Open-File Report 2020–1113, 112 p., https://doi.org/10.3133/ofr20201113.","productDescription":"Report: xi, 112 p.; Tables 1.1-21.1; Data Release; Metadata; Spatial Data","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111543","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":381899,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1113/ofr20201113_appendix_tables_csv.zip","text":"Tables","size":"88.9 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Zip file of tables 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49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.
","language":"English","publisher":"Wiley Publishing","doi":"10.1111/gwat.13106","usgsCitation":"Hunt, R., White, J., Duncan, L.L., Haugh, C., and Doherty, J.E., 2021, Evaluating lower computational burden approaches for calibration of large environmental models: Groundwater, v. 59, no. 6, p. 788-798, https://doi.org/10.1111/gwat.13106.","productDescription":"11 p.","startPage":"788","endPage":"798","ipdsId":"IP-126431","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":452645,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13106","text":"Publisher Index Page"},{"id":436403,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AR7Y02","text":"USGS data release","linkHelpText":"MODFLOW-NWT models and calibration files for the Mississippi Alluvial Plain, USA"},{"id":387106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi Embayment regional aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.04296874999999,\n 32.69486597787505\n ],\n [\n -87.275390625,\n 32.69486597787505\n ],\n [\n -87.275390625,\n 39.774769485295465\n ],\n [\n -94.04296874999999,\n 39.774769485295465\n ],\n [\n -94.04296874999999,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"59","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":208800,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[],"preferred":true,"id":819023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":248830,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":50032,"text":"GNS New Zealand","active":true,"usgs":false}],"preferred":false,"id":819024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duncan, Leslie L. 0000-0002-5938-5721","orcid":"https://orcid.org/0000-0002-5938-5721","contributorId":204004,"corporation":false,"usgs":true,"family":"Duncan","given":"Leslie","email":"","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819025,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haugh, Connor J. 0000-0002-5204-8271","orcid":"https://orcid.org/0000-0002-5204-8271","contributorId":219945,"corporation":false,"usgs":true,"family":"Haugh","given":"Connor J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819026,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, John E.","contributorId":8817,"corporation":false,"usgs":false,"family":"Doherty","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":7046,"text":"Watermark Numerical Computing","active":true,"usgs":false}],"preferred":false,"id":819027,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222092,"text":"70222092 - 2021 - Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags","interactions":[],"lastModifiedDate":"2021-07-19T23:55:57.038029","indexId":"70222092","displayToPublicDate":"2021-04-17T18:48:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags","docAbstract":"Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges. We also used simulations to determine how different sampling designs influence the estimability of transition probabilities. We then parameterized the model with tracking data and mark–resight data from declining Painted Bunting (Passerina ciris) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, nonbreeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of the full annual cycle ecology of migratory species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duab010","usgsCitation":"Rushing, C.S., Van Tatenhove, A.M., Sharp, A., Ruiz-Gutierrez, V., Freeman, M., Sykes, P.W., Given, A.M., and Sillett, T., 2021, Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags: Ornithological Applications, v. 123, no. 2, duab010, https://doi.org/10.1093/ornithapp/duab010.","productDescription":"duab010","ipdsId":"IP-118948","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":452649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duab010","text":"Publisher Index Page"},{"id":387261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rushing, Clark S","contributorId":237020,"corporation":false,"usgs":false,"family":"Rushing","given":"Clark","email":"","middleInitial":"S","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":819483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Tatenhove, Aimee M","contributorId":261211,"corporation":false,"usgs":false,"family":"Van Tatenhove","given":"Aimee","email":"","middleInitial":"M","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":819484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharp, Andrew","contributorId":261213,"corporation":false,"usgs":false,"family":"Sharp","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":819485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruiz-Gutierrez, Viviana","contributorId":261212,"corporation":false,"usgs":false,"family":"Ruiz-Gutierrez","given":"Viviana","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":819486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sykes, Paul W.","contributorId":214917,"corporation":false,"usgs":false,"family":"Sykes","given":"Paul","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":819489,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Given, Aaron M.","contributorId":49474,"corporation":false,"usgs":true,"family":"Given","given":"Aaron","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":819490,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sillett, T. Scott","contributorId":80788,"corporation":false,"usgs":false,"family":"Sillett","given":"T. Scott","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":819487,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228492,"text":"70228492 - 2021 - Range expansion and factors affecting abundance of invasive Flathead Catfish in the Delaware and Susquehanna Rivers, Pennsylvania, USA","interactions":[],"lastModifiedDate":"2022-02-11T19:14:55.784154","indexId":"70228492","displayToPublicDate":"2021-04-16T12:56:38","publicationYear":"2021","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":"Range expansion and factors affecting abundance of invasive Flathead Catfish in the Delaware and Susquehanna Rivers, Pennsylvania, USA","docAbstract":"Flathead Catfish Pylodictis olivaris have been either intentionally or accidentally introduced into Atlantic Slope drainages extending from Florida to Pennsylvania and have quickly become established. In Pennsylvania, Flathead Catfish were first detected in the Schuylkill River at the Fairmont Dam in 1999 and in the Susquehanna River at Safe Harbor Dam in 2002. The species has since moved throughout the respective basins, with subsequent detections during 244 riverine surveys in these drainages. Fishway and electrofishing surveys in the tidal Schuylkill River, a Delaware River tributary, have documented an increase in abundances since 2004, when the surveys were first implemented. Hoop-net surveys in nontidal large-river reaches found mean (±SD) catch rates varying from 0.00 to 4.51 ± 4.38 fish/series. A Bayesian hierarchical Poisson regression model indicated that Flathead Catfish abundance decreased as the distance from the initial point of detection increased, demonstrating a general pattern of fish expansion upstream from the point of detection. The distance downstream of the nearest dam, although not significant, had a relatively high posterior probability of being negatively correlated with Flathead Catfish abundance. Ongoing and future targeted surveys should help to better understand changes in the distribution and abundance of Flathead Catfish in these systems.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10628","usgsCitation":"Smith, G.D., Massie, D.L., Perillo, J., Wagner, T., and Pierce, D., 2021, Range expansion and factors affecting abundance of invasive Flathead Catfish in the Delaware and Susquehanna Rivers, Pennsylvania, USA: North American Journal of Fisheries Management, v. 41, no. S1, p. S205-S220, https://doi.org/10.1002/nafm.10628.","productDescription":"16 p.","startPage":"S205","endPage":"S220","ipdsId":"IP-116902","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Delaware River, Juniata River, Lehigh River, Schuylkill River, Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.7774658203125,\n 39.7240885773337\n ],\n [\n -75.73974609375,\n 39.7240885773337\n ],\n [\n -75.16845703124999,\n 39.80853604144591\n ],\n [\n -74.619140625,\n 40.111688665595956\n ],\n [\n -75.16845703124999,\n 40.713955826286046\n ],\n [\n -74.94873046875,\n 40.863679665481676\n ],\n [\n -75.08056640625,\n 40.9964840143779\n ],\n [\n -74.739990234375,\n 41.45919537950706\n ],\n [\n -74.81689453125,\n 41.463311976686235\n ],\n [\n -74.9542236328125,\n 41.50446357504803\n ],\n [\n -75.0311279296875,\n 41.611335399441735\n ],\n [\n -75.0311279296875,\n 41.775408403663285\n ],\n [\n -75.1025390625,\n 41.87774145109676\n ],\n [\n -75.223388671875,\n 41.89001042401827\n ],\n [\n -75.322265625,\n 42.00848901572399\n ],\n [\n -78.7335205078125,\n 42.00032514831621\n ],\n [\n -78.7774658203125,\n 39.7240885773337\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"S1","noUsgsAuthors":false,"publicationDate":"2021-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Geoffrey D.","contributorId":274361,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","email":"","middleInitial":"D.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":834438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massie, Danielle L.","contributorId":196717,"corporation":false,"usgs":false,"family":"Massie","given":"Danielle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":834439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perillo, Joseph","contributorId":275966,"corporation":false,"usgs":false,"family":"Perillo","given":"Joseph","email":"","affiliations":[{"id":56915,"text":"Philadelphia Water Department","active":true,"usgs":false}],"preferred":false,"id":834440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pierce, Daryl","contributorId":276044,"corporation":false,"usgs":false,"family":"Pierce","given":"Daryl","email":"","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":834514,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219983,"text":"ofr20211033 - 2021 - Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","interactions":[],"lastModifiedDate":"2021-04-19T11:44:39.479074","indexId":"ofr20211033","displayToPublicDate":"2021-04-16T12:10:46","publicationYear":"2021","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":"2021-1033","displayTitle":"Connectivity of Mojave Desert Tortoise Populations: Management Implications for Maintaining a Viable Recovery Network","title":"Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","docAbstract":"The historic distribution of Mojave desert tortoises (Gopherus agassizii) was relatively continuous across the range, and the importance of tortoise habitat outside of designated tortoise conservation areas (TCAs) to recovery has long been recognized for its contributions to supporting gene flow between TCAs and to minimizing impacts and edge effects within TCAs. However, connectivity of Mojave desert tortoise populations has become a concern because of recent and proposed development of large tracts of desert tortoise habitat that cross, fragment, and surround designated conservation areas. This paper summarizes the underlying concepts and importance of connectivity for Mojave desert tortoise populations by reviewing current information on connectivity and providing information to managers for maintaining or enhancing desert tortoise population connectivity as they consider future proposals for development and management actions.
Maintaining an ecological network for the Mojave desert tortoise, with a system of core habitats (TCAs) connected by linkages, is necessary to support demographically viable populations and long-term gene flow within and between TCAs. There are four points for wildlife and land-management agencies to consider when making decisions that could affect connectivity of Mojave desert tortoise populations (for example, in updating actions in resource management plans or amendments that could help maintain or restore functional connectivity in light of the latest information):
Each TCA has unique strengths and weaknesses regarding its ability to support minimum sustainable populations based on areal extent and its ability to support population increases based on landscape connection with adjacent populations. Considering how proposed projects (inside or outside of TCAs) affect connectivity and the ability of TCAs to support at least 5,000 adult tortoises (the numerical goal for each TCA) could help managers to maintain the resilience of TCAs to population declines. The same project, in an alternative location, could have very different impacts on local and regional populations. For example, within the habitat matrix surrounding TCAs, narrowly delineated corridors may not allow for natural population dynamics if they do not accommodate overlapping home ranges along most of their widths so that tortoises reside, grow, find mates, and produce offspring that can replace older tortoises. In addition, most habitat outside TCAs may receive more surface disturbance than habitat within TCAs. Therefore, managing the entire remaining matrix of desert tortoise habitat for permeability may be better than delineating fixed corridors. These concepts apply, especially given uncertainty about long-term condition of habitat, within and outside of TCAs under a changing climate.
Ultimately, questions such as “What are the critical linkages that need to be protected?” could be better framed as “How can we manage the remaining habitat matrix in ways that sustain ecological processes and habitat suitability for special status species?” Land-management decisions made in the context of the latter question may be more conducive to maintenance of a functional ecological network.
In California, the Bureau of Land Management established 0.1–1.0 percent caps on new surface-disturbance for TCAs and mapped linkages that address the issues described in number 1 of this list.
Nevada, Utah, and Arizona currently do not have surface-disturbance limits. Limits comparable to those in the Desert Renewable Energy Conservation Plan (DRECP) would be 0.5 percent within TCAs and 1 percent within the linkages modeled by Averill-Murray and others (2013). Limits in some areas of California within the Desert Renewable Energy Conservation Plan, such as Ivanpah Valley, are more restrictive, at 0.1 percent. Continuity across the state line in Nevada could be achieved with comparable limits in the adjacent portion of Ivanpah Valley, as well as the Greater Trout Canyon Translocation Area and the Stump Springs Regional Augmentation Site. These more restrictive limits would help protect remaining habitat in the major interstate connectivity pathway through Ivanpah Valley and focal areas of population augmentation that provide additional population connectivity along the western flank of the Spring Mountains.
In a recent study that analyzed 13 years of desert tortoise monitoring data, nearly all desert tortoise observations were at sites in which 5 percent or less of the surrounding landscape within 1 kilometer was disturbed (Carter and others, 2020a). To help maintain tortoise habitability and permeability across all other non-conservation-designated tortoise habitat, all surface disturbance could be limited to less than 5-percent development per square kilometer because the 5-percent threshold for development is the point at which tortoise occupation drops precipitously (Carter and others, 2020a). However, although individual desert tortoises were observed at development levels up to 5 percent, we do not know the fitness or reproductive characteristics of these individuals. This level of development also may not allow for long-term persistence of healthy populations that are of adequate size needed for demographic or functional connectivity; therefore, a conservative interpretation suggests that, ideally, development could be lower. Lower development levels would be particularly useful in areas within the upper 5th percentile of connectivity values modeled by Gray and others (2019).
Reducing ancillary threats in places where connectivity is restricted to narrow strips of habitat, for example, narrow mountain passes or vegetated strips between solar development, could enhance the functionality of these vulnerable linkages. In such areas, maintaining multiple, redundant linkages could further enhance overall connectivity.
Minimization of mortality from roads and maximization of passage under roads. Roads pose a significant threat to the long-term persistence of local tortoise populations, and roads of high traffic volume lead to severe population declines, which ultimately fragments populations farther away from the roads. Three points (a.–c.) pertain to reducing direct mortality of tortoises on the many paved roads that cross desert tortoise habitat and to maintaining a minimal level of permeability across these roads:
Tortoise-exclusion fencing tied into culverts, underpasses, overpasses, or other passages below roads in desert tortoise habitat, would limit vehicular mortality of tortoises and provide opportunities for movement across the roads. Installation of shade structures on the habitat side of fences installed in areas with narrow population-depletion zones would limit overheating of tortoises that may pace the fence.
Passages below highways could be maintained or retrofitted to ensure safe tortoise access, for example, by filling eroded drop-offs or modifying erosion-control features such as rip-rap to make them safer and more passable for tortoises. Wildlife management agencies could work with transportation departments to develop construction standards that are consistent with hydrologic/erosion management goals, while also incorporating a design and materials consistent with tortoise survival and passage and make the standards widely available. The process would be most effective if the status of passages was regularly monitored and built into management plans.
Healthy tortoise populations along fenced highways could be supported by ensuring that land inside tortoise-exclusion fences is not so degraded that it leads to degradation of tortoise habitat outside the exclusion areas. For example, severe invasive plant infestations inside a highway exclusion could cause an increase of invasive plants outside the exclusion area and degrade habitat; therefore, invasive plants inside road rights of way could be mown or treated with herbicide to limit their spread into adjacent tortoise habitat and minimize the risk of these plants carrying wildfires into adjacent habitat.
Adaptation of management based on new information. Future research will continue to build upon and refine models related to desert tortoise population connectivity and develop new ones. New models could consider landscape levels of development and be constructed such that they share common foundations to support future synthesis efforts. If model development was undertaken in partnership with entities that are responsible for management of desert tortoise habitat, it would facilitate incorporation of current and future modeling results into their land management decisions. There are specific topics that may be clarified with further evaluation:
The effects of climate change on desert tortoise habitat, distribution, and population connectivity;
The effects of large-scale fires, especially within repeatedly burned habitat, on desert tortoise distribution and population connectivity;
The ability of solar energy facilities or similar developments to support tortoise movement and presence by leaving washes intact; leaving native vegetation intact whenever possible, or if not possible, mowing the site, allowing vegetation to re-sprout, and managing weeds; and allowing tortoises to occupy the sites; and
The design and frequency of underpasses necessary to maintain functional demographic and genetic connectivity across linear features, like highways.
Wildlife Program
Prepared in cooperation with the U.S. Fish and Wildlife Service
","usgsCitation":"Averill-Murray, R.C., Esque, T.C., Allison, L.J., Bassett, S., Carter, S.K., Dutcher, K.E., Hromada, S.J., Nussear, K.E., and Shoemaker, K., 2021, Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network: U.S. Geological Survey Open-File Report 2021–1033, 23 p., https://doi.org/10.3133/ofr20211033.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-125269","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":385161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1033/covrthb.jpg"},{"id":385162,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":385163,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1033/images"},{"id":385164,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.xml"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.71923828124999,\n 33.669496972795535\n ],\n [\n -113.8623046875,\n 33.578014746143985\n ],\n [\n -112.69775390625,\n 33.50475906922609\n ],\n [\n -111.51123046875,\n 33.284619968887675\n ],\n [\n -111.73095703125,\n 34.10725639663118\n ],\n [\n -111.9287109375,\n 35.51434313431818\n ],\n [\n -113.00537109375,\n 36.24427318493909\n ],\n [\n -114.3896484375,\n 36.73888412439431\n ],\n [\n -115.86181640625001,\n 37.07271048132943\n ],\n [\n -117.42187500000001,\n 37.68382032669382\n ],\n [\n -118.27880859375001,\n 37.579412513438385\n ],\n [\n -117.7734375,\n 35.97800618085566\n ],\n [\n -117.72949218749999,\n 35.44277092585766\n ],\n [\n -118.76220703125001,\n 34.75966612466248\n ],\n [\n -117.99316406249999,\n 34.488447837809304\n ],\n [\n -116.74072265625,\n 34.288991865037524\n ],\n [\n -114.71923828124999,\n 33.669496972795535\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Interactions between climate and hydrogeologic settings contribute to the hydrologic and chemical variability among depressional wetlands, which influences their aquatic communities. These interactions and resulting variability have led to inconsistent results in terms of identifying reliable predictors of aquatic-macroinvertebrate community composition for depressional wetlands. This is especially true in the Prairie Pothole Region of North America where, in addition to pronounced climate variability, studies are often confounded by fish introductions. We used environmental monitoring data collected over a 24-year period from a complex of sixteen depressional wetlands and structural equation modeling techniques that incorporated theoretical and empirical relationships outlined in the Wetland Continuum to identify key environmental (climate and hydrogeologic setting) and biotic (competition and predation) drivers of aquatic-macroinvertebrate community composition for prairie-pothole wetlands. Uplands in the study area were primarily native prairie, thus, embedded wetlands were impacted minimally by agricultural influences. Additionally, study wetlands were predominately fishless. In the absence of the overwhelming influence of fishes, major drivers influencing aquatic-macroinvertebrate communities were revealed through the use of data spanning multidecadal-long climate cycles. We found variables related to the placement of wetlands along axes of the Wetland Continuum, e.g., hydrogeologic setting (relative wetland elevation) and hydroclimatic setting (proportion of wetland ponded), to be influential drivers of within-wetland habitat characteristics, such as the proportion of open-water area, which in turn was the strongest predictor of macroinvertebrate community composition. In contrast, predatory invertebrate and salamander abundance and non-predatory invertebrate biomass (i.e., predation and competition) were found to have minimal influence on community composition.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107678","usgsCitation":"McLean, K., Mushet, D.M., Newton, W.E., and Sweetman, J.N., 2021, Long-term multidecadal data from a prairie-pothole wetland complex reveal controls on aquatic-macroinvertebrate communities: Ecological Indicators, v. 126, 107678, 11 p., https://doi.org/10.1016/j.ecolind.2021.107678.","productDescription":"107678, 11 p.","ipdsId":"IP-094142","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":452658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107678","text":"Publisher Index Page"},{"id":396116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.70600509643555,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.85014598272475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248538,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sweetman, Jon N.","contributorId":279537,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":835109,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228945,"text":"70228945 - 2021 - Exploring the contemporary relationship between predator and prey in a significant, reintroduced Lahontan Cutthroat Trout population","interactions":[],"lastModifiedDate":"2022-02-25T14:47:41.142779","indexId":"70228945","displayToPublicDate":"2021-04-16T08:44:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the contemporary relationship between predator and prey in a significant, reintroduced Lahontan Cutthroat Trout population","docAbstract":"Lahontan Cutthroat Trout (LCT) Oncorhynchus clarkii henshawi have experienced some of the most marked reductions in abundance and distribution among Cutthroat Trout subspecies. The population of LCT in Pyramid Lake, Nevada has returned from the brink of extirpation, and although it is highly managed via stocking, the population is thriving and has recently started to reproduce naturally. Our objectives were to determine (1) whether predator and prey remain tightly coupled, (2) whether LCT are food limited, and (3) the status of the LCT population with regard to the potential prey-based contemporary carrying capacity. We used a multifaceted approach, including intensive field sampling of fish, bioenergetics modeling, cohort reconstruction, and comparisons of prey availability to consumption. We estimated that the average population of LCT in Pyramid Lake is 1.2 million, average annual stocking is 650,000, and the number of fish angled ranges from 5,000 to 14,000 per year, with a 90% release rate. Driven by seasonal and size variation in consumption, individual annual consumption by LCT varied from 667 to 992 g/year for small LCT (200–400 mm TL) and from 2,388 to 3,057 g/year for large LCT (>400 mm TL). Lahontan Cutthroat Trout are consuming, on average, 14–63% of the standing crop of Tui Chub Siphateles bicolor annually, indicating that LCT are currently not exceeding their prey-based carrying capacity. The LCT in Pyramid Lake remain tightly coupled to their primary native prey, Tui Chub, despite considerable changes to the ecosystem; therefore, managing for a robust population of LCT translates largely to managing for forage fish. This supply-versus-demand issue is of particular concern for Pyramid Lake given that the density of Tui Chub may be declining concordant with declining lake elevation. Given the conservation importance of this LCT population, careful monitoring is critical; however, “predation inertia” indicates that effective short-term management in response to fluctuations in forage fishes is likely possible.
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Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
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We piloted the use of smartphones to monitor wildlife in the Riverside East Solar Energy Zone (California) and at Indiana Dunes National Park (Indiana). For both efforts, we established remote autonomous monitoring stations in which we housed an Android smartphone in a weather-proof box mounted to a pole and powered by solar panels. We connected each smartphone to a Google account, and the smartphone received its recording/photo schedule daily via a Google Calendar connection when in data transmission mode. Phones were automated by Tasker, an Android application for automating cell phone tasks. We describe a simple approach that could be adopted by others who wish to use nonproprietary methods of data collection and analysis.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-071","usgsCitation":"Donovan, T.M., Balantic, C., Katz, J., Massar, M., Knutson, R., Duh, K., Jones, P., Epstein, K., Lacasse-Roger, J., and Dias, J., 2021, Remote ecological monitoring with smartphones and tasker: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 163-173, https://doi.org/10.3996/JFWM-20-071.","productDescription":"11 p.","startPage":"163","endPage":"173","ipdsId":"IP-122817","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":452688,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-071","text":"Publisher Index Page"},{"id":396620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Indiana","otherGeospatial":"Indiana Dunes National Park, Riverside East Solar Energy 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sequences","docAbstract":"This study addresses questions about the productivity of Cascadia mainshock–aftershock sequences using earthquake catalogs produced by the Geological Survey of Canada and the Pacific Northwest Seismic Network. Questions concern the likelihood that future moderate to large intermediate depth intraslab earthquakes in Cascadia would have as few detectable aftershocks as those documented since 1949. More broadly, for Cascadia, we consider if aftershock productivities vary spatially, if they are outliers among global subduction zones, and if they are consistent with a physical model in which aftershocks are clock‐advanced versions of tectonically driven background seismicity. A practical motivation for this study is to assess the likely accuracy of aftershock forecasts based on productivities derived from global data that are now being issued routinely by the U.S. Geological Survey. For this reason, we estimated productivity following the identical procedures used in those forecasts and described in Page et al. (2016). Results indicate that in Cascadia we can say that the next intermediate depth intraslab earthquake will likely have just a few detectable aftershocks and that aftershock productivity appears to be an outlier among global subduction zones, with rates that on average are lower by more than half, except for mainshocks in the upper plate. Our results are consistent with a clock‐advance model; productivities may be related to the proximity of mainshocks to a population of seismogenic fault patches and correlate with background seismicity rates. The latter and a clear correlation between productivities with mainshock depth indicate that both factors may have predictive value for aftershock forecasting.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200344","usgsCitation":"Gomberg, J.S., and Bodin, P., 2021, The productivity of Cascadia aftershock sequences: Bulletin of the Seismological Society of America, v. 111, no. 3, p. 1494-1507, https://doi.org/10.1785/0120200344.","productDescription":"14 p.","startPage":"1494","endPage":"1507","ipdsId":"IP-123911","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130,\n 50\n ],\n [\n -130,\n 40\n ],\n [\n -120,\n 40\n ],\n [\n -120,\n 50\n ],\n [\n -130,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"111","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-04-13","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":924645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodin, Paul","contributorId":339818,"corporation":false,"usgs":false,"family":"Bodin","given":"Paul","affiliations":[],"preferred":false,"id":924646,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223120,"text":"70223120 - 2021 - Regional calibration of hybrid ground‐motion simulations in moderate seismicity areas: Application to the Upper Rhine Graben","interactions":[],"lastModifiedDate":"2021-08-11T12:10:17.371176","indexId":"70223120","displayToPublicDate":"2021-04-13T07:00:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Regional calibration of hybrid ground‐motion simulations in moderate seismicity areas: Application to the Upper Rhine Graben","docAbstract":"This study presents the coupling of the spectral decomposition results for anelastic attenuation, stress drop, and site effects with the Graves‐Pitarka (GP) hybrid ground‐motion simulation methodology, as implemented on the Southern California Earthquake Center (SCEC) broadband platform (BBP). It is targeted to applications in the Upper Rhine graben (URG), which is among the seismically active areas in western Europe, yet a moderate seismicity area. Our development consists of three main steps: (1) calibration of regional high‐frequency (HF) attenuation properties; (2) modification of the hybrid approach to add compressional waves in the HF computation and examine various strategies to evaluate site amplification factors in the Fourier domain (e.g.,
Methylmercury concentrations vary widely across geographic space and among habitat types, with marine and aquatic-feeding organisms typically exhibiting higher mercury concentrations than terrestrial-feeding organisms. However, there are few model organisms to directly compare mercury concentrations as a result of foraging in marine, estuarine, or terrestrial food webs. The ecological impacts of differential foraging may be especially important for generalist species that exhibit high plasticity in foraging habitats, locations, or diet. Here, we investigate whether foraging habitat, sex, or fidelity to a foraging area impact blood mercury concentrations in western gulls (Larus occidentalis) from three colonies on the US west coast. Cluster analyses showed that nearly 70% of western gulls foraged primarily in ocean or coastal habitats, whereas the remaining gulls foraged in terrestrial and freshwater habitats. Gulls that foraged in ocean or coastal habitats for half or more of their foraging locations had 55% higher mercury concentrations than gulls that forage in freshwater and terrestrial habitats. Ocean-foraging gulls also had lower fidelity to a specific foraging area than freshwater and terrestrial-foraging gulls, but fidelity and sex were unrelated to gull blood mercury concentrations in all models. These findings support existing research that has described elevated mercury levels in species using aquatic habitats. Our analyses also demonstrate that gulls can be used to detect differences in contaminant exposure over broad geographic scales and across coarse habitat types, a factor that may influence gull health and persistence of other populations that forage across the land-sea gradient.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemosphere.2021.130470","usgsCitation":"Clatterbuck, C.A., Lewison, R.L., Orben, R.A., Ackerman, J.T., Torres, L., Suryan, R.M., Warzybok, P., Jahncke, J., and Shaffer, S.A., 2021, Foraging in marine habitats increases mercury concentrations in a generalist seabird: Chemosphere, v. 279, 130470, 9 p., https://doi.org/10.1016/j.chemosphere.2021.130470.","productDescription":"130470, 9 p.","ipdsId":"IP-125235","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452708,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemosphere.2021.130470","text":"Publisher Index Page"},{"id":436411,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92PFAXS","text":"USGS data release","linkHelpText":"Mercury Concentrations in Western Gulls along the West Coast, USA, 2015-2017"},{"id":385751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Cleft-in-Rock, Hunters Island, Farallon Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.06835937499997,\n 36.70365959719453\n ],\n [\n -122.69531249999997,\n 36.70365959719453\n ],\n [\n -122.69531249999997,\n 44.465151013519616\n ],\n [\n -125.06835937499997,\n 44.465151013519616\n ],\n [\n -125.06835937499997,\n 36.70365959719453\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clatterbuck, Corey A. 0000-0003-1351-8565","orcid":"https://orcid.org/0000-0003-1351-8565","contributorId":202763,"corporation":false,"usgs":false,"family":"Clatterbuck","given":"Corey","email":"","middleInitial":"A.","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":816048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewison, Rebecca L.","contributorId":194537,"corporation":false,"usgs":false,"family":"Lewison","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":816049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orben, Rachael A 0000-0002-0802-407X","orcid":"https://orcid.org/0000-0002-0802-407X","contributorId":221851,"corporation":false,"usgs":false,"family":"Orben","given":"Rachael","email":"","middleInitial":"A","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":816050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torres, Leigh G 0000-0002-2643-3950","orcid":"https://orcid.org/0000-0002-2643-3950","contributorId":258229,"corporation":false,"usgs":false,"family":"Torres","given":"Leigh G","affiliations":[{"id":52257,"text":"Marine Mammal Institute, Department of Fisheries and Wildlife, Oregon State University, Newport, OR, USA","active":true,"usgs":false}],"preferred":false,"id":816052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suryan, Robert M. 0000-0003-0755-8317","orcid":"https://orcid.org/0000-0003-0755-8317","contributorId":221852,"corporation":false,"usgs":false,"family":"Suryan","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40443,"text":"Oregon State University, NOAA","active":true,"usgs":false}],"preferred":false,"id":816053,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Warzybok, Peter","contributorId":198612,"corporation":false,"usgs":false,"family":"Warzybok","given":"Peter","email":"","affiliations":[],"preferred":false,"id":816054,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jahncke, Jaime","contributorId":152294,"corporation":false,"usgs":false,"family":"Jahncke","given":"Jaime","email":"","affiliations":[{"id":18899,"text":"Point Blue Conservation Science; 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The North Fork Ninnescah River is the largest tributary to Cheney Reservoir and contributes about 70 percent of the inflow. The U.S. Geological Survey, in cooperation with the City of Wichita, has been continuously monitoring water quality (including water temperature, specific conductance, pH, dissolved oxygen, and turbidity) on the North Fork Ninnescah River upstream from Cheney Reservoir (U.S. Geological Survey site 07144780) since November 1998. Continued data collection would be beneficial to update and describe changing water-quality conditions in the drainage basin and in the reservoir over time.
Regression models were developed to describe relations between discretely measured constituent concentrations and continuously measured physical properties. The models updated in this report include total suspended solids (TSS), suspended-sediment concentration (SSC), nitrate plus nitrite, nitrate, orthophosphate (OP), total phosphorus (TP), and total organic carbon (TOC).
Daily computed concentrations for TSS, TP, and nitrate plus nitrite during 1999–2019 were compared with Cheney Reservoir Task Force (CRTF) goals for base-flow and runoff conditions. CRTF goals for base-flow concentrations were exceeded more frequently (70 to 99.9 percent of the time) than runoff goals (0 to 11 percent of the time). Except for 2012, annual mean TSS concentrations exceeded the base-flow goal every year. Nitrate plus nitrite and TP annual mean concentrations exceeded the base-flow goals every year. TSS and nitrate plus nitrite annual mean concentrations during runoff conditions never exceeded the CRTF runoff goal. TP annual mean concentrations during runoff conditions only exceeded the CRTF runoff goal during 2002.
Sedimentation is progressively reducing the storage capacity of Cheney Reservoir. During 1999–2019, 55 percent of the computed suspended-sediment load was transported during the top 1 percent of loading days (76 days); 22 percent of the total load was transported in the top 10 loading days, indicating that substantial parts of suspended-sediment loads continue to be delivered during disproportionately small periods in Cheney Reservoir. Successful sediment management efforts necessitate reduction techniques that account for these large load events.
Flow-normalized concentrations and fluxes were computed during 1999 through 2019 using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and WRTDS bootstrap tests. Flow-normalized concentrations of TSS, SSC, OP, TP, and TOC had upward trend probabilities; conversely, nitrate plus nitrite had a downward trend. Flow-normalized fluxes for OP, TP, and TOC had an upward trend. No discernible patterns were identified for flow-normalized flux of TSS or suspended sediment. Nitrate plus nitrite flow-normalized flux indicated a downward trend.
Flow-normalized concentrations for TSS were less than the CRTF long-term goal of 100 milligrams per liter (mg/L), but the upward trend indicated the long-term goal may be exceeded if no changes are made. Flow-normalized TP concentrations exceeded the CRTF long-term goal (0.1 mg/L) and were assigned a very likely upward trend. Flow-normalized nitrate plus nitrite concentrations exceeded the CRTF long-term goal of 1.2 mg/L during the beginning of the study period, then were less than the CRTF goal for the remainder of the study; however, during 2010–19 flow-normalized concentrations increased by 6 percent.
Linking water-quality changes to causal factors requires consistent monitoring before, during, and after changes; this presents challenges related to length and frequency of data collection and available concomitant land-use and conservation practice data. As such, attribution of water-quality trends to land-use changes or conservation practices was not possible for this study because of a lack of land-use and conservation practice data. Additionally, because precipitation frequency and intensity are projected to continue to increase in the Great Plains region, accounting for extreme episodic events may be an important consideration in future sediment and nutrient load reduction plans.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215006","collaboration":"Prepared in cooperation with the City of Wichita","usgsCitation":"Kramer, A.R., Klager, B.J., Stone, M.L., and Eslick-Huff, P.J., 2021, Regression relations and long-term water-quality constituent concentrations, loads, yields, and trends in the North Fork Ninnescah River, south-central Kansas, 1999–2019: U.S. Geological Survey Scientific Investigations Report 2021–5006, 51 p., https://doi.org/10.3133/sir20215006.","productDescription":"Report: ix, 51 p.; Appendixes: 24; Dataset","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118868","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":384937,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":384935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5006/coverthb.jpg"},{"id":384936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5006/sir20215006.pdf","text":"Report","size":"3.80 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5006"},{"id":384938,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5006/downloads/","text":"Appendixes 1–24","description":"SIR 2021–5006 Appendixes 1–24"}],"country":"United States","state":"Kansas","otherGeospatial":"North Fork Ninnescah River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.7176513671875,\n 37.60987994374712\n ],\n [\n -97.3663330078125,\n 37.60987994374712\n ],\n [\n -97.3663330078125,\n 38.238180119798635\n ],\n [\n -98.7176513671875,\n 38.238180119798635\n ],\n [\n -98.7176513671875,\n 37.60987994374712\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
1217 Biltmore Drive
Lawrence, KS 66049
Climate warming in high-latitude regions is thawing carbon-rich permafrost soils, which can release carbon to the atmosphere and enhance climate warming. Using a coupled model of long-term peatland dynamics (Holocene Peat Model, HPM-Arctic), we quantify the potential loss of carbon with future climate warming for six sites with differing climates and permafrost histories in Northwestern Canada. We compared the net carbon balance at 2100 CE resulting from new productivity and the decomposition of active layer and newly thawed permafrost peats under RCP8.5 as a high-end constraint. Modeled net carbon losses ranged from −3.0 kg C m−2 (net loss) to +0.1 kg C m−2 (net gain) between 2015 and 2100. Losses of newly thawed permafrost peat comprised 0.2%–25% (median: 1.6%) of “old” C loss, which were related to the residence time of peat in the active layer before being incorporated into the permafrost, peat temperature, and presence of permafrost. The largest C loss was from the permafrost-free site, not from permafrost sites. C losses were greatest from depths of 0.2–1.0 m. New C added to the profile through net primary productivity between 2015 and 2100 offset ∼40% to >100% of old C losses across the sites. Differences between modeled active layer deepening and flooding following permafrost thaw resulted in very small differences in net C loss by 2100, illustrating the important role of present-day conditions and permafrost aggradation history in controlling net C loss.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005872","usgsCitation":"Treat, C.C., Jones, M.C., Alder, J.R., Sannel, A.B., Camill, P., and Frolking, S., 2021, Predicted vulnerability of carbon in permafrost peatlands With future climate change and permafrost thaw in western Canada: JGR Biogeosciences, v. 126, e2020JG005872, 17 p., https://doi.org/10.1029/2020JG005872.","productDescription":"e2020JG005872, 17 p.","ipdsId":"IP-119562","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":452714,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://orcid.org/0000-0002-1225-8178","text":"Publisher Index Page"},{"id":396431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"western Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -133.06640625,\n 51.67255514839674\n ],\n [\n -86.66015624999999,\n 51.67255514839674\n ],\n [\n -86.66015624999999,\n 70.19999407534661\n ],\n [\n -133.06640625,\n 70.19999407534661\n ],\n [\n -133.06640625,\n 51.67255514839674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Treat, Claire C.","contributorId":150798,"corporation":false,"usgs":false,"family":"Treat","given":"Claire","email":"","middleInitial":"C.","affiliations":[{"id":18105,"text":"University of New Hampshire, Durham","active":true,"usgs":false}],"preferred":false,"id":835955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Miriam C. 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":257239,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"","middleInitial":"C.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":835956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":835957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sannel, A. Britta K. 0000-0002-1350-6516","orcid":"https://orcid.org/0000-0002-1350-6516","contributorId":223672,"corporation":false,"usgs":false,"family":"Sannel","given":"A.","email":"","middleInitial":"Britta K.","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":835990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camill, Philip","contributorId":176994,"corporation":false,"usgs":false,"family":"Camill","given":"Philip","email":"","affiliations":[],"preferred":false,"id":835991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frolking, Steve","contributorId":7638,"corporation":false,"usgs":true,"family":"Frolking","given":"Steve","email":"","affiliations":[],"preferred":false,"id":835992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219588,"text":"70219588 - 2021 - Regional target loads of atmospheric nitrogen and sulfur deposition for the protection of stream and watershed soil resources of the Adirondack Mountains, USA","interactions":[],"lastModifiedDate":"2021-04-22T18:02:33.699935","indexId":"70219588","displayToPublicDate":"2021-04-10T07:42:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Regional target loads of atmospheric nitrogen and sulfur deposition for the protection of stream and watershed soil resources of the Adirondack Mountains, USA","docAbstract":"Acidic deposition contributes to a range of environmental impacts across forested landscapes, including acidification of soil and drainage water, toxic aluminum mobilization, depletion of available soil nutrient cations, and impacts to forest and aquatic species health and biodiversity. In response to decreasing levels of acidic deposition, soils and drainage waters in some regions of North America have become gradually less acidic. Thresholds of atmospheric deposition at which adverse ecological effects are manifested are called critical loads (CLs) and/or target loads (TLs). Target loads are developed based on approaches that account for spatial and temporal aspects of acidification and recovery. Exceedance represents the extent to which current or projected future levels of acidic deposition exceed the level expected to cause ecological harm. We report TLs of sulfur (S) and nitrogen (N) deposition and the potential for ecosystem recovery of watershed soils and streams in the Adirondack region of New York State, resources that have been less thoroughly investigated than lakes. Regional TLs were calculated by statistical extrapolation of hindcast and forecast simulations of 25 watersheds using the process-based model PnET-BGC coupled with empirical observations of stream hydrology and established sensitivity of sugar maple (Acer saccharum) to soil base saturation and brook trout (Salvelinus fontinalis) to stream acid neutralizing capacity (ANC). Historical impacts and the expected recovery timeline of regional soil and stream chemistry and fish community condition within the Adirondack Park were evaluated. Analysis suggests that many low-order Adirondack streams and associated watershed soils have low TLs (<40 meq/m2/yr of N+S deposition) to achieve specified benchmarks for recovery of soil base saturation or stream ANC. Acid-sensitive headwater and low-order streams and watershed soils in the region are expected to experience continued adverse effects from N and S deposition well into the future even under aggressive emissions reductions. Watershed soils and streams in the western Adirondack Park are particularly vulnerable to acidic deposition and currently in exceedance of TLs. The methods used for linking statistical and process-based models to consider chemical and biological response under varying flow conditions at the regional scale in this study can be applied to other areas of concern.
The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.
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0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":813937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":813939,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219510,"text":"70219510 - 2021 - Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars","interactions":[],"lastModifiedDate":"2021-06-30T18:21:45.95743","indexId":"70219510","displayToPublicDate":"2021-04-08T10:24:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars","docAbstract":"The Curiosity rover is exploring Hesperian-aged stratigraphy in Gale crater, Mars, where a transition from clay-bearing units to a layered sulfate-bearing unit has been interpreted to represent a major environmental transition of unknown character. We present the first description of key facies in the sulfate-bearing unit, recently observed in the distance by the rover, and propose a model for changes in depositional environments. Our results indicate a transition from lacustrine mudstones into thick aeolian deposits, topped by a major deflation surface, above which strata show architectures likely diagnostic of a subaqueous environment. This model offers a reference example of a depositional sequence for layered sulfate-bearing strata, which have been identified from orbit in other locations globally. It differs from the idea of a monotonic Hesperian climate change into long-term aridity on Mars and instead implies a period characterized by multiple transitions between sustained drier and wetter climates.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G48519.1","usgsCitation":"Rapin, W., Dromart, G., Rubin, D., Le Deit, L., Mangold, N., Edgar, L.A., Gasnault, O., Herkenhoff, K., Lemouelic, S., Anderson, R.B., Maurice, S., Fox, V., Ehlmann, B.L., Dickson, J.L., and Wiens, R.C., 2021, Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars: Geology, v. 49, no. 7, p. 842-846, https://doi.org/10.1130/G48519.1.","productDescription":"5 p.","startPage":"842","endPage":"846","ipdsId":"IP-118537","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":452736,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g48519.1","text":"Publisher Index Page"},{"id":385019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale Crater, Mars, Mount Sharp","volume":"49","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Rapin, William","contributorId":172305,"corporation":false,"usgs":false,"family":"Rapin","given":"William","email":"","affiliations":[{"id":27023,"text":"Institut de Recherche en Astrophysique et Planétologie","active":true,"usgs":false}],"preferred":false,"id":813845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dromart, Gilles","contributorId":172300,"corporation":false,"usgs":false,"family":"Dromart","given":"Gilles","email":"","affiliations":[{"id":25661,"text":"Laboratoire de Géologie de Lyon, Ecole Normale Supérieure de Lyon and Université Claude Bernard Lyon","active":true,"usgs":false}],"preferred":false,"id":813846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubin, Dave","contributorId":189222,"corporation":false,"usgs":false,"family":"Rubin","given":"Dave","email":"","affiliations":[],"preferred":false,"id":813847,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Le Deit, Laticia","contributorId":257240,"corporation":false,"usgs":false,"family":"Le Deit","given":"Laticia","email":"","affiliations":[{"id":27021,"text":"Universite de Nantes","active":true,"usgs":false}],"preferred":false,"id":813848,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mangold, Nicolas","contributorId":52903,"corporation":false,"usgs":false,"family":"Mangold","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":813849,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":813850,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gasnault, Olivier","contributorId":181501,"corporation":false,"usgs":false,"family":"Gasnault","given":"Olivier","email":"","affiliations":[],"preferred":false,"id":813851,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herkenhoff, Kenneth E. 0000-0002-3153-6663","orcid":"https://orcid.org/0000-0002-3153-6663","contributorId":206170,"corporation":false,"usgs":true,"family":"Herkenhoff","given":"Kenneth E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":813852,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lemouelic, S.","contributorId":71765,"corporation":false,"usgs":true,"family":"Lemouelic","given":"S.","email":"","affiliations":[],"preferred":false,"id":813951,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":813952,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Maurice, S.","contributorId":18144,"corporation":false,"usgs":true,"family":"Maurice","given":"S.","email":"","affiliations":[],"preferred":false,"id":813953,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Fox, V.","contributorId":257270,"corporation":false,"usgs":false,"family":"Fox","given":"V.","affiliations":[],"preferred":false,"id":813954,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ehlmann, B. L.","contributorId":252876,"corporation":false,"usgs":false,"family":"Ehlmann","given":"B.","email":"","middleInitial":"L.","affiliations":[{"id":50450,"text":"JPL/Caltech, Pasadena, CA","active":true,"usgs":false}],"preferred":false,"id":813955,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dickson, J. L.","contributorId":257271,"corporation":false,"usgs":false,"family":"Dickson","given":"J.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":813956,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wiens, R. 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When used in conjunction with historic species’ distribution records and predictive habitat suitability modeling, genetic information adds a key piece to the puzzle that will increase the probability of successful ecosystem restoration.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rewilding agricultural landscapes: a California study in rebalancing the needs of people and nature","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Island Press","usgsCitation":"Richmond, J.Q., Wood, D.A., and Matocq, M.D., 2021, Genetic considerations for rewilding the San Joaquin Desert, chap. 8 of Rewilding agricultural landscapes: a California study in rebalancing the needs of people and nature, p. 109-128.","productDescription":"20 p.","startPage":"109","endPage":"128","ipdsId":"IP-125484","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.17782627056152,\n 35.01010185782188\n ],\n [\n -118.30695417496116,\n 35.621582059868544\n ],\n [\n -119.44808176516588,\n 37.07443079472277\n ],\n [\n -120.97833657128518,\n 36.86695517685743\n ],\n [\n -119.17782627056152,\n 35.01010185782188\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863290,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matocq, Marjorie D","contributorId":222917,"corporation":false,"usgs":false,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":863292,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219526,"text":"70219526 - 2021 - Reconstructing the dynamics of the highly similar May 2016 and June 2019 Iliamna Volcano, Alaska ice–rock avalanches from seismoacoustic data","interactions":[],"lastModifiedDate":"2021-04-12T13:21:07.805475","indexId":"70219526","displayToPublicDate":"2021-04-08T08:08:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7942,"text":"Earth Surface Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the dynamics of the highly similar May 2016 and June 2019 Iliamna Volcano, Alaska ice–rock avalanches from seismoacoustic data","docAbstract":"Surficial mass wasting events are a hazard worldwide. Seismic and acoustic signals from these often remote processes, combined with other geophysical observations, can provide key information for monitoring and rapid response efforts and enhance our understanding of event dynamics. Here, we present seismoacoustic data and analyses for two very large ice–rock avalanches occurring on Iliamna Volcano, Alaska (USA), on 22 May 2016 and 21 June 2019. Iliamna is a glacier-mantled stratovolcano located in the Cook Inlet, ∼200 km from Anchorage, Alaska. The volcano experiences massive, quasi-annual slope failures due to glacial instabilities and hydrothermal alteration of volcanic rocks near its summit. The May 2016 and June 2019 avalanches were particularly large and generated energetic seismic and infrasound signals which were recorded at numerous stations at ranges from ∼9 to over 600 km. Both avalanches initiated in the same location near the head of Iliamna's east-facing Red Glacier, and their ∼8 km long runout shapes are nearly identical. This repeatability – which is rare for large and rapid mass movements – provides an excellent opportunity for comparison and validation of seismoacoustic source characteristics. For both events, we invert long-period (15–80 s) seismic signals to obtain a force-time representation of the source. We model the avalanche as a sliding block which exerts a spatially static point force on the Earth. We use this force-time function to derive constraints on avalanche acceleration, velocity, and directionality, which are compatible with satellite imagery and observed terrain features. Our inversion results suggest that the avalanches reached speeds exceeding 70 m s−1, consistent with numerical modeling from previous Iliamna studies. We lack sufficient local infrasound data to test an acoustic source model for these processes. However, the acoustic data suggest that infrasound from these avalanches is produced after the mass movement regime transitions from cohesive block-type failure to granular and turbulent flow – little to no infrasound is generated by the initial failure. At Iliamna, synthesis of advanced numerical flow models and more detailed ground observations combined with increased geophysical station coverage could yield significant gains in our understanding of these events.
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