The Salton Trough (southeastern California, USA) is the northernmost transtensional stepover of the Gulf of California oblique-divergent plate boundary and is also where the southern terminus of the San Andreas fault occurs. Until recently, the distribution of active faults in and around the Salton Sea and their displacement histories were largely unknown. Subbottom CHIRP (compressed high-intensity radar pulse) surveys in the Salton Sea are used to develop a seismic facies model for ancient Lake Cahuilla deposits, a detailed map of submerged active faults, and reconstructed fault displacement histories during the late Holocene. We observe as many as fourteen Lake Cahuilla sequences in the Salton Sea (last ~3 k.y.) and develop a chronostratigraphic framework for the last six sequences (last ~1200 yr) by integrating CHIRP data and cone penetrometer logs with radiocarbon-dated stratigraphy at an onshore paleoseismic site. The Salton Sea contains northern and southern subbasins that appear to be separated by a tectonic hinge zone, and a subsidence signal across hinge-zone faults of 6–9 mm/yr (since ca. A.D. 940) increases toward the south to >15 mm/yr. The faults mapped to the south of the hinge zone appear to accommodate transtension within the San Andreas–Imperial fault stepover. We identify 8–15 distinct growth events across hinge-zone faults, meaning growth occurred at least once every 100 yr since Lake Cahuilla sedimentation began. Several faults offset the top of the most recent Lake Cahuilla highstand deposits, and at least two faults have offset the Salton Sea flood deposits. Active faults and folds were also mapped to a limited extent within the northern subbasin and display growth, but their kinematics and rupture histories require further study. The broad distribution of active faulting suggests that strain between the San Andreas, San Jacinto, and Imperial faults is highly distributed, thus discrepancies between geologic and geodetic slip-rate estimates from these major fault systems are to be expected.
Satellite and GPS tracking technology continues to reveal new migration patterns of birds which enables comparative studies of migration strategies and distributional information useful in conservation. Bar-tailed godwits in the East Asian–Australasian Flyway Limosa lapponica baueri and L. l. menzbieri are known for their long non-stop flights, however these populations are in steep decline. A third subspecies in this flyway, L. l. anadyrensis, breeds in the Anadyr River basin, Chukotka, Russia, and is morphologically distinct from menzbieri and baueri based on comparison of museum specimens collected from breeding areas. However, the non-breeding distribution, migration route and population size of anadyrensis are entirely unknown. Among 24 female bar-tailed godwits tracked in 2015–2018 from northwest Australia, the main non-breeding area for menzbieri, two birds migrated further east than the rest to breed in the Anadyr River basin, i.e. they belonged to the anadyrensis subspecies. During pre-breeding migration, all birds staged in the Yellow Sea and then flew to the breeding grounds in the eastern Russian Arctic. After breeding, these two birds migrated southwestward to stage in Russia on the Kamchatka Peninsula and on Sakhalin Island en route to the Yellow Sea. This contrasts with the other 22 tracked godwits that followed the previously described route of menzbieri, i.e. they all migrated northwards to stage in the New Siberian Islands before turning south towards the Yellow Sea, and onwards to northwest Australia. Since the Kamchatka Peninsula was not used by any of the tracked menzbieri birds, the 4500 godwits counted in the Khairusova–Belogolovaya estuary in western Kamchatka may well be anadyrensis. Comparing migration patterns across the three bar-tailed godwits subspecies, the migration strategy of anadyrensis lies between that of menzbieri and baueri. Future investigations combining migration tracks with genomic data could reveal how differences in migration routines are evolved and maintained.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.02988","usgsCitation":"Chan, Y., Tibbitts, T.L., Dorofeev, D., Hassell, C.J., and Piersma, T., 2022, Hidden in plain sight: Migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking: Journal of Avian Biology, v. 2022, no. 8, e02988, 11 p., https://doi.org/10.1111/jav.02988.","productDescription":"e02988, 11 p.","ipdsId":"IP-134993","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":447405,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jav.02988","text":"External Repository"},{"id":402766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, China, Russia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 179.6484375,\n 64.32087157990324\n ],\n [\n 177.18749999999997,\n 68.9110048456202\n ],\n [\n 152.9296875,\n 71.52490903732816\n ],\n [\n 103.0078125,\n 5.965753671065536\n ],\n [\n 109.6875,\n -22.593726063929296\n ],\n [\n 117.7734375,\n -21.943045533438166\n ],\n [\n 130.4296875,\n -12.897489183755892\n ],\n [\n 139.5703125,\n 37.16031654673677\n ],\n [\n 179.6484375,\n 64.32087157990324\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2022","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Chan, Ying-Chi","contributorId":167762,"corporation":false,"usgs":false,"family":"Chan","given":"Ying-Chi","email":"","affiliations":[{"id":24822,"text":"Department of Marine Ecology, NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":845416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":102185,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":845417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dorofeev, Dmitry","contributorId":292661,"corporation":false,"usgs":false,"family":"Dorofeev","given":"Dmitry","email":"","affiliations":[{"id":62965,"text":"All Russian Research Institute for Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":845418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hassell, Chris J.","contributorId":127818,"corporation":false,"usgs":false,"family":"Hassell","given":"Chris","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":845419,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Piersma, Theunis 0000-0001-9668-466X","orcid":"https://orcid.org/0000-0001-9668-466X","contributorId":203123,"corporation":false,"usgs":false,"family":"Piersma","given":"Theunis","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":845420,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232236,"text":"70232236 - 2022 - Enumerating plausible multifault ruptures in complex fault systems with physical constraints","interactions":[],"lastModifiedDate":"2022-08-02T14:30:33.803665","indexId":"70232236","displayToPublicDate":"2022-06-16T09:04:07","publicationYear":"2022","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":"Enumerating plausible multifault ruptures in complex fault systems with physical constraints","docAbstract":"We propose a new model for determining the set of plausible multifault ruptures in an interconnected fault system. We improve upon the rules used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to increase connectivity and the physical consistency of ruptures. We replace UCERF3’s simple azimuth change rules with new Coulomb favorability metrics and increase the maximum jump distance to 15 km. Although the UCERF3 rules were appropriate for faults with similar rakes, the Coulomb calculations used here inherently encode preferred orientations between faults with different rakes. Our new rules are designed to be insensitive to discretization details and are generally more permissive than their UCERF3 counterparts; they allow more than twice the connectivity compared to UCERF3, yet heavily penalize long ruptures that take multiple improbable jumps. The set of all possible multifault ruptures in the California fault system is near-infinite, but our model produces a tractable set of 326,707 ruptures (a modest 29% increase over UCERF3, despite the greatly increased connectivity). Inclusion in the rupture set does not dictate that a rupture receives a significant rate in the final model; rupture rates are subsequently determined by data constraints used in an inversion.\n\nWe describe the rupture building algorithm and its components in detail and provide comparisons with ruptures generated by a physics-based multicycle earthquake simulator. We find that greater than twice as many ruptures generated by the simulator violate the UCERF3 rules than violate our proposed model.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210322","usgsCitation":"Milner, K.R., Shaw, B.E., and Field, E.H., 2022, Enumerating plausible multifault ruptures in complex fault systems with physical constraints: Bulletin of the Seismological Society of America, v. 112, no. 4, p. 1806-1824, https://doi.org/10.1785/0120210322.","productDescription":"19 p.","startPage":"1806","endPage":"1824","ipdsId":"IP-139511","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"112","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaw, Bruce E.","contributorId":194146,"corporation":false,"usgs":false,"family":"Shaw","given":"Bruce","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":844760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232235,"text":"70232235 - 2022 - Mapping a magnetic superstorm: March 1989 geoelectric hazards and impacts on United States power systems","interactions":[],"lastModifiedDate":"2022-06-16T14:00:42.556778","indexId":"70232235","displayToPublicDate":"2022-06-16T08:55:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"Mapping a magnetic superstorm: March 1989 geoelectric hazards and impacts on United States power systems","docAbstract":"A study is made of the relationships between geomagnetic and geoelectric field variation, Earth-surface impedance, and operational interference (anomalies) experienced on electric-power systems across the contiguous United States during the March 13-14, 1989 magnetic storm. For this, a 1-minute-resolution sequence of geomagnetic field maps is constructed from magnetometer time series acquired at ground-based observatories. Induced geoelectric field maps are calculated by convolving the geomagnetic maps with magnetotelluric impedance tensors. During the storm, anomalies were concentrated where the lithosphere is electrically resistive, and when and where geoelectric field amplitudes were high. This was particularly true in the Mid-Atlantic, Northeast, and the upper Midwest. Few anomalies were experienced in other parts of the Midwest and across\nmuch of the West, where the lithosphere is more conductive, and when and where geoelectric field amplitudes were low. Peak 1-minute-resolution geoelectric field amplitude ranged from 21.66 V/km in Maine and 19.02 V/km in Virginia to < 0.02 V/km in Idaho. Latitude-dependent organization of geoelectric hazards by auroral-zone electrojet currents is detectable, but it is much weaker than geographic organization due to surface impedance. Hazardous geoelectric fields were induced during different storm phases, at different local times, and, by inference, by a variety of ionospheric currents. Compared to geoelectric field amplitudes realized across the United States during March 1989, hazard maps used by utility companies to estimate systems exposure have much less geographic detail and a much smaller maximum-to-minimum range in geoelectric field amplitude. Future research will benet from denser geomagnetic monitoring, additional magnetotelluric surveying, and access to power-system impact data.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021SW003030","usgsCitation":"Love, J.J., Lucas, G., Rigler, E.J., Murphy, B.S., Kelbert, A., and Bedrosian, P.A., 2022, Mapping a magnetic superstorm: March 1989 geoelectric hazards and impacts on United States power systems: Space Weather, v. 20, no. 5, e2021SW003030, 27 p., https://doi.org/10.1029/2021SW003030.","productDescription":"e2021SW003030, 27 p.","ipdsId":"IP-140408","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":447410,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021sw003030","text":"Publisher Index 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]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":223556,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg M.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":844754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844758,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237243,"text":"70237243 - 2022 - A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.","interactions":[],"lastModifiedDate":"2022-10-05T13:32:04.880727","indexId":"70237243","displayToPublicDate":"2022-06-16T08:19:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"A framework for ensemble modelling of climate change impacts on lakes worldwide: The ISIMIP lake sector.","docAbstract":"Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.
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recording units, although cost-effective, provides imperfect knowledge about species presence. Summertime bat acoustic surveys in North America exemplify the challenges with characterizing sources of uncertainty: observation error, inability to census populations, and natural stochastic variation. Statistical uncertainty, if not considered thoroughly, hampers determining rare species presence accurately and/or estimating rangewide status and trends with suitable precision. Bat acoustic data are processed using an automated workflow in which proprietary or open-source algorithms assign a species label to each recorded high-frequency echolocation sequence. A false-negative occurs, if a species is actually present but not recorded and/or all recordings from the species are of such poor quality that a correct species identity cannot be assigned to any observation. False positives for a focal species are a direct result of the presence and incorrect identification of a recording from another species. We compare four analytical approaches in terms of parameter estimation and their resulting (in)correct decisions regarding species presence or absence using realistic data-generating scenarios for bat acoustic data within a simulation study. The current standard for deciding species presence or absence uses a multinomial likelihood-ratio test p value (maximum likelihood estimate [MLE]-metric) that accounts for known species misidentifications, but not imperfect detection and only returns a binary outcome (evidence of presence or not). We found that the MLE-metric had estimated median correct decisions less than 60% for presence and greater than 85% for absence. Alternatively, a multispecies count detection model was equivalent to or better than the MLE-metric for correct claims of rare species presence or absence using the posterior probability a species was present at a site and, importantly, provided unbiased estimates of relative activity and probability of occurrence, creating opportunities for reducing posterior uncertainty through the inclusion of meaningful covariates. Single-species occupancy models with and without false-positive detections removed were insufficient for determining local presence because of substantially biased occurrence and detection probabilities. We propose solutions to potential barriers for integrating local, short-term and rangewide, long-term acoustic surveys within a cohesive statistical framework that facilitates determining local species presence with uncertainty concurrent with estimating species–environment relationships.
For agricultural areas facing water scarcity, sustainable water use policy relies on irrigation information that is timely and at a high resolution, but existing publicly available water use data are often insufficient for monitoring compliance or understanding the influence of policy on individual farmer decisions. This study attempts to fill this data gap by using remote sensing to map annual irrigation quantity at the field-scale within the central Ogallala aquifer region of the United States. We compiled in situ annual irrigation volume data at the field scale in the Republican River Basin of Colorado for 2015–2018 and at the Public Land Survey System (PLSS) section scale in western Kansas for 2000–2016, which served as reference data in random forest models that relied on Landsat-based actual evapotranspiration from the Operational Simplified Surface Energy Balance model (SSEBop) along with maps of irrigated area, Landsat spectral indices, climate, soils, and derived hydrologic variables. The models explained 87% of the variability in irrigation volume in Colorado and 75% in Kansas, but accuracy declined when transferring the models in spatial cross-validation (Colorado R2 =0.81; Kansas R2 =0.51) and temporal cross-validation (Colorado R2 =0.82; Kansas R2 =0.68). Predicted annual totals of irrigation volume in western Kansas had a mean absolute error of 11.9%, which was slightly higher than the average annual change of 11%. Use of predicted irrigation maps also lead to an underestimated effect size for a water use restriction policy in Kansas. These results indicate that field- and section-scale irrigation can be mapped with reasonable accuracy within a region and time period that has adequate sample data, but that methods may need to be improved for applying the models more broadly in areas that lack extensive in situ irrigation data to support further research on water use and aid in structuring policy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2022.107764","usgsCitation":"Filippelli, S.S., Sloggy, M.R., Vogeler, J.C., Manning, D.T., Goemans, C., and Senay, G.B., 2022, Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region: Agricultural Water Management, v. 271, 107764, 15 p., https://doi.org/10.1016/j.agwat.2022.107764.","productDescription":"107764, 15 p.","ipdsId":"IP-137832","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488115,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2022.107764","text":"Publisher Index Page"},{"id":428798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado Kansas","otherGeospatial":"Ogallala aquifer, Republican River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.00825131829713,\n 36.98969791337646\n ],\n [\n -97.51352205922755,\n 36.9837502608661\n ],\n [\n -97.3406085961385,\n 38.44391430535899\n ],\n [\n -98.0932074623356,\n 38.87773029390084\n ],\n [\n -99.65122819650178,\n 39.098871590431884\n ],\n [\n -99.26469253047271,\n 40.0142028593921\n ],\n [\n -102.06717715371995,\n 40.032435593038315\n ],\n [\n -102.11261804171953,\n 40.992555768008174\n ],\n [\n -104.06597899342937,\n 40.93187765306061\n ],\n [\n -104.48051561524214,\n 39.43819239944247\n ],\n [\n -103.69918549158801,\n 38.5715258326463\n ],\n [\n -102.64452864991789,\n 38.27521479596666\n ],\n [\n -102.4653590264147,\n 36.967066532161525\n ],\n [\n -102.00825131829713,\n 36.98969791337646\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"271","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Filippelli, Steven S 0000-0001-7291-0888","orcid":"https://orcid.org/0000-0001-7291-0888","contributorId":336733,"corporation":false,"usgs":false,"family":"Filippelli","given":"Steven","email":"","middleInitial":"S","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":900929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sloggy, Matthew R 0000-0001-5129-0755","orcid":"https://orcid.org/0000-0001-5129-0755","contributorId":336734,"corporation":false,"usgs":false,"family":"Sloggy","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":900930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogeler, Jody C.","contributorId":264796,"corporation":false,"usgs":false,"family":"Vogeler","given":"Jody","email":"","middleInitial":"C.","affiliations":[{"id":54555,"text":"umn","active":true,"usgs":false}],"preferred":false,"id":900931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manning, Dale T 0000-0001-6465-5530","orcid":"https://orcid.org/0000-0001-6465-5530","contributorId":336735,"corporation":false,"usgs":false,"family":"Manning","given":"Dale","email":"","middleInitial":"T","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":900932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goemans, Christopher 0000-0003-4930-4278","orcid":"https://orcid.org/0000-0003-4930-4278","contributorId":336736,"corporation":false,"usgs":false,"family":"Goemans","given":"Christopher","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":900933,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":900934,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262405,"text":"70262405 - 2022 - On the multiple identities of stakeholders in wolf management in Minnesota, United States","interactions":[],"lastModifiedDate":"2025-01-17T17:31:28.733664","indexId":"70262405","displayToPublicDate":"2022-06-15T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"On the multiple identities of stakeholders in wolf management in Minnesota, United States","docAbstract":"Social identity theory offers a means to understand attitudes about wolves, with consequences for management support. Using data from a mail survey about wolves, we explored relationships among seven identities (i.e., wolf advocate, hunter, environmentalist, nature enthusiast, farmer, trapper, conservationist) using multidimensional scaling (MDS) and principal components analysis (PCA). We examined how identities correlated with political ideology, trust in a wildlife management agency, wildlife value orientations (WVOs) and attitudes about wolves, and we evaluated whether WVOs mediated the relationship between identities and attitudes. PCA suggested two factors in identifying relationships among stakeholders, while MDS and correlations found diversity among stakeholders beyond these factors. Hunter identity was most strongly associated with a domination WVO and conservative political ideology. Farmer identity was most strongly associated with agency distrust and negative wolf attitudes. Wolf advocate was most strongly associated with a mutualism WVO (i.e., beliefs that humans are meant to coexist in harmonious relationships with wildlife), agency trust, and positive wolf attitudes. Conservationist identity was positively correlated with all other identities. WVOs partially mediated the relationship between identities and attitudes.
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Cooperative Unit","active":true,"usgs":false}],"preferred":true,"id":924130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McInenly, Leslie","contributorId":349185,"corporation":false,"usgs":false,"family":"McInenly","given":"Leslie","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924131,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232208,"text":"ofr20221043 - 2022 - Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data","interactions":[],"lastModifiedDate":"2022-06-15T14:11:27.09211","indexId":"ofr20221043","displayToPublicDate":"2022-06-14T14:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1043","displayTitle":"Opportunities To Improve Alignment With the FAIR Principles for U.S. Geological Survey Data","title":"Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data","docAbstract":"In 2016, an interdisciplinary, international group of 53 scientists introduced a framework named “the FAIR Principles” for addressing 21st century scientific data challenges. The FAIR Principles are increasingly used as a guide for producing digital scientific products that are findable, accessible, interoperable, and reusable (FAIR), especially to enable use of such products in automated systems. Data aligned with the FAIR Principles can increase the efficiency of science integration capabilities such as those envisioned for the U.S. Geological Survey (USGS) Earth Monitoring, Analyses, and Projections (EarthMAP) initiative.
The FAIR Principles clearly define the characteristics of reusable scientific products, but it is less clear how to facilitate consistency in achieving these characteristics across the Bureau. USGS data are produced by local research projects distributed over more than 100 centers in 7 regions. After data are approved for release, they could be managed in numerous repositories and online data systems. The diversity of USGS data is illustrated by the topical range of the USGS mission areas: Core Science Systems, Ecosystems, Energy and Minerals, Natural Hazards, and Water Resources. In the USGS context, realizing the EarthMAP vision for automated, predictive, integrated science that provides timely and actionable results involves providing knowledge and support services and developing the skills, infrastructure, and culture to enable Bureau-wide implementation of the FAIR Principles.
In 2019, the USGS Community for Data Integration funded a project to convene a broadly representative workshop and produce recommendations to enable consistency with the FAIR Principles across the USGS. The workshop, held in Fort Collins, Colorado, in September 2019, brought together 28 participants for 3 days to engage with the FAIR Principles, analyze USGS use cases, and discuss the roles of data producers and managers, data storage and catalogs, value-added services, and policy makers in implementing the FAIR Principles. Workshop participants agreed that scientific reproducibility requires the extension of the FAIR Principles beyond measured data to include physical samples, research methods, software, and tools at the USGS. Workshop discussions focused on how the USGS can implement the FAIR Principles by supporting research teams in creating data, metadata, and other scientific products and also by supporting enterprise systems that maintain and leverage the products’ consistency with the FAIR Principles.
The resulting FAIR roadmap of recommendations describes nine proposed interdependent strategies that could be achieved by coordinated actions taken by different parts of the USGS. A proposed early action would be the creation of a coordinating council that includes representatives from the groups engaged in activities consistent with better alignment with the FAIR Principles. The nine proposed strategies, which are presented in more detail in this roadmap report, focus on enabling improvements to individual data products, providing infrastructure, and structuring administrative activities to support an organizational culture that values the FAIR Principles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221043","usgsCitation":"Lightsom, F.L., Hutchison, V.B., Bishop, B., Debrewer, L.M., Govoni, D.L., Latysh, N., and Stall, S., 2022, Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data: U.S. Geological Survey Open-File Report 2022–1043, 23 p., https://doi.org/10.3133/ofr20221043.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125836","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":402133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1043/coverthb.jpg"},{"id":402134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1043/ofr20221043.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1043"},{"id":402135,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1043/images/"},{"id":402136,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1043/ofr20221043.XML"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori, and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long-term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the selfpropelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
Increased ecological disturbances, species invasions, and climate change are creating severe conservation problems for several plant species that are widespread and foundational. Understanding the genetic diversity of these species and how it relates to adaptation to these stressors are necessary for guiding conservation and restoration efforts. This need is particularly acute for big sagebrush (Artemisia tridentata; Asteraceae), which was once the dominant shrub over 1,000,000 km2 in western North America but has since retracted by half and thus has become the target of one of the largest restoration seeding efforts globally. Here, we present the first reference-quality genome assembly for an ecologically important subspecies of big sagebrush (A. tridentata subsp. tridentata) based on short and long reads, as well as chromatin proximity ligation data analyzed using the HiRise pipeline. The final 4.2-Gb assembly consists of 5,492 scaffolds, with nine pseudo-chromosomal scaffolds (nine scaffolds comprising at least 90% of the assembled genome; n = 9). The assembly contains an estimated 43,377 genes based on ab initio gene discovery and transcriptional data analyzed using the MAKER pipeline, with 91.37% of BUSCOs being completely assembled. The final assembly was highly repetitive, with repeat elements comprising 77.99% of the genome, making the Artemisia tridentata subsp. tridentata genome one of the most highly repetitive plant genomes to be sequenced and assembled. This genome assembly advances studies on plant adaptation to drought and heat stress and provides a valuable tool for future genomic research.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/g3journal/jkac122","usgsCitation":"Melton, A.E., Child, A.W., Beard, R.S., Dumaguit, C.D., Forbey, J.S., Germino, M., de Graaff, M., Kliskey, A., Leitch, I.J., Martinez, P., Novak, S.J., Pellicer, J., Richardson, B., Self, D., Serpe, M.D., and Buerki, S., 2022, A haploid pseudo-chromosome genome assembly for a keystone sagebrush species of western North American rangelands: G3 Genes, Genomes, Genetics, v. 12, no. 7, jkac122, 9 p., https://doi.org/10.1093/g3journal/jkac122.","productDescription":"jkac122, 9 p.","ipdsId":"IP-138937","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447434,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/g3journal/jkac122","text":"Publisher Index Page"},{"id":402105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.28906250000001,\n 35.817813158696616\n ],\n [\n -103.3154296875,\n 35.817813158696616\n ],\n [\n -103.3154296875,\n 48.980216985374994\n ],\n [\n -121.28906250000001,\n 48.980216985374994\n ],\n [\n -121.28906250000001,\n 35.817813158696616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Melton, Anthony E.","contributorId":292452,"corporation":false,"usgs":false,"family":"Melton","given":"Anthony","email":"","middleInitial":"E.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Child, Andrew W.","contributorId":292453,"corporation":false,"usgs":false,"family":"Child","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":844627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, Richard S. Jr.","contributorId":292454,"corporation":false,"usgs":false,"family":"Beard","given":"Richard","suffix":"Jr.","email":"","middleInitial":"S.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumaguit, Carlos Dave C.","contributorId":292455,"corporation":false,"usgs":false,"family":"Dumaguit","given":"Carlos","email":"","middleInitial":"Dave C.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844629,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forbey, Jennifer S.","contributorId":194442,"corporation":false,"usgs":false,"family":"Forbey","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":844630,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844603,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"de Graaff, Marie-Anne","contributorId":195121,"corporation":false,"usgs":false,"family":"de Graaff","given":"Marie-Anne","email":"","affiliations":[],"preferred":false,"id":844631,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kliskey, Andrew","contributorId":189256,"corporation":false,"usgs":false,"family":"Kliskey","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":844632,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leitch, Ilia J.","contributorId":292456,"corporation":false,"usgs":false,"family":"Leitch","given":"Ilia","email":"","middleInitial":"J.","affiliations":[{"id":48590,"text":"Royal Botanic Gardens","active":true,"usgs":false}],"preferred":false,"id":844633,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Martinez, Peggy","contributorId":292457,"corporation":false,"usgs":false,"family":"Martinez","given":"Peggy","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844634,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Novak, Stephen J.","contributorId":208249,"corporation":false,"usgs":false,"family":"Novak","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844635,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pellicer, Jaume","contributorId":292458,"corporation":false,"usgs":false,"family":"Pellicer","given":"Jaume","email":"","affiliations":[{"id":48590,"text":"Royal Botanic Gardens","active":true,"usgs":false}],"preferred":false,"id":844636,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Richardson, Bryce A.","contributorId":37249,"corporation":false,"usgs":true,"family":"Richardson","given":"Bryce A.","affiliations":[],"preferred":false,"id":844637,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Self, Desiree","contributorId":292461,"corporation":false,"usgs":false,"family":"Self","given":"Desiree","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844638,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Serpe, Marcelo D.","contributorId":257074,"corporation":false,"usgs":false,"family":"Serpe","given":"Marcelo","email":"","middleInitial":"D.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844639,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Buerki, Sven","contributorId":257075,"corporation":false,"usgs":false,"family":"Buerki","given":"Sven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844640,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70232202,"text":"70232202 - 2022 - How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","interactions":[],"lastModifiedDate":"2022-06-14T13:08:00.238472","indexId":"70232202","displayToPublicDate":"2022-06-13T11:00:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","docAbstract":"Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire.
Accuracy and map agreement varied considerably among the vegetation types and across time and space (r2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r2) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108935","usgsCitation":"Applestein, C., and Germino, M., 2022, How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?: Ecological Indicators, v. 139, 108935, 11 p., https://doi.org/10.1016/j.ecolind.2022.108935.","productDescription":"108935, 11 p.","ipdsId":"IP-138386","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447436,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108935","text":"Publisher Index Page"},{"id":435803,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8G8XM","text":"USGS data release","linkHelpText":"Modelled functional group vegetation cover from 2016 to 2020 on the Soda Wildfire"},{"id":402095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.05883789062501,\n 42.391008609205045\n ],\n [\n -114.730224609375,\n 42.391008609205045\n ],\n [\n -114.730224609375,\n 45.44471679159555\n ],\n [\n -119.05883789062501,\n 45.44471679159555\n ],\n [\n -119.05883789062501,\n 42.391008609205045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844583,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232200,"text":"70232200 - 2022 - Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","interactions":[],"lastModifiedDate":"2022-09-15T14:08:12.517171","indexId":"70232200","displayToPublicDate":"2022-06-13T10:49:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","docAbstract":"A robust assessment of the American eel (Anguilla rostrata) stock, required to guide conservation efforts, is challenged by the species’ vast range, high variability in demographic parameters and data inadequacies. Novel ideas and underutilised resources that may assist both analytic assessments and spatially oriented modelling include (1) species and environmental databases; (2) mining of data from scattered sources; (3) infilling of data gaps by spatial analysis; (4) age estimation from measurements of DNA methylation; evaluation of eel abundance by (5) larval, (6) glass-bottom boat, (7) net enclosure and (8) eDNA surveys; (9) accounting for dam-induced habitat increases in eel watercourse modelling; (10) spatially oriented modelling with and without temporal components; (11) geographically nested modelling of glass eel recruitment; (12) spawner per recruit modelling and (13) life cycle modelling to examine larval allocation effects. Eel biologists are too few to gather the required assessment data across all of the species’ range. Public posting of electrofishing and eDNA metabarcoding data sets and the use of machine learning techniques to comprehensively inventory small dams will help meet some data needs. These approaches address only a small proportion of the assessment challenges that face American eels. Worldwide collaboration amongst Anguilla scientists is a key enabler of progress towards stock assessment goals.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12572","usgsCitation":"Cairns, D., Benchetrit, J., Bernatchez, L., Bornarel, V., Casselman, J., Castonguay, M., Charsley, A., Dorrow, M., Drouineau, H., Frankowski, J., Haro, A., Hoyle, S., Knickle, D.C., Koops, M.A., Poirier, L.A., Thorson, J.T., Young, J.A., and Zhu, X., 2022, Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment: Fisheries Management and Ecology, v. 29, no. 5, p. 516-541, https://doi.org/10.1111/fme.12572.","productDescription":"26 p.","startPage":"516","endPage":"541","ipdsId":"IP-135604","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fme.12572","text":"Publisher Index 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Best subsets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic explanatory metrics (i.e., duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and six categories of biological response metrics (i.e., composition, habitat, life history, reproductive, tolerance, trophic) computed from fish community samples, as well as fish-based indices of biotic integrity (FIBI) scores and FIBI scores normalized to the an impairment threshold of the corresponding stream class (FIBI_BCG4). Three hydrologic datasets were used to examine rRelations between altered hydrology and fish community responses were examined at three different temporal scalesusing three hydrologic datasets that represented periods of record, long-term changes, and short-term changes to flow regimes in streams of Minnesota.","language":"English","publisher":"Springer","doi":"10.1186/s13717-022-00383-z","usgsCitation":"Ziegeweid, J.R., Johnson, G.D., Krall, A.L., Fitzpatrick, K., and Levin, S., 2022, Quantifying relations between altered hydrology and fish community responses for streams in Minnesota: Ecological Processes, v. 11, 41, 25 p., https://doi.org/10.1186/s13717-022-00383-z.","productDescription":"41, 25 p.","ipdsId":"IP-125704","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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\"}}]}","volume":"11","noUsgsAuthors":false,"publicationDate":"2022-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Gregory D.","contributorId":201568,"corporation":false,"usgs":false,"family":"Johnson","given":"Gregory","email":"","middleInitial":"D.","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":844552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krall, Aliesha L. 0000-0003-2521-5043 adiekoff@usgs.gov","orcid":"https://orcid.org/0000-0003-2521-5043","contributorId":176545,"corporation":false,"usgs":true,"family":"Krall","given":"Aliesha","email":"adiekoff@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fitzpatrick, Kara","contributorId":292426,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Kara","email":"","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":844554,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844555,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232196,"text":"70232196 - 2022 - Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","interactions":[],"lastModifiedDate":"2022-06-13T15:44:24.981546","indexId":"70232196","displayToPublicDate":"2022-06-13T10:31:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","docAbstract":"Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.
Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.
This paper develops a barometer that indexes water quality in the Chesapeake Bay and summarizes quality over spatial regions and temporal periods. The barometer has a basis in risk assessment and hydrology, and is a function of three different metrics of water quality relative to numerical criteria: relative frequency of criterion attainment; magnitude of deviation from a numerical criterion; and duration of criterion attainment. Metrics associated with these features are calculated at the station level, allowing flexibility for simultaneously evaluating multiple stressors, different designated uses, and physical characteristics of the water. The barometer score is then created as a geometric mean of the three metrics. The water quality barometer (WQB) station scores may be spatially aggregated to report habitat scores across a spectrum of spatial resolutions (e.g., management segment, tidal subsystem, or the whole tidal bay). Dissolved oxygen measurements in the Chesapeake Bay collected during summer seasons of 1985 to 2020 are used to evaluate water quality. The WQB score and its bootstrapped confidence interval are reported at the station, segment, tidal subsystem and whole tidal bay levels. Notably, water quality interpreted through application of the WQB with dissolved oxygen concentration data and averaged over the 29-year period of record is good (i.e. protects aquatic living resources) in tributaries such as the James River, Rappahannock River and others; but is not as good in areas such as the Upper Tributaries and the York River. Recent summaries indicate that while the water quality is improving in much of the bay and its tidal tributaries, however, there is an indication of decline in quality in the period 20182020, especially in the upper regions of the Bay. The barometer is designed around using the time series data produced by the Chesapeake Bay Programs annual monitoring strategy; the approach has application to other large water bodies with large scale monitoring programs with extended time series or for integrating information from environmental sensor systems.
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221033","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, M.N., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021: U.S. Geological Survey Open-File Report 2022–1033, 38 p., https://doi.org/10.3133/ofr20221033.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-137795","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.pdf","text":"Report","size":"3.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1033"},{"id":401934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1033/coverthb.jpg"},{"id":402059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221033/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401939,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":401938,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1033/images"},{"id":401937,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The rate and timing of hydrologically forced landslides is a complex function of precipitation patterns, material properties, topography, and groundwater hydrology. In the simplest form, however, slopes fail when subsurface pore pressure grows large enough to exceed the Mohr-Coulomb failure criterion. The capacity for pore pressure rise in a landslide is determined in part by the thickness of the unsaturated zone above the water table, which itself is set by weathering patterns that should have predictable patterns across different lithologies. To investigate how this structure affects landslide behavior, we exploit a multi-year record of precipitation, pore pressure, and velocity from Oak Ridge earthflow, a slow-moving landslide set in Franciscan mélange, northern California, USA. In conjunction with electrical resistivity tomography and hydraulic conductivity measurements, these data show that Oak Ridge has a thin weathered profile that is comparable in thickness to other mélange landslides in California. We propose that due to the inherently thin vadose zone, mélange landscapes experience an unusually high water table that frequently brings them close to movement; however, the capacity to increase stress is limited by the small amount of dynamic storage available. Instead, excess pore pressure is shed via springs and saturation overland flow once the water table reaches the surface. Linkages between weathering patterns, hydrology, and deformation can explain behavior patterns exhibited by Franciscan mélange earthflows across a large precipitation gradient.
Designing a population monitoring program for Asian bears presents challenges associated with their low densities and detectability, generally large home ranges, and logistical or resource constraints. The use of an occupancy-based method to monitor bear populations can be appropriate under certain conditions given the mechanistic relationship between occupancy and abundance. The form of the occupancy–abundance relationship is dependent on species-specific characteristics such as home range size and population density, as well as study area size. To assess the statistical power of tests to detect population change of Asian bears, we conducted a study using a range of scenarios by simulating spatially explicit individual-based capture-recapture data from a demographically open model. Simulations assessed the power to detect changes in population density via changes in site-level occupancy or abundance through time, estimated using a standard occupancy model or a Royle-Nichols model, both with point detectors (representing camera traps). We used IUCN Red List criteria as a guide in selection of two population decline scenarios (20% and 50%), but we chose a shorter time horizon (10 years = 1 bear generation), meaning that declines were steeper than used for IUCN criteria (3 generations). Our simulations detected population declines of 50% with high power (>0.80) and low false positive rates (FPR: incorrectly detecting a decline) (<0.10) when detectors were spaced at > 0.67 times the home range diameter (home-range spacing ratio: HRSR, a measure of spatial correlation), such that bears would tend to overlap no more than two detectors. There was high (0.85) correlation between realized occupancy and N in these scenarios. The FPR increased as the HRSR decreased because of spatial correlation in the occupancy process induced when individual home ranges overlap multiple detectors. The mean statistical power to detect more gradual population declines (20% in 10 years) with HRSR > 0.67 was low for occupancy models 0.22 (maximum power 0.67) and Royle-Nichols models (0.24; maximum power 0.67), suggesting that declines of this magnitude may not be described reliably with 10 years of monitoring. Our results demonstrated that under many realistic scenarios that we explored, false positive rates were unacceptably high. We highlight that when designing occupancy studies, the spacing between point detectors be at least 0.67 times the diameter of the home range size of the larger sex (e.g., males) when the assumptions of the spatial capture-recapture model used for simulation are met.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02075","usgsCitation":"Fuller, A.K., Augustine, B., Morin, D., Pigeon, K., Boulanger, J., Lee, D., Bisi, F., and Garshelis, D., 2022, The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears: Global Ecology and Conservation, v. 35, e02075, 18 p., https://doi.org/10.1016/j.gecco.2022.e02075.","productDescription":"e02075, 18 p.","ipdsId":"IP-135705","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02075","text":"Publisher Index Page"},{"id":480920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, Ben C.","contributorId":349417,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":924298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morin, Dana J.","contributorId":349419,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":924299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pigeon, Karine","contributorId":349420,"corporation":false,"usgs":false,"family":"Pigeon","given":"Karine","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":924300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boulanger, John","contributorId":349422,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","affiliations":[{"id":83347,"text":"Integrated Ecological Research","active":true,"usgs":false}],"preferred":false,"id":924301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, David C.","contributorId":349424,"corporation":false,"usgs":false,"family":"Lee","given":"David C.","affiliations":[{"id":83348,"text":"University of South Wales","active":true,"usgs":false}],"preferred":false,"id":924302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bisi, Francesco","contributorId":349429,"corporation":false,"usgs":false,"family":"Bisi","given":"Francesco","affiliations":[{"id":83482,"text":"University of Insubria","active":true,"usgs":false}],"preferred":false,"id":924303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garshelis, David L.","contributorId":349431,"corporation":false,"usgs":false,"family":"Garshelis","given":"David L.","affiliations":[{"id":83484,"text":"IUCN SSC Bear Specialist Group.","active":true,"usgs":false}],"preferred":false,"id":924304,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232217,"text":"70232217 - 2022 - A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","interactions":[],"lastModifiedDate":"2022-09-27T16:46:26.038359","indexId":"70232217","displayToPublicDate":"2022-06-09T09:23:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","title":"A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","docAbstract":"Despite the recognized role of wild waterfowl in the potential dispersal and transmission of highly pathogenic avian influenza (HPAI) virus, little is known about how infection affects these birds. This lack of information limits our ability to estimate viral spread in the event of an HPAI outbreak, thereby limiting our abilities to estimate and communicate risk. Here we present telemetry data from a wild Lesser Scaup (Aythya affinis), captured during a separate ecology study in the Chesapeake Bay, Maryland. This bird tested positive for infection with clade 2.3.4.4 HPAI virus of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 lineage (results received post-release) during the 2021–22 ongoing outbreaks in North America. While the infected bird was somewhat lighter than other adult males surgically implanted with transmitters (790g, ߂ = 868g, n = 11), it showed no clinical signs of infection at capture, during surgery, nor upon release. The bird died 3d later, pathology undetermined as the specimen was not able to be recovered. Analysis of movement data within the 3d window showed that the infected individual's maximum and average hourly movements (3894.3m, 428.8m respectively) were noticeably lower than noninfected conspecifics tagged and released the same day (߂ = 21594.5m, ߂ = 1097.9m, respectively; n = 4). We identified four instances where the infected bird had close contact (fixes located within 25m and 15 min) with another marked bird during this time. Collectively, these data suggest that the HPAI positive bird observed in this study may have been shedding virus for some period prior to death, with opportunities for direct bird to bird or environmental transmission. Although limited by low sample size and proximity to the time of tagging, we hope that these data will provide useful information as managers continue to respond to this ongoing outbreak event.
","language":"English","publisher":"Wiley","doi":"10.1111/tbed.14614","usgsCitation":"Prosser, D., Schley, H., Simmons, N., Sullivan, J.D., Homyack, J., Weegman, M.M., Olsen, G.H., Berlin, A., Poulson, R., Stallknecht, D., and Williams, C.K., 2022, A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors: Transboundary and Emerging Diseases, v. 69, no. 5, p. e2653-e2660, https://doi.org/10.1111/tbed.14614.","productDescription":"8 p.","startPage":"e2653","endPage":"e2660","ipdsId":"IP-138877","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":435810,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJG53M","text":"USGS data release","linkHelpText":"Telemetry data of a Lesser Scaup (Aythya affinis) positive for 2.3.4.4 Highly Pathogenic H5N1"},{"id":402195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schley, Hannah","contributorId":292145,"corporation":false,"usgs":false,"family":"Schley","given":"Hannah","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":844691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simmons, Nathan","contributorId":292146,"corporation":false,"usgs":false,"family":"Simmons","given":"Nathan","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":844692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":844693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Homyack, Josh","contributorId":292150,"corporation":false,"usgs":false,"family":"Homyack","given":"Josh","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":844694,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weegman, Matthew M.","contributorId":200610,"corporation":false,"usgs":false,"family":"Weegman","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844695,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olsen, Glenn H. 0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":238130,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844696,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Berlin, Alicia 0000-0002-5275-3077","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":216023,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844697,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":844698,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":844699,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Williams, Christopher K.","contributorId":202263,"corporation":false,"usgs":false,"family":"Williams","given":"Christopher","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":844700,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236280,"text":"70236280 - 2022 - Tree rings reveal unmatched 2nd century drought in the Colorado River Basin","interactions":[],"lastModifiedDate":"2022-08-31T14:13:13.360127","indexId":"70236280","displayToPublicDate":"2022-06-09T09:01:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Tree rings reveal unmatched 2nd century drought in the Colorado River Basin","docAbstract":"The ongoing 22 year drought in the Upper Colorado River Basin (UCRB) has been extremely severe, even in the context of the longest available tree-ring reconstruction of annual flow at Lees Ferry, Arizona, dating back to 762 CE. While many southwestern drought assessments have been limited to the past 1200 years, longer paleorecords of moisture variability do exist for the UCRB. Here, gridded drought-atlas data in the UCRB domain along with naturalized streamflow data from the instrumental period (1906–2021) are used in a K nearest neighbor (KNN) nonparametric algorithm to develop a streamflow reconstruction for the Lees Ferry gage starting in 1 CE. The reconstruction reveals a 2nd century drought unmatched in severity by the current drought or by well-documented medieval period droughts in the UCRB. Although data are sparse, analysis of individual long tree ring records and other paleoclimatic data also support the occurrence of an exceptional 2nd century drought.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL098781","usgsCitation":"Gangopadhyay, S., Woodhouse, C., McCabe, G.J., Routson, C.C., and Meko, D., 2022, Tree rings reveal unmatched 2nd century drought in the Colorado River Basin: Geophysical Research Letters, v. 49, no. 11, e2022GL098781, 10 p., https://doi.org/10.1029/2022GL098781.","productDescription":"e2022GL098781, 10 p.","ipdsId":"IP-139459","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":447488,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl098781","text":"Publisher Index Page"},{"id":405995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.03857421875,\n 36.38591277287651\n ],\n [\n -111.181640625,\n 36.58024660149866\n ],\n [\n -110.25878906249999,\n 36.36822190085111\n ],\n [\n -109.9072265625,\n 35.88905007936091\n ],\n [\n -108.8525390625,\n 35.782170703266075\n ],\n [\n -107.51220703125,\n 35.67514743608467\n ],\n [\n -106.74316406249999,\n 36.56260003738545\n ],\n [\n -106.34765625,\n 37.07271048132943\n ],\n [\n -106.5673828125,\n 37.64903402157866\n ],\n [\n -106.12792968749999,\n 38.048091067457236\n ],\n [\n -106.083984375,\n 39.07890809706475\n ],\n [\n -105.0732421875,\n 39.825413103424786\n ],\n [\n -105.35888671875,\n 40.96330795307353\n ],\n [\n -107.11669921875,\n 42.342305278572816\n ],\n [\n -110.32470703125,\n 43.29320031385282\n ],\n [\n -110.6982421875,\n 43.56447158721811\n ],\n [\n -110.91796875,\n 41.75492216766298\n ],\n [\n -111.1376953125,\n 41.393294288784865\n ],\n [\n -111.4013671875,\n 41.09591205639546\n ],\n [\n -111.884765625,\n 39.90973623453719\n ],\n [\n -112.19238281249999,\n 38.66835610151506\n ],\n [\n -112.3681640625,\n 37.21283151445594\n ],\n [\n -112.39013671875,\n 36.65079252503471\n ],\n [\n -112.03857421875,\n 36.38591277287651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":850421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodhouse, Connie 0000-0003-0545-9753","orcid":"https://orcid.org/0000-0003-0545-9753","contributorId":296028,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":850422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":850423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Routson, Cody C. 0000-0001-8694-7809","orcid":"https://orcid.org/0000-0001-8694-7809","contributorId":187600,"corporation":false,"usgs":false,"family":"Routson","given":"Cody","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":850424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meko, David 0000-0002-5171-2724","orcid":"https://orcid.org/0000-0002-5171-2724","contributorId":296029,"corporation":false,"usgs":false,"family":"Meko","given":"David","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":850425,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231446,"text":"ofr20221031 - 2022 - Dynamic rating method for computing discharge from time-series stage data","interactions":[],"lastModifiedDate":"2026-03-27T20:08:34.586583","indexId":"ofr20221031","displayToPublicDate":"2022-06-08T08:55:54","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1031","displayTitle":"Dynamic Rating Method for Computing Discharge from Time-Series Stage Data","title":"Dynamic rating method for computing discharge from time-series stage data","docAbstract":"Ratings are used for a variety of reasons in water-resources investigations. The simplest rating relates discharge to the stage of the river. From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because of unsteady flow as a flood wave passes. Simple ratings are unable to represent hysteresis in a stage/discharge relation. A dynamic rating method is capable of capturing hysteresis owing to the variable energy slope caused by unsteady momentum and pressure.
A dynamic rating method developed to compute discharge from stage for compact channel geometry, referred to as DYNMOD, previously has been developed through a simplification of the one-dimensional Saint-Venant equations. A dynamic rating method, which accommodates compound and compact channel geometry, referred to as DYNPOUND, has been developed through a similar simplification as a part of this study. The DYNMOD and DYNPOUND methods were implemented in the Python programming language. Discharge time series computed with the dynamic rating method implementations were then compared to simulated discharge time series and discrete discharge measurements made at U.S. Geological Survey streamgage sites.
Four sets of stage and discharge time series were created using one-dimensional unsteady simulation software with compound channel geometry to compare the results of both dynamic rating methods to results from the full one-dimensional shallow water equations. Discharge time series were computed from stage time series using DYNMOD and DYNPOUND. DYNPOUND outperformed DYNMOD in all four scenarios. The minimum and maximum mean squared logarithmic error (MSLE) for the DYNMOD results were 2.75×10−2 and 3.40×10−2, respectively. The minimum and maximum MSLE for the DYNPOUND results were 2.51×10−7 and 1.91×10−4, respectively.
The dynamic rating methods were calibrated for six U.S. Geological Survey streamgage sites using observed discharge data collected at the sites. The calibration objective for each site was to minimize the MSLE of the discharge computed with the rating method with respect to observed discharge. For each site, the calibration included all field measurements within a selected water year. The DYNMOD method failed to compute discharge for the full calibration time series for three sites. A method fails to compute when the implementation returns a nonfinite value at a time step. Because the values computed for following time steps are dependent on the previous time step, a nonfinite value results in nonfinite values that follow. For the three sites for which DYNMOD computed the complete discharge time series, the minimum MSLE for calibration was 2.19×10−3 and the maximum was 9.77×10−3. The MSLE of the DYNPOUND computed discharge calibration time series for the six sites ranged from 3.70×10−3 to 1.25. For each site, an event-based time period was selected to compare the discharge time series computed with the dynamic rating methods to discrete discharge field measurements made at the streamgage sites. The DYNMOD-computed discharge time series for the three sites had an MSLE range of 2.76×10−3 to 3.14×10−2. The range of MSLE for the six DYNPOUND sites was 3.64×10−3 to 7.23×10−2. Although the DYNMOD method outperforms the DYNPOUND method when calibrated streamgage sites are under consideration, the DYNMOD method failed to compute a discharge time series at three of the six sites. The DYNPOUND method, therefore, was more robust than the DYNMOD method. Improvements to the implementation of the DYNPOUND method may improve the accuracy of the method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221031","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Domanski, M., Holmes, R.R., Jr., and Heal, E.N., 2022, Dynamic rating method for computing discharge from time-series stage data: U.S. Geological Survey Open-File Report 2022–1031, 48 p., https://doi.org/10.3133/ofr20221031.","productDescription":"Report: vii, 48 p.; 2 Data Releases; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-128037","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501770,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113161.htm","linkFileType":{"id":5,"text":"html"}},{"id":400457,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":400459,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YUV9DG","text":"USGS data release","linkHelpText":"Dynamic stage to discharge rating model archive"},{"id":400458,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P955QRPQ","text":"USGS data release","linkHelpText":"Dynamic rating method for computing discharge from time series stage data—Site datasets"},{"id":400454,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.XML"},{"id":400455,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1031/images"},{"id":400453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1031"},{"id":400452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1031/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801