The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires > 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (<1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.
The behavior of young songbirds after fledging is one of the least understood phases of the breeding cycle, although parental provisioning rates and movement of fledglings are key to understanding life history evolution. We studied Cordilleran Flycatchers (Empidonax occidentalis) at two sites in southwestern Colorado, USA, from 2012 to 2017. We banded and sexed breeding adults to determine the relative contributions of males and females to nestling and fledgling care, and attached radio-transmitters to nestlings to facilitate observations of brood behavior after fledging. Females made 60% and 78% of total observed feedings of nestlings and fledglings, respectively. Parental provisioning rates increased with nestling age, and per-nestling provisioning rates increased with brood size. Parental provisioning rates declined just before fledging, then increased just after fledging. Fledging times of individuals in broods were asynchronous and concentrated during the late afternoon and early evening. Males stopped caring for fledglings before females even though this species is single-brooded, with some late-season broods being abandoned by males. Broods spent the first three weeks after fledging within 400 m of nests, after which they began to disperse. Most aspects of the breeding biology of Cordilleran Flycatchers in our study, including the duration of nestling and fledging periods, female-dominated provisioning, and movement patterns of fledglings, were similar to those of other Empidonax species. However, the times when young fledged were not concentrated in the morning as reported in most other songbirds, and this result warrants additional study of the timing of fledging in ecologically and taxonomically similar species. The increased per-nestling provisioning rate with increasing brood size was unexpected, and additional study is needed to determine if this increase results from a trade-off between adult annual survival and productivity favoring increased provisioning of young in larger broods, or from the existence of high-quality individuals where larger clutches and higher provisioning rates are linked.
The malaria parasite Plasmodium relictum (lineage GRW4) was introduced less than a century ago to the native avifauna of Hawaiʻi, where it has since caused major declines of endemic bird populations. One of the native bird species that is frequently infected with GRW4 is the Hawaiʻi ʻamakihi (Chlorodrepanis virens). To achieve a better understanding of the transcriptional activities of this virulent parasite, we performed a controlled challenge experiment of 15 ʻamakihi that were infected with GRW4. Blood samples containing malaria parasites were collected at two time points (intermediate and peak infection stages) from host individuals that were either experimentally infected by mosquitoes or inoculated with infected blood. We then used RNA sequencing to assemble a high‐quality blood transcriptome of P. relictum GRW4, allowing us to quantify parasite expression levels inside individual birds. We found few significant differences (one to two transcripts) in GRW4 expression levels between host infection stages and between inoculation methods. However, 36 transcripts showed differential expression levels among all host individuals, indicating a potential presence of host‐specific gene regulation across hosts. To reduce the extinction risk of the remaining native bird species in Hawaiʻi, genetic resources of the local Plasmodium lineage are needed to enable further molecular characterization of this parasite. Our newly built Hawaiian GRW4 transcriptome assembly, together with analyses of the parasite's transcriptional activities inside the blood of Hawaiʻi ʻamakihi, can provide us with important knowledge on how to combat this deadly avian disease in the future.
The United States (US) National Park Service (NPS) manages protected public lands to preserve biodiversity. Exposure to and effects of bioactive organic contaminants in NPS streams are challenges for resource managers. Recent assessment of pesticides and pharmaceuticals in protected-streams within the urbanized NPS Southeast Region (SER) indicated the importance of fluvial inflows from external sources as drivers of aquatic contaminant-mixture exposures. Great Smoky Mountains National Park (GRSM), lies within SER, has the highest biodiversity and annual visitation of NPS parks, but, in contrast to the previously studied systems, straddles a high-elevation hydrologic divide; this setting limits fluvial-inflows of contaminants but potentially increases visitation-driven contaminant deliveries. We leveraged the unique characteristics of GRSM to test further the importance of fluvial contaminant inflows as drivers of protected-stream exposures and to inform the relative importance of potential additional contaminant transport mechanisms, by comparing the estimated risks of 328 pesticides and pharmaceuticals in water at 16 GRSM stream locations to those estimated previously in SER streams. Extensive mixtures (31 compounds) were only observed in an atypical reach on the boundary of GRSM downstream of a wastewater discharge, while limited mixtures (2–5 compounds) were observed in one stream with elevated visitation pressure (recreational “tube floating”). The insecticide, imidacloprid, used to eradicate hemlock woolly adelgid, was detected in 8 (50%) streams. Infrequent exceedances of a cumulative ToxCast-based, exposure-activity ratio (ΣEAR) 0.001 screening-level of concern suggested limited risk to non-target, aquatic vertebrates, whereas exceedances of a cumulative benchmark-based, invertebrate toxicity quotient (ΣTQ) 0.1 screening level at 8 locations indicated generally high risk to invertebrates. The results are consistent with the importance of fluvial transport from extra-park sources as a driver of bioactive-contaminant mixture exposures in protected streams and illustrate the potential additional risks from visitation-driven and tactical-use-pesticides.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.146711","usgsCitation":"Bradley, P., Kulp, M.A., Huffman, B.J., Romanok, K., Smalling, K., Breitmeyer, S.E., Clark, J., and Journey, C., 2021, Reconnaissance of cumulative risk of pesticides and pharmaceuticals in Great Smoky Mountains National Park streams: Science of the Total Environment, v. 781, 146711, 9 p., https://doi.org/10.1016/j.scitotenv.2021.146711.","productDescription":"146711, 9 p.","onlineOnly":"N","ipdsId":"IP-117880","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":436436,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GUEIMD","text":"USGS data release","linkHelpText":"Pesticide and Pharmaceutical Exposure Data for Select Streams within Great Smoky Mountains National Park, 2019"},{"id":384693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Great Smokey Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.166259765625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.03899204678081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"781","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":813009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huffman, Bradley J. 0000-0003-2827-8074","orcid":"https://orcid.org/0000-0003-2827-8074","contributorId":220344,"corporation":false,"usgs":true,"family":"Huffman","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":813012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clark, Jimmy 0000-0002-3138-5738","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":221235,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Journey, Celeste A. 0000-0002-2284-5851","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":221232,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70220205,"text":"70220205 - 2021 - Measuring and interpreting multilayer aquifer-system compactions for a sustainable groundwater-system development","interactions":[],"lastModifiedDate":"2021-04-27T11:44:36.379671","indexId":"70220205","displayToPublicDate":"2021-03-23T06:40:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Measuring and interpreting multilayer aquifer-system compactions for a sustainable groundwater-system development","docAbstract":"Ever decreasing water resources and climate change have driven the increasing use of groundwater causing land subsidence in many countries. Geodetic sensors such as InSAR, GPS and leveling can detect surface deformation but cannot measure subsurface deformation. A single‐well, single‐depth extensometer can be used to measure subsurface deformation, but it cannot delineate the depths of major compaction and provide insight about the deformation mechanism throughout a complex aquifer system, unless man extensometers at different depths are used. We present a multilayer compaction well (MLCW), installed in a borehole, that uses magnetic rings to detect stratum compaction at 25 depths as deep as 300 m below land surface. Our laboratory and field assessments indicate 1 mm precision and accuracy for one single‐depth magnetic reading. We tested the performance of MLCW by measuring aquifer‐system compaction over the proximal, middle, and distal fans of the Choushui River Alluvial Fan (CRAF) that has long experienced severe land subsidence. The MLCW measurements were used to create time‐depth diagrams of compaction, showing different compaction rates at different layers of aquifers and aquitards to identify the depths of major compactions. The elastic (reversible) and inelastic (irreversible) compactions from MLCW were used in stress‐strain analyses to estimate skeletal specific storages and the safe groundwater levels, below which groundwater extractions have caused irreversible compactions. The hydrogeological parameters derived from MLCW measurements can help governmental agencies to determine effective land‐use and water‐use policies, and ascertain the best strategy for utilizing artificial recharge to prevent land subsidence and achieve sustainable groundwater management.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028194","usgsCitation":"Hung, W., Hwang, C., Sneed, M., Chen, Y., Chu, C., and Lin, S., 2021, Measuring and interpreting multilayer aquifer-system compactions for a sustainable groundwater-system development: Water Resources Research, v. 57, no. 4, e2020WR028194, 19 p., https://doi.org/10.1029/2020WR028194.","productDescription":"e2020WR028194, 19 p.","ipdsId":"IP-120150","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028194","text":"Publisher Index Page"},{"id":385313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hung, Wei-Chia","contributorId":172937,"corporation":false,"usgs":false,"family":"Hung","given":"Wei-Chia","email":"","affiliations":[{"id":27123,"text":"Green Environmental Engineering Consultant Co. LTD, Hsinchu, Taiwan","active":true,"usgs":false}],"preferred":false,"id":814749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hwang, Cheinway 0000-0002-3322-353X","orcid":"https://orcid.org/0000-0002-3322-353X","contributorId":172932,"corporation":false,"usgs":false,"family":"Hwang","given":"Cheinway","email":"","affiliations":[{"id":27120,"text":"Department of Civil Engineering, National Chiao Tung University, Taiwan","active":true,"usgs":false}],"preferred":false,"id":814750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sneed, Michelle 0000-0002-8180-382X micsneed@usgs.gov","orcid":"https://orcid.org/0000-0002-8180-382X","contributorId":155,"corporation":false,"usgs":true,"family":"Sneed","given":"Michelle","email":"micsneed@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Yi-An","contributorId":257631,"corporation":false,"usgs":false,"family":"Chen","given":"Yi-An","email":"","affiliations":[{"id":52072,"text":"Department of Earth Sciences, National Central University, Taiwan","active":true,"usgs":false}],"preferred":false,"id":814752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chu, Chi-Hua","contributorId":257632,"corporation":false,"usgs":false,"family":"Chu","given":"Chi-Hua","email":"","affiliations":[{"id":52074,"text":"Green Environmental Engineering Consultant Co. LTD, Taiwan","active":true,"usgs":false}],"preferred":false,"id":814753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lin, Shao-Hung","contributorId":257633,"corporation":false,"usgs":false,"family":"Lin","given":"Shao-Hung","email":"","affiliations":[{"id":52074,"text":"Green Environmental Engineering Consultant Co. LTD, Taiwan","active":true,"usgs":false}],"preferred":false,"id":814754,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229067,"text":"70229067 - 2021 - Ashmole's hypothesis and the latitudinal gradient in clutch size","interactions":[],"lastModifiedDate":"2022-02-28T15:57:25.094828","indexId":"70229067","displayToPublicDate":"2021-03-22T09:53:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Ashmole's hypothesis and the latitudinal gradient in clutch size","docAbstract":"One enduring priority for ecologists has been to understand the cause(s) of variation in reproductive effort among species and localities. Avian clutch size generally increases with increasing latitude, both within and across species, but the mechanism(s) driving that pattern continue to generate hypotheses and debate. In 1961, a Ph.D. student at Oxford University, N. Philip Ashmole, proposed the influential hypothesis that clutch size varies in direct proportion to the seasonality of resources available to a population. Ashmole's hypothesis has been widely cited and discussed in the ecological literature. However, misinterpretation and confusion has been common regarding the mechanism that underlies Ashmole's hypothesis and the testable predictions it generates. We review the development of well-known hypotheses to explain clutch size variation with an emphasis on Ashmole's hypothesis. We then discuss and clarify sources of confusion about Ashmole's hypothesis in the literature, summarise existing evidence in support and refutation of the hypothesis, and suggest some under-utilised and novel approaches to test Ashmole's hypothesis and gain an improved understanding of the mechanisms responsible for variation in avian clutch size and fecundity, and life-history evolution in general.
","language":"English","publisher":"Wiley","doi":"10.1111/brv.12705","usgsCitation":"Lundblad, C., and Conway, C.J., 2021, Ashmole's hypothesis and the latitudinal gradient in clutch size: Biological Reviews, v. 96, no. 4, p. 1349-1366, https://doi.org/10.1111/brv.12705.","productDescription":"18 p.","startPage":"1349","endPage":"1366","ipdsId":"IP-121474","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Lundblad, Carl G.","contributorId":265812,"corporation":false,"usgs":false,"family":"Lundblad","given":"Carl G.","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":836398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836397,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219520,"text":"70219520 - 2021 - Fire frequency impacts soil properties and processes in sagebrush steppe ecosystems of the Columbia Basin","interactions":[],"lastModifiedDate":"2021-04-12T13:47:40.249648","indexId":"70219520","displayToPublicDate":"2021-03-21T08:42:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":846,"text":"Applied Soil Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fire frequency impacts soil properties and processes in sagebrush steppe ecosystems of the Columbia Basin","docAbstract":"Increased fire frequency in semi-arid ecosystems can alter biochemical soil properties and soil processes that underpin ecosystem structure and functioning, thus threatening native plant communities and the species that rely on them. However, there is much uncertainty about the magnitude of change as soils are exposed to more fires, because soil recovery and changes in fire severity following a first fire mediate the impact of successive fires on soil properties. With this study we aim to evaluate how increased fire frequency affects soil biochemical properties (i.e. soil pH, soil organic matter (SOM), soil organic carbon (SOC), soil structure and mineral N) and processes (i.e. microbial and enzymatic activity) in a sagebrush-steppe ecosystem located in the Columbia Plateau Ecoregion, Washington, USA. During 2016, we collected soils from once (2012), twice (2003 and 2012), and thrice (2003, 2007, and 2012) burned areas, enabling us to test the hypothesis that increasing fire frequency will exacerbate the impact of fire on soil properties and processes. Our study yielded three main results: (1) fire reduced the total soil C concentration and soil C in aggregates relative to unburned soil, but only when soil was exposed to fire once (i.e. the most recent fire), (2) compared to the unburned soils, SOM contents, enzyme activity and microbial CO2 respiration were suppressed in the once and thrice burned soils, but not in the twice burned soils, and (3) fire increased NO3−-N contents across the once and twice burned sites, and reduced enzyme activity associated with N cycling in the thrice burned sites. Taken together, our findings suggest that a one-time fire in this shrub dominated semi-arid ecosystem significantly changes soil biochemical attributes and microbially driven processes. With sufficient time between fires, these structural and functional properties can partially recover, and this may persist even after a second fire, but recovery is limited when a third fire creates an additional disturbance at a shorter time interval. Furthermore, while soil C pools and microbial decomposition processes were able to recover with sufficient time, greater soil resource availability prevailed in soil across all fire frequencies, indicating that fire is likely to promote invasion and reduce ecosystem stability, even when other soil properties recover.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apsoil.2021.103967","usgsCitation":"Nichols, L., Shinneman, D.J., McIlroy, S., and de Graaff, M., 2021, Fire frequency impacts soil properties and processes in sagebrush steppe ecosystems of the Columbia Basin: Applied Soil Ecology, v. 165, 103967, 12 p., https://doi.org/10.1016/j.apsoil.2021.103967.","productDescription":"103967, 12 p.","ipdsId":"IP-122506","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":385008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","county":"Lincoln County","otherGeospatial":"Columbia Plateau Ecoregion","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-118.9787,47.9408],[-118.9673,47.9407],[-118.9625,47.9411],[-118.9375,47.9427],[-118.9301,47.9426],[-118.9152,47.9447],[-118.9064,47.946],[-118.8956,47.9473],[-118.8868,47.949],[-118.882,47.9527],[-118.8779,47.9554],[-118.8664,47.9562],[-118.8529,47.9569],[-118.8468,47.9564],[-118.8428,47.9559],[-118.8322,47.9475],[-118.8323,47.9419],[-118.8292,47.9322],[-118.8232,47.9271],[-118.8093,47.9195],[-118.781,47.9178],[-118.765,47.9121],[-118.7437,47.9026],[-118.737,47.9007],[-118.7282,47.8987],[-118.7174,47.9018],[-118.7126,47.905],[-118.709,47.9114],[-118.7102,47.9183],[-118.712,47.9257],[-118.7085,47.9316],[-118.6975,47.938],[-118.6839,47.9433],[-118.6712,47.9362],[-118.6598,47.9352],[-118.65,47.924],[-118.6411,47.9275],[-118.6249,47.9269],[-118.6175,47.9268],[-118.6054,47.928],[-118.5919,47.9278],[-118.5762,47.9317],[-118.5669,47.9297],[-118.5595,47.926],[-118.5462,47.9202],[-118.5328,47.9178],[-118.5297,47.9085],[-118.5389,47.8953],[-118.5433,47.8829],[-118.5395,47.8759],[-118.5261,47.8716],[-118.5161,47.8705],[-118.5012,47.8703],[-118.4938,47.8721],[-118.4856,47.8756],[-118.4618,47.8803],[-118.4554,47.872],[-118.4562,47.8678],[-118.4542,47.8646],[-118.4538,47.8577],[-118.4441,47.8446],[-118.4381,47.8427],[-118.4208,47.8369],[-118.4001,47.8297],[-118.3921,47.8282],[-118.3819,47.8303],[-118.3711,47.8297],[-118.3608,47.836],[-118.3573,47.8405],[-118.3558,47.8447],[-118.3557,47.8474],[-118.3535,47.8529],[-118.3499,47.8598],[-118.3505,47.8644],[-118.3487,47.8782],[-118.3409,47.8891],[-118.3387,47.8951],[-118.3238,47.8976],[-118.319,47.898],[-118.3169,47.9016],[-118.3113,47.9075],[-118.3071,47.9116],[-118.3009,47.9152],[-118.2927,47.9188],[-118.2845,47.9214],[-118.2749,47.9272],[-118.2679,47.9322],[-118.2604,47.9348],[-118.25,47.9443],[-118.2413,47.9428],[-118.2373,47.9404],[-118.2312,47.9408],[-118.2251,47.9416],[-118.2197,47.9429],[-118.2149,47.9447],[-118.2074,47.945],[-118.2042,47.9422],[-118.2017,47.9362],[-118.1984,47.9333],[-118.1979,47.9301],[-118.1966,47.9264],[-118.194,47.9231],[-118.1915,47.9199],[-118.1868,47.9175],[-118.183,47.9119],[-118.1831,47.9082],[-118.1792,47.9045],[-118.1746,47.9012],[-118.1654,47.895],[-118.1628,47.8931],[-118.1609,47.8894],[-118.1597,47.8857],[-118.1563,47.8838],[-118.1544,47.8829],[-118.1497,47.8823],[-118.1483,47.8814],[-118.143,47.879],[-118.1371,47.8757],[-118.1318,47.8719],[-118.126,47.8653],[-118.1228,47.8598],[-118.1231,47.8538],[-118.1226,47.8492],[-118.1234,47.8455],[-118.1229,47.839],[-118.1224,47.834],[-118.1212,47.8312],[-118.1199,47.8312],[-118.1085,47.8291],[-118.1032,47.8263],[-118.0818,47.8033],[-118.0792,47.801],[-118.0746,47.7986],[-118.072,47.7967],[-118.068,47.7953],[-118.0633,47.7956],[-118.0586,47.7951],[-118.0552,47.795],[-118.0505,47.7959],[-118.0457,47.7962],[-118.041,47.7971],[-118.0369,47.797],[-118.0322,47.7988],[-118.0281,47.8005],[-118.0253,47.8023],[-118.0226,47.8037],[-118.0198,47.8055],[-118.0163,47.8082],[-118.0143,47.809],[-118.0121,47.8118],[-118.008,47.8149],[-118.0051,47.8186],[-118.003,47.8217],[-118.0008,47.8254],[-117.9981,47.8276],[-117.9946,47.8299],[-117.9918,47.8321],[-117.9863,47.8348],[-117.9769,47.8351],[-117.9729,47.8336],[-117.9636,47.8298],[-117.959,47.8283],[-117.9349,47.8237],[-117.9247,47.8263],[-117.9177,47.8322],[-117.9102,47.8339],[-117.9,47.8364],[-117.8939,47.8382],[-117.8898,47.8404],[-117.885,47.8431],[-117.8774,47.8466],[-117.8727,47.8451],[-117.8648,47.8413],[-117.8621,47.8422],[-117.8575,47.8393],[-117.8509,47.8355],[-117.8428,47.8367],[-117.836,47.8361],[-117.8321,47.8342],[-117.8296,47.8296],[-117.8217,47.8234],[-117.8181,47.7],[-117.8217,47.6117],[-117.8216,47.435],[-117.8215,47.3881],[-117.8214,47.3596],[-117.8212,47.3471],[-117.8228,47.2598],[-117.961,47.2606],[-118.0865,47.2596],[-118.5946,47.2612],[-118.9805,47.2613],[-118.9792,47.4029],[-118.9756,47.7023],[-118.9756,47.7299],[-118.9763,47.787],[-118.9749,47.8731],[-118.9772,47.9201],[-118.9787,47.9408]]]},\"properties\":{\"name\":\"Lincoln\",\"state\":\"WA\"}}]}","volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nichols, Leslie","contributorId":257260,"corporation":false,"usgs":false,"family":"Nichols","given":"Leslie","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":813918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","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":813919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":813920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Graaff, Marie-Anne","contributorId":195121,"corporation":false,"usgs":false,"family":"de Graaff","given":"Marie-Anne","email":"","affiliations":[],"preferred":false,"id":813921,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223227,"text":"70223227 - 2021 - Coflowering invasive plants and a congener have neutral effects on fitness components of a rare endemic plant","interactions":[],"lastModifiedDate":"2021-08-18T12:40:16.346666","indexId":"70223227","displayToPublicDate":"2021-03-20T07:37:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Coflowering invasive plants and a congener have neutral effects on fitness components of a rare endemic plant","docAbstract":"Network analyses rarely include fitness components, such as germination, to tie invasive plants to population-level effects on the natives. We address this limitation in a previously studied network of flower visitors around a suite of native and invasive plants that includes an endemic plant at Badlands National Park, South Dakota, USA. Eriogonum visheri coflowers with two abundant invasive plants, Salsola tragus and Melilotus officinalis, as well as a common congener, E. pauciflorum. Network analyses had suggested strong linkages between E. visheri and S. tragus and E. pauciflorum, with a weaker link to M. officinalis. We measured visitation, pollen deposited on stigmas, achene weight and germination over three field seasons (two for germination) in four populations (two in the final season) of E. visheri and applied in situ pollen treatments to E. visheri, adding pollen from other flowers on the same plant; flowers on other E. visheri plants; S. tragus, M. officinalis, or E. pauciflorum; open pollination; or excluding pollinators. Insect visitation to E. visheri was not affected by floral abundance of any of the focal species. Most visitors were halictid bees; one of these (Lasioglossum packeri) was the only identified species to visit E. visheri all three years. Ninety-seven percent of pollen on collected E. visheri stigmas was conspecific, but 22% of flowers had >1 grain of E. pauciflorum pollen on stigmas and 7% had >1 grain of S. tragus pollen; <1% of flowers had M. officinalis pollen on stigmas. None of the pollen treatments produced significant differences in weight or germination of E. visheri achenes. We conclude that, in contrast to the results of the network analysis, neither of the invasive species poses a threat, via heterospecific pollen deposition, to pollination of the endemic E. visheri, and that its congener provides alternative pollen resources to its pollinators.
Although sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes have been studied intensively for more than 70 years, many questions remain about their complex life cycle. One of the least understood portions is the post-metamorphic stage (hereafter pre-feeding juvenile, PFJ) that occurs prior to parasitic feeding. PFJ must emerge from stream sediments and migrate downstream into nearshore feeding areas. Key uncertainties include the internal and exogenous triggers that regulate the timing and duration of the migration, and the mechanisms the animal uses to navigate, avoid predators, and locate their first host. However, many of these factors may vary predictably among natal streams in response to stable geomorphological and hydraulic characteristics that regulate the timing of movements (e.g., flood phenology), energetic costs (e.g., stream length), and risk (e.g., predator density). An improved understanding of the PFJ stage presents two opportunities to improve the success of sea lamprey control: (1) identification of streams where natural mortality during the PFJ stage is high, allowing for the reallocation of larval control to streams more likely to produce successful parasites, and (2) removal or killing of PFJs in streams where natural mortality is low. Either approach represents an opportunity to limit parasitic damage to valuable fish stocks. Here, we review the state of knowledge of the PFJ stage and identify critical knowledge gaps that, if addressed, could facilitate sea lamprey assessment and control by exploiting the behavior of PFJ as they outmigrate from streams in search of their first meal.
Shallow production of sweet high-gravity oil from the Upper Devonian Berea Sandstone in northeastern Kentucky has caused the region to become the leading oil producer in the state. Potential nearby source rocks, namely, the overlying Mississippian Sunbury Shale and underlying Ohio Shale, are immature for commercial oil generation according to vitrinite reflectance and programmed pyrolysis analyses. We used organic geochemical measurements from Berea oils and solvent extracts from potential Upper Devonian–Mississippian source rocks to better understand organic matter sources, oil–oil and oil–source rock correlations, and thermal maturity in the shallow Berea oil play. Multiple geochemical proxies suggest Berea oils are from one family and from similar source rocks. Oils and organic matter in the potential source rocks are from a marine source based on pristane-to-phytane (Pr/Ph) and terrestrial-to-aquatic ratios, carbon preference index values, n-alkane maxima, C-isotopic composition, and tricyclic terpane and hopane ratios. Any or all of the Devonian to Mississippian black shale source rocks could be potential source rocks for Berea oils based on similarities in oil and solvent extract Pr/n-C17 and Ph/n-C18 ratios, sterane distributions, C-isotopic values, and sterane/hopane and tricyclic terpane ratios. Multiple biomarker ratios suggest Berea oils formed at thermal maturities of approximately 0.7% –0.9% vitrinite reflectance. These data require significant updip lateral migration of 30–50 mi from a downdip Devonian black shale source kitchen to emplace low-sulfur oils in the shallow updip oil-play area and indicate that immature source rocks nearby to Berea oil production are not contributing to produced hydrocarbons.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08192019077","usgsCitation":"Hackley, P.C., Parris, T., Eble, C.F., Greb, S.F., and Harris, D., 2021, Oil–source correlation studies in the shallow Berea Sandstone petroleum system, eastern Kentucky: American Association of Petroleum Geologists Bulletin, v. 105, no. 3, p. 517-542, https://doi.org/10.1306/08192019077.","productDescription":"26 p.","startPage":"517","endPage":"542","ipdsId":"IP-098811","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Northeast Kentucky","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 38.74551518488265\n ],\n [\n -83.81469726562499,\n 37.95286091815649\n ],\n [\n -83.001708984375,\n 37.448696585910376\n ],\n [\n -82.2216796875,\n 37.709899354855125\n ],\n [\n -82.562255859375,\n 38.05674222065296\n ],\n [\n -82.562255859375,\n 38.47079371120379\n ],\n [\n -82.90283203125,\n 38.805470223177466\n ],\n [\n -83.177490234375,\n 38.62545397209084\n ],\n [\n -83.671875,\n 38.74551518488265\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":812510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parris, T.M.","contributorId":255535,"corporation":false,"usgs":false,"family":"Parris","given":"T.M.","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eble, C. F.","contributorId":255536,"corporation":false,"usgs":false,"family":"Eble","given":"C.","email":"","middleInitial":"F.","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greb, S. F.","contributorId":255538,"corporation":false,"usgs":false,"family":"Greb","given":"S.","email":"","middleInitial":"F.","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, D.C.","contributorId":255540,"corporation":false,"usgs":false,"family":"Harris","given":"D.C.","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":812514,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219046,"text":"70219046 - 2021 - Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast","interactions":[],"lastModifiedDate":"2021-05-13T15:51:18.012368","indexId":"70219046","displayToPublicDate":"2021-03-18T08:23:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast","docAbstract":"Numerous glacial refugia have been hypothesized along North America's North Pacific Coast that may have increased divergence of refugial taxa, leading to elevated endemism and subsequently clustered hybrid zones following deglaciation. The locations and community composition of these ice‐free areas remains controversial, but whole‐genome sequences now enable detailed analysis of the demographic and evolutionary histories of refugial taxa. Here, we use genomic data to test spatial and temporal processes of diversification among martens with respect to the Coastal Refugium Hypothesis, to understand the role of climate cycling in shaping diversity across complex landscapes.
North America and North Pacific Coast archipelagos.
North American martens (Martes).
Short‐read whole‐genome resequencing data were generated for 11 martens: four M. americana, four M. caurina, two hybrids, and one outgroup (Martes zibellina). Sampling was representative of known genetic clades within New World martens, including sampling within insular and continental hybrid zones and along the North Pacific Coast (five island populations). ADMIXTURE, F‐statistics, and D‐statistics (ABBA‐BABA) were used to identify introgression and infer directionality. Heterozygosity densities, estimated via PSMC, were used to characterize historical demography at and below the species level to infer refugial and colonization processes.
Forest‐associated Pacific martens (M. caurina) are divided into distinct insular and continental clades consistent with the Coastal Refugium Hypothesis. There was no evidence of introgression on islands that received historical translocations of American pine martens (M. americana), but introgression was detected in two active zones of secondary contact: one insular and one continental. Only early‐generational hybrids were identified across multiple hybrid zones, a pattern consistent with potential genetic swamping of M. caurina by M. americana.
Despite an incomplete fossil record, genomic evidence supports the persistence of forest‐associated martens, likely the insular Pacific marten lineage, along the western edges of the Alexander Archipelago during the Last Glacial Maximum. This discovery informs our understanding of refugial paleoenvironments, critical to interpreting refugial timing, duration, and community composition. Genomic reevaluations of other taxa along North America's North Pacific Coast may yield new and deeper perspectives on the history of refugial forest communities and the role of dynamic climate shifts in shaping high‐latitude diversity across complex insular landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14068","usgsCitation":"Colella, J.P., Lan, T., Talbot, S.L., Lindqvist, C., and Cook, J.A., 2021, Whole‐genome resequencing reveals persistence of forest‐associated mammals in Late Pleistocene refugia along North America’s North Pacific Coast: Journal of Biogeography, v. 48, no. 5, p. 1153-1169, https://doi.org/10.1111/jbi.14068.","productDescription":"17 p.","startPage":"1153","endPage":"1169","ipdsId":"IP-117301","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia, California, Oregon, Washington, Yukon","otherGeospatial":"North Pacific Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.796875,\n 61.438767493682825\n ],\n [\n -165.58593749999997,\n 53.54030739150022\n ],\n [\n -154.68749999999997,\n 55.3791104480105\n ],\n [\n -147.3046875,\n 58.07787626787517\n ],\n [\n -139.5703125,\n 56.559482483762245\n ],\n [\n -129.375,\n 48.45835188280866\n ],\n [\n -127.61718749999999,\n 38.272688535980976\n ],\n [\n -121.640625,\n 38.54816542304656\n ],\n [\n -120.9375,\n 47.754097979680026\n ],\n [\n -128.671875,\n 58.07787626787517\n ],\n [\n -138.515625,\n 62.75472592723178\n ],\n [\n -156.796875,\n 61.438767493682825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Colella, Jocelyn P.","contributorId":190332,"corporation":false,"usgs":false,"family":"Colella","given":"Jocelyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":812554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lan, Tianying","contributorId":207037,"corporation":false,"usgs":false,"family":"Lan","given":"Tianying","email":"","affiliations":[{"id":37434,"text":"Department of Biological Scineces, State University of New York at Buffalo","active":true,"usgs":false}],"preferred":false,"id":812555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","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":812556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindqvist, Charlotte","contributorId":207038,"corporation":false,"usgs":false,"family":"Lindqvist","given":"Charlotte","email":"","affiliations":[{"id":37434,"text":"Department of Biological Scineces, State University of New York at Buffalo","active":true,"usgs":false}],"preferred":false,"id":812557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Joseph A.","contributorId":8323,"corporation":false,"usgs":false,"family":"Cook","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":812558,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241892,"text":"70241892 - 2021 - A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","interactions":[],"lastModifiedDate":"2023-03-30T12:08:50.610487","indexId":"70241892","displayToPublicDate":"2021-03-18T07:06:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","docAbstract":"Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.
The 2018 eruption on the lower East Rift Zone of Kīlauea Volcano produced one of the largest and most destructive lava flows in Hawai’i during the past 200 years. Over the course of more than 3 months, twenty-four fissures erupted, and the rate of lava effusion varied by two orders of magnitude, with significant implications for evolving flow behavior and hazards. Syn-eruptive data were collected to quantify these changes in lava effusion rate, including video of flow through channels and digital elevation models acquired using small unoccupied aircraft systems, airborne lidar, and airborne single-pass interferometric synthetic aperture radar. Topographic data through time allowed calculation of subaerial lava flow volume and time-averaged discharge rate over the course of the eruption, which we integrated with pre- and post-eruption bathymetric surveys. Repeat videos of the near-vent channel were analyzed with particle velocimetry to extract flow velocities, and these were combined with open channel flow theory to calculate a time series of instantaneous effusion rates. Results show a general increase in dense rock equivalent (DRE) effusion rate from ~7 to ~100 m3/s from early to late May for the whole flow field and ≥ 200 m3/s by mid-June after the eruption had focused at a primary vent. By the end of the eruption in August, 0.9–1.4 km3 DRE of lava had erupted, with 0.4 km3 deposited on land and at least 0.5 km3 offshore. The trends in effusion rate through time reflect magmatic processes in the connected summit and rift zone system that controlled eruption rate, with resulting implications for lava flow dynamics and hazards.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-021-01443-6","usgsCitation":"Dietterich, H., Diefenbach, A., Soule, S.A., Zoeller, M.H., Patrick, M.R., Major, J., and Lundgren, P., 2021, Lava effusion rate evolution and erupted volume during the 2018 Kīlauea lower East Rift Zone eruption: Bulletin of Volcanology, v. 83, no. 25, 18 p., https://doi.org/10.1007/s00445-021-01443-6.","productDescription":"18 p.","ipdsId":"IP-122554","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488679,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/gsofacpubs/2493","text":"External Repository"},{"id":384747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano, Hawaii volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.0507354736328,\n 19.321511226817176\n ],\n [\n -155.25054931640625,\n 19.369454073094243\n ],\n [\n -155.35011291503906,\n 19.39082944712291\n ],\n [\n -155.4242706298828,\n 19.204186382298897\n ],\n [\n -155.39749145507812,\n 19.191217165341648\n ],\n [\n -155.12832641601562,\n 19.2748506284423\n ],\n [\n -155.0507354736328,\n 19.321511226817176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"25","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Angela K. 0000-0003-0214-7818","orcid":"https://orcid.org/0000-0003-0214-7818","contributorId":204743,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Angela K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soule, S. Adam 0000-0002-4691-6300","orcid":"https://orcid.org/0000-0002-4691-6300","contributorId":221052,"corporation":false,"usgs":false,"family":"Soule","given":"S.","email":"","middleInitial":"Adam","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":813191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zoeller, Michael H. 0000-0003-4716-8567","orcid":"https://orcid.org/0000-0003-4716-8567","contributorId":214557,"corporation":false,"usgs":true,"family":"Zoeller","given":"Michael","email":"","middleInitial":"H.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Major, J. J. 0000-0003-2449-4466","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":29461,"corporation":false,"usgs":true,"family":"Major","given":"J. J.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":813194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundgren, Paul 0000-0002-6771-2876","orcid":"https://orcid.org/0000-0002-6771-2876","contributorId":215622,"corporation":false,"usgs":false,"family":"Lundgren","given":"Paul","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":813195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219181,"text":"70219181 - 2021 - American Woodcock singing-ground survey: Comparison of four models for trend in population size","interactions":[],"lastModifiedDate":"2021-08-03T13:58:58.411403","indexId":"70219181","displayToPublicDate":"2021-03-16T07:14:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"American Woodcock singing-ground survey: Comparison of four models for trend in population size","docAbstract":"Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-079","usgsCitation":"Sauer, J.R., Link, W., Seamans, M.E., and Rau, R.D., 2021, American Woodcock singing-ground survey: Comparison of four models for trend in population size: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 83-97, https://doi.org/10.3996/JFWM-20-079.","productDescription":"15 p.","startPage":"83","endPage":"97","onlineOnly":"N","ipdsId":"IP-127453","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":453075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-079","text":"Publisher Index Page"},{"id":384754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Eastern and Central United States and Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.412109375,\n 50.17689812200107\n ],\n [\n -95.537109375,\n 51.069016659603896\n ],\n [\n -95.888671875,\n 48.922499263758255\n ],\n [\n -95.00976562499999,\n 43.58039085560784\n ],\n [\n -93.955078125,\n 39.027718840211605\n ],\n [\n -93.515625,\n 30.826780904779774\n ],\n [\n -86.748046875,\n 32.10118973232094\n ],\n [\n -82.6171875,\n 29.99300228455108\n ],\n [\n -77.783203125,\n 34.161818161230386\n ],\n [\n -75.76171875,\n 35.88905007936091\n ],\n [\n -60.29296874999999,\n 45.706179285330855\n ],\n [\n -60.29296874999999,\n 47.100044694025215\n ],\n [\n -67.412109375,\n 50.17689812200107\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William 0000-0002-9913-0256","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":221718,"corporation":false,"usgs":true,"family":"Link","given":"William","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seamans, Mark E","contributorId":256724,"corporation":false,"usgs":false,"family":"Seamans","given":"Mark","email":"","middleInitial":"E","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rau, Rebecca D.","contributorId":256726,"corporation":false,"usgs":false,"family":"Rau","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219207,"text":"70219207 - 2021 - Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","interactions":[],"lastModifiedDate":"2021-05-18T14:07:03.389177","indexId":"70219207","displayToPublicDate":"2021-03-15T13:32:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3042,"text":"Petroleum Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","docAbstract":"Geological carbon sequestration (GCS) is necessary to help meet emissions reduction goals, but groundwater contamination may occur if CO2 and/or brine were to leak out of deep storage formations into the shallow subsurface. For this study, a natural analogue was investigated: in the Virgin River Basin of southwest Utah, water with moderate salinity and high CO2 concentrations is leaking upward into shallow aquifers that contain heavy metal-bearing concretions. The aquifer system is comprised of the Navajo and Kayenta formations, which are pervasive across southern Utah and have been considered as a potential GCS injection unit where they are sufficiently deep. Numerical models of the site were constructed based on measured water chemistry and head distributions from previous studies. Simulations were used to improve understanding of the rate and distribution of the upwelling flow into the aquifers, and to assess the reactive transport processes that may occur if the upwelling fluids were to interact with a zone of iron oxide and other heavy metals, representing the concretions that are common in the area. Various mineralogies were tested, including one in which Pb+2 was adsorbed onto ferrihydrite, and another in which it was bound within a solid mixture of litharge (PbO) and hematite (Fe2O3). Results indicate that metal mobilization depends strongly on the source zone composition and that Pb+2 transport can be naturally attenuated by gas phase formation and carbonate mineral precipitation. These findings could be used to improve risk assessment and mitigation strategies at geological carbon sequestration sites.
","language":"English","publisher":"The Geological Society of London","doi":"10.1144/petgeo2020-109","usgsCitation":"Plampin, M.R., Blondes, M., Sonnenthal, E., and Craddock, W.H., 2021, Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah: Petroleum Geoscience, v. 27, no. 3, petgeo2020-109, 15 p., https://doi.org/10.1144/petgeo2020-109.","productDescription":"petgeo2020-109, 15 p.","ipdsId":"IP-120620","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":384769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"47\",\"properties\":{\"name\":\"Utah\",\"nation\":\"USA 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Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":813215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":813217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229460,"text":"70229460 - 2021 - Migration distance and maternal resource allocation determine timing of birth in a large herbivore","interactions":[],"lastModifiedDate":"2022-03-09T15:38:27.235558","indexId":"70229460","displayToPublicDate":"2021-03-12T09:31:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Migration distance and maternal resource allocation determine timing of birth in a large herbivore","docAbstract":"Birth timing is a key life-history characteristic that influences fitness and population performance. For migratory animals, however, appropriately timing birth on one seasonal range may be constrained by events occurring during other parts of the migratory cycle. We investigated how the use of capital and income resources may facilitate flexibility in reproductive phenology of migratory mule deer in western Wyoming, USA, over a 5-yr period (2015–2019). Specifically, we examined how seasonal interactions affected three interrelated life-history characteristics: fetal development, birth mass, and birth timing. Females in good nutritional condition at the onset of winter and those that migrated short distances had more developed fetuses (measured as fetal eye diameter in March). Variation in parturition date was explained largely by fetal development; however, there were up to 16 d of plasticity in expected birth date. Plasticity in expected birth date was shaped by income resources in the form of exposure to spring green-up. Although individuals that experienced greater exposure to spring green-up were able to advance expected birth date, being born early or late with respect to fetal development had no effect on birth mass of offspring. Furthermore, we investigated the trade-offs migrating mule deer face by evaluating support for existing theory that predicts that births should be matched to local peaks in resource availability at the birth site. In contrast to this prediction, only long-distance migrants that paced migration with the flush of spring green-up, giving birth shortly after ending migration, were able to match birth with spring green-up. Shorter-distance migrants completed migration sooner and gave birth earlier, seemingly trading off more time for offspring to grow and develop over greater access to resources. Thus, movement tactic had profound downstream effects on birth timing. These findings highlight a need to reconsider classical theory on optimal birth timing, which has focused solely on conditions at the birth site.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3334","usgsCitation":"Aikens, E., Dwinnell, S., LaSharr, T., Jakopak, R., Fralick, G., Randall, J., Kaiser, R., Thonhoff, M., Kauffman, M., and Monteith, K., 2021, Migration distance and maternal resource allocation determine timing of birth in a large herbivore: Ecology, v. 102, no. 6, e03334, 12 p., https://doi.org/10.1002/ecy.3334.","productDescription":"e03334, 12 p.","ipdsId":"IP-125404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3334","text":"Publisher Index Page"},{"id":396917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.98388671874999,\n 41.91045347666418\n ],\n [\n -109.6875,\n 41.91045347666418\n ],\n [\n -109.6875,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 43.628123412124616\n ],\n [\n -110.98388671874999,\n 41.91045347666418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":288165,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwinnell, Samantha P.H.","contributorId":288166,"corporation":false,"usgs":false,"family":"Dwinnell","given":"Samantha P.H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaSharr, Tayler N.","contributorId":288167,"corporation":false,"usgs":false,"family":"LaSharr","given":"Tayler N.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jakopak, Rhiannon P.","contributorId":288168,"corporation":false,"usgs":false,"family":"Jakopak","given":"Rhiannon P.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fralick, Gary L.","contributorId":288169,"corporation":false,"usgs":false,"family":"Fralick","given":"Gary L.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Randall, Jill","contributorId":288170,"corporation":false,"usgs":false,"family":"Randall","given":"Jill","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":837546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaiser, Rusty","contributorId":288171,"corporation":false,"usgs":false,"family":"Kaiser","given":"Rusty","affiliations":[{"id":56194,"text":"fs","active":true,"usgs":false}],"preferred":false,"id":837547,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thonhoff, Mark","contributorId":288174,"corporation":false,"usgs":false,"family":"Thonhoff","given":"Mark","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":837548,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837540,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monteith, Kevin L.","contributorId":288177,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":837549,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70219433,"text":"70219433 - 2021 - Eastward expansion of Round Goby in New York: Assessment of detection methods and current range","interactions":[],"lastModifiedDate":"2021-04-06T12:01:57.537653","indexId":"70219433","displayToPublicDate":"2021-03-12T06:55:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Eastward expansion of Round Goby in New York: Assessment of detection methods and current range","docAbstract":"The Round Goby Neogobius melanostomus has spread rapidly around the Great Lakes region since its introduction to North America in 1990. In 2014, a specimen was captured in the New York State Canal System west of Utica, prompting concerns that Round Goby would soon reach the ecologically and economically valuable watersheds of Lake Champlain and the Hudson River estuary. The establishment of Round Goby populations elsewhere has been linked to a number of negative ecological consequences, yet methods for monitoring the invasion front of this species remain limited. The objectives of this study were to assess the current distribution of Round Goby in central New York and to determine the most effective methods for monitoring the invasion front. This was achieved by concurrently using benthic trawling, seining, minnow traps, and environmental DNA (eDNA) twice annually from 2016 to 2019 at 12 sites on the canal system between Oneida Lake and the Hudson River. Of the three traditional gear types, benthic trawling was the most effective method and captured Round Goby as far east as Utica by 2019. This finding suggests only minimal eastward expansion of Round Goby occurred between 2014 and 2019. Round Goby DNA was detected in water samples during all surveys in which individuals were captured with trawling, and the estimated concentration of DNA explained 69% of the variability in trawl catch. At multiple study sites, Round Goby DNA was identified during consecutive surveys before Round Goby were first captured with trawling. This suggests that in lotic waters, eDNA has the potential to forecast or serve as a sentinel for the expansion of Round Goby to new locations. Our results demonstrate the importance of using eDNA in a repeated sampling framework and supplementing eDNA sampling with some level of effort with traditional sampling methods.
Hawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.
Improved analytical methods can quantify hundreds of pesticide transformation products (TPs), but understanding of TP occurrence and potential toxicity in aquatic ecosystems remains limited. We quantified 108 parent pesticides and 116 TPs in more than 3 700 samples from 442 small streams in mostly urban basins across five major regions of the United States. TPs were detected nearly as frequently as parents (90 and 95% of streams, respectively); 102 TPs were detected at least once and 28 were detected in >20% samples in at least one region—TPs of 9 herbicides, 2 fungicides (chlorothalonil and thiophanate-methyl), and 1 insecticide (fipronil) were the most frequently detected. TPs occurred commonly during baseflow conditions, indicating chronic environmental TP exposures to aquatic organisms and the likely importance of groundwater as a TP source. Hazard quotients based on acute aquatic-life benchmarks for invertebrates and nonvascular plants and vertebrate-centric molecular endpoints (sublethal effects) quantify the range of the potential contribution of TPs to environmental risk and highlight several TP exposure–response data gaps. A precautionary approach using equimolar substitution of parent benchmarks or endpoints for missing TP benchmarks indicates that potential aquatic effects of pesticide TPs could be underestimated by an order of magnitude or more.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06625","usgsCitation":"Mahler, B., Nowell, L.H., Sandstrom, M.W., Bradley, P., Romanok, K., Konrad, C., and Van Metre, P., 2021, Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams: Environmental Science and Technology, v. 55, no. 8, p. 4740-4752, https://doi.org/10.1021/acs.est.0c06625.","productDescription":"13 p.","startPage":"4740","endPage":"4752","ipdsId":"IP-122426","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science 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Center","active":true,"usgs":true}],"preferred":true,"id":812673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":812674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Konrad, Christopher 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":220231,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","affiliations":[{"id":622,"text":"Washington Water Science 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Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom","docAbstract":"Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.
In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028076","usgsCitation":"Formetta, G., Over, T.M., and Stewart, E., 2021, Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom: Water Resources Research, v. 57, no. 4, e2020WR028076, 21 p., https://doi.org/10.1029/2020WR028076.","productDescription":"e2020WR028076, 21 p.","ipdsId":"IP-119682","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/529960/1/N529960PP.pdf","text":"External Repository"},{"id":384966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -5.712890625,\n 49.61070993807422\n ],\n [\n -2.28515625,\n 50.064191736659104\n ],\n [\n 1.669921875,\n 50.84757295365389\n ],\n [\n 2.3291015625,\n 52.32191088594773\n ],\n [\n 0.9228515625,\n 54.826007999094955\n ],\n [\n -0.2197265625,\n 55.85064987433714\n ],\n [\n -0.791015625,\n 57.231502991478926\n ],\n [\n -1.142578125,\n 57.938183012205315\n ],\n [\n -2.548828125,\n 58.63121664342478\n ],\n [\n -4.130859375,\n 59.153403092050375\n ],\n [\n -6.767578125,\n 58.97266715450153\n ],\n [\n -8.1298828125,\n 56.24334992410525\n ],\n [\n -7.9541015625,\n 54.521081495443596\n ],\n [\n -7.0751953125,\n 54.059387886623576\n ],\n [\n -5.888671875,\n 53.409531853086435\n ],\n [\n -5.6689453125,\n 51.56341232867588\n ],\n [\n -6.1083984375,\n 50.233151832472245\n ],\n [\n -6.064453125,\n 49.55372551347579\n ],\n [\n -5.712890625,\n 49.61070993807422\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Formetta, Giuseppe 0000-0002-0252-1462","orcid":"https://orcid.org/0000-0002-0252-1462","contributorId":210296,"corporation":false,"usgs":false,"family":"Formetta","given":"Giuseppe","email":"","affiliations":[{"id":38100,"text":"Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":813730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Elizabeth","contributorId":257050,"corporation":false,"usgs":false,"family":"Stewart","given":"Elizabeth","email":"","affiliations":[{"id":51971,"text":"UK Centre for Ecology & Hydrology","active":true,"usgs":false}],"preferred":false,"id":813732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221894,"text":"70221894 - 2021 - Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","interactions":[],"lastModifiedDate":"2021-07-13T18:35:29.258188","indexId":"70221894","displayToPublicDate":"2021-03-05T13:30:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3892,"text":"Ecohydrology & Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","docAbstract":"Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000).
In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 (Huntington and Aiken, 2013).
Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecohyd.2021.02.005","usgsCitation":"Rouhani, S., Schaaf, C.B., Huntington, T., and Choate, J., 2021, Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine: Ecohydrology & Hydrobiology, v. 21, no. 23-24, p. 256-270, https://doi.org/10.1016/j.ecohyd.2021.02.005.","productDescription":"15 p.","startPage":"256","endPage":"270","ipdsId":"IP-106391","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecohyd.2021.02.005","text":"Publisher Index Page"},{"id":387156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.73046875,\n 44.48866833139464\n ],\n [\n -67.576904296875,\n 45.57560020947802\n ],\n [\n -68.5986328125,\n 46.255846818480315\n ],\n [\n -70.15869140625,\n 46.430285240839964\n ],\n [\n -70.37841796875,\n 45.78284835197676\n ],\n [\n -69.43359375,\n 45.874712248904764\n ],\n [\n -69.60937499999999,\n 45.36758436884978\n ],\n [\n -70.11474609375,\n 45.213003555993964\n ],\n [\n -69.345703125,\n 44.6061127451739\n ],\n [\n -68.73046875,\n 44.48866833139464\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"23-24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rouhani, Shabnam","contributorId":260994,"corporation":false,"usgs":false,"family":"Rouhani","given":"Shabnam","email":"","affiliations":[{"id":52735,"text":"University of Massachusetts, Boston, MA","active":true,"usgs":false}],"preferred":false,"id":819233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Janet","contributorId":260995,"corporation":false,"usgs":false,"family":"Choate","given":"Janet","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":819236,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218813,"text":"70218813 - 2021 - The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","interactions":[],"lastModifiedDate":"2021-03-15T13:59:10.529639","indexId":"70218813","displayToPublicDate":"2021-03-05T07:53:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","docAbstract":"The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.