Genome-wide evaluations of genetic diversity and population structure are important for informing management and conservation of trailing-edge populations. North American moose (Alces alces) are declining along portions of the southern edge of their range due to disease, species interactions, and marginal habitat, all of which may be exacerbated by climate change. We employed a genotyping by sequencing (GBS) approach in an effort to collect baseline information on the genetic variation of moose inhabiting the species’ southern range periphery in the contiguous United States. We identified 1920 single nucleotide polymorphisms (SNPs) from 155 moose representing three subspecies from five states: A. a. americana (New Hampshire), A. a. andersoni (Minnesota), and A. a. shirasi (Idaho, Montana, and Wyoming). Molecular analyses supported three geographically isolated clusters, congruent with currently recognized subspecies. Additionally, while moderately low genetic diversity was observed, there was little evidence of inbreeding. Results also indicated > 20% shared ancestry proportions between A. a. shirasi samples from northern Montana and A. a. andersoni samples from Minnesota, indicating a putative hybrid zone warranting further investigation. GBS has proven to be a simple and effective method for genome-wide SNP discovery in moose and provides robust data for informing herd management and conservation priorities. With increasing disease, predation, and climate related pressure on range edge moose populations in the United States, the use of SNP data to identify gene flow between subspecies may prove a powerful tool for moose management and recovery, particularly if hybrid moose are more able to adapt.
In 2020, the fire season affecting the western United States reached unprecedented levels. The 116 fires active in September consumed nearly 20,822 km2 (https://inciweb.nwcg.gov/accessible-view/ Accessed 2020-09-29) with eighty percent of this footprint (16,567 km2) from 68 fires occurring within California, Oregon, and Washington. Although the 2020 fire season was the most extreme on record, it exemplified patterns of increased wildfire size, number, timing, return frequency, and extent which are linked to climate-driven changes in precipitation and temperature affecting fire ignition and severity (Westerling 2016, Goss et al. 2020, Weber and Yadav 2020).
Rediscovery of living populations of a species that was presumed to be extirpated can generate new narratives for conservation in areas suffering from losses in biodiversity. We used field observations and DNA sequence data to verify the rediscovery of the Critically Endangered scincid lizard Emoia slevini on Dåno′, an islet off the coast of Guam in the southern Mariana Islands, where for > 20 years it had been considered possibly extirpated. Endemic to the Marianas, E. slevini has declined throughout its range and no longer occurs on as many as five islands from which it was historically known, most likely because of interactions with invasive species and loss of native forest. Our results show that individuals from Dåno′, the type locality for E. slevini, are genetically similar but not identical to E. slevini on Sarigan and Alamagan to the north, and that E. slevini is a close evolutionary relative to another congener in the southern Marianas that is currently recognized as Emoia atrocostata but probably represents an undescribed species in this archipelago. We also show that other, more broadly distributed species of Emoia occurring on Dåno′ are distant relatives to E. slevini and the Mariana lineage of E. atrocostata, providing further evidence of the distinctiveness of these taxa. The rediscovery of E. slevini on Dåno′ following rodent eradication and culling of a population of monitor lizards suggests that management of invasive species is key to the recovery of this skink in the Mariana Islands, and that a range eclipse on the larger neighbouring island of Guam best explains why the rediscovery took place at the periphery of the species’ historic range. A Chamorro abstract can be found in the supplementary material.
Pesticides are widely recognized as important biological stressors in streams, especially in heavily developed urban and agricultural areas like the Central California Coast region. We assessed occurrence and potential toxicity of pesticides in small streams in the region using two analytical methods: a broad-spectrum (223 compounds) method in use since 2012 and a newly developed method for 30 additional new-generation fungicides and insecticides. At least one pesticide compound was identified in 83 of the 85 streams sampled. About one-half (48%) of the 253 pesticides measured were detected at least once and 27 were detected in 10% or more of samples. Three of the top 4, and 6 of the top 10 most frequently detected compounds (chlorantraniliprole, dinotefuran, boscalid, thiamethoxam, clothianidin and the fluopicolide degradate 2,6-dichlorobenzamide) were analyzed by the new method. Pesticide mixtures were common, with two or more pesticide compounds detected in 81% of samples and 10 or more in 32% of samples. The pesticide count at a site was relatively consistent over the 6-week study. Four sites with mixed land-use in the lower basin (<5 km from the sampling site) tended to have the highest pesticide counts and the highest concentrations. Potential toxicity (assessed by comparison to benchmarks) to invertebrates was much more common than potential toxicity to fish or plants and was associated with a wide array of insecticides. The common occurrence of new-generation pesticides highlights the need to continuously update analytical methods to keep pace with changing pesticide use for a fuller assessment of pesticide occurrence and effects on the environment.
Dryland ecosystems can be constrained by low soil fertility. Within drylands, the soil nutrient and organic carbon (C) cycling that does occur is often mediated by soil surface communities known as biological soil crusts (biocrusts), which cycle C and nutrients in the top ca. 0–2 cm of soil. However, the degree to which biocrusts are influencing soil fertility and biogeochemical cycling in deeper, subsurface mineral soils is unclear. The movement of dissolved resources from biocrusts to deeper soil layers in leachate may be one of the main mechanisms through which biocrust fertility is transferred downward towards deeper microbial communities and plant roots occurring within mineral soil. Here we examined the role of biocrust leachate in contributing to subsurface nutrient and soluble C pools and subsurface microbial cycling. We collected biocrusts from three biocrust successional stages and explored resource pools in situ at multiple soil depths, while collecting leachate and measuring nutrient and organic C concentrations and metabolite composition from each successional stage in the laboratory. After four leachate collections, we conducted an incubation of mineral soil collected from below each biocrust successional stage to measure heterotrophic microbial CO2 flux and biomass. Overall, our findings observed that the degree of nutrient and C connectivity between biocrusts and the sub-crust mineral soil depended on the biocrust successional stage and the element being considered, and the influence of biocrust successional stage on mineral soil CO2 flux is likely related to long-term resource build up. Together, our results suggest that the influence of biocrust leachate on subsurface mineral soil is complex and context dependent, but, over longer time periods and at later successional stages, can have measurable effects on dryland soil biogeochemical cycling with feedbacks to resource availability and CO2 flux.
We use the Third Uniform California Earthquake Rupture Forecast (UCERF3) epidemic‐type aftershock sequence (ETAS) model (UCERF3‐ETAS) to evaluate the effects of declustering and Poisson assumptions on seismic hazard estimates. Although declustering is necessary to infer the long‐term spatial distribution of earthquake rates, the question is whether it is also necessary to honor the Poisson assumption in classic probabilistic seismic hazard assessment. We use 500,000 yr, M ≥ 2.5 synthetic catalogs to address this question, for which UCERF3‐ETAS exhibits realistic spatiotemporal clustering effects (e.g., aftershocks). We find that Gardner and Knopoff (1974) declustering, used in the U.S. Geological Survey seismic hazard models, lowers 2% in 50 yr and risk‐targeted ground‐motion hazard metrics by about 4% on average (compared with the full time‐dependent [TD] model), with the reduction being 5% at 40% in 50 yr ground motions. Keeping all earthquakes and treating them as a Poisson process increases these same hazard metrics by about 3%–12%, on average, due to the removal of relatively quiet time periods in the full TD model. In the interest of model simplification, bias minimization, and consideration of the probabilities of multiple exceedances, we agree with others (Marzocchi and Taroni, 2014) that we are better off keeping aftershocks and treating them as a Poisson process rather than removing them from hazard consideration via declustering. Honoring the true time dependence, however, will likely be important for other hazard and risk metrics, and this study further exemplifies how this can now be evaluated more extensively.
We derive approximate expressions for the ellipticity (i.e. horizontal-to-vertical or vertical-to-horizontal ratio) of Rayleigh waves propagating in a layered medium. The approximation is based on the generalized energy equation for Rayleigh waves, which has been used previously to obtain perturbational results for ellipticity. For a medium with weakly heterogeneous layers, we obtain an approximation from the perturbational result by taking the background medium to be homogeneous. The generalized energy equation also requires an auxiliary function and we discuss how the various possible functions are related to the homogeneous Rayleigh-wave eigenfunction. The analysis reveals that, within the weak approximation, the product of ellipticity and squared phase velocity is linearly related to squared shear wave velocity in the subsurface. We show the accuracy of the approximation with a simple layer-over-half-space model and then demonstrate its utility in a linear inversion scheme for shear wave velocity.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab395","usgsCitation":"Haney, M.M., and Tsai, V.C., 2022, Rayleigh-wave ellipticity in weakly heterogeneous layered media: Geophysical Journal International, v. 228, no. 2, p. 1313-1323, https://doi.org/10.1093/gji/ggab395.","productDescription":"11 p.","startPage":"1313","endPage":"1323","ipdsId":"IP-130168","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab395","text":"Publisher Index Page"},{"id":406672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":851700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsai, Victor C. 0000-0003-1809-6672","orcid":"https://orcid.org/0000-0003-1809-6672","contributorId":199684,"corporation":false,"usgs":false,"family":"Tsai","given":"Victor","email":"","middleInitial":"C.","affiliations":[{"id":27150,"text":"Seismological Laboratory, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":851701,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227179,"text":"70227179 - 2022 - Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","interactions":[],"lastModifiedDate":"2022-03-28T16:34:32.669545","indexId":"70227179","displayToPublicDate":"2021-10-03T10:21:46","publicationYear":"2022","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}},"displayTitle":"Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","title":"Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains","docAbstract":"Broad-scale patterns of genetic diversity for Brook Trout remain poorly understood across their endemic range in the eastern United States. We characterized variation at 12 microsatellite loci in 22,020 Brook Trout among 836 populations from Georgia, USA to Quebec, Canada to the western Great Lakes region. Within-population diversity was typically lower in the southern Appalachians relative to the mid-Atlantic and northeastern regions. Effective population sizes in the southern Appalachians were often very small, with many estimates less than 30 individuals. The population genetics of Brook Trout in the southern Appalachians are far more complex than a conventionally held simple “northern” versus “southern” dichotomy would suggest. Contemporary population genetic variation was consistent with geographic expansion of Brook Trout from Mississippian, mid-Atlantic, and Acadian glacial refuges, as well as differentiation among drainages within these broader clades. Genetic variation was pronounced among drainages (57.4% of overall variation occurred among Hydrologic Unit Code (HUC)10 or larger units) but was considerable even at fine spatial scales (13% of variation occurred among collections within HUC12 drainage units). Remarkably, 87.2% of individuals were correctly assigned to their collection of origin. While comparisons with fish from existing major hatcheries showed impacts of stocking in some populations, genetic introgression did not overwhelm the signal of broad-scale patterns of population genetic structure. Although our results reveal deep genetic structure in Brook Trout over broad spatial extents, fine-scale population structuring is prevalent across the southern Appalachians. Our findings highlight the distinctiveness and vulnerability of many Brook Trout populations in the southern Appalachian Mountains and have important implications for wild Brook Trout management. To facilitate application of our findings by conservation practitioners, we provide an interactive online visualization tool to allow our results to be explored at management relevant scales.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10337","usgsCitation":"Kazyak, D., Lubinski, B.A., Kulp, M.A., Pregler, K., Whiteley, A.R., Hallerman, E.M., Coombs, J.A., Kanno, Y., Rash, J., Morgan II, R., Habera, J., Henegar, J., Weathers, T., Sell, M.T., Rabern, A., Rankin, D., and King, T., 2022, Population genetics of Brook Trout (Salvelinus fontinalis) in the southern Appalachian Mountains: Transactions of the American Fisheries Society, v. 151, no. 2, p. 127-149, https://doi.org/10.1002/tafs.10337.","productDescription":"23 p.","startPage":"127","endPage":"149","ipdsId":"IP-126747","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449683,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/tafs.10337","text":"External Repository"},{"id":393864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia, West Virginia","otherGeospatial":"southern Appalachian Mountians","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.375732421875,\n 36.30627216957992\n ],\n [\n -79.38720703125,\n 36.30627216957992\n ],\n [\n -79.38720703125,\n 38.66835610151506\n ],\n [\n -81.375732421875,\n 38.66835610151506\n ],\n [\n -81.375732421875,\n 36.30627216957992\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":829942,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pregler, K. C.","contributorId":270744,"corporation":false,"usgs":false,"family":"Pregler","given":"K. C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":829943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whiteley, Andrew R.","contributorId":52072,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":829944,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hallerman, Eric M.","contributorId":202528,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":829945,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Coombs, Jason A.","contributorId":270745,"corporation":false,"usgs":false,"family":"Coombs","given":"Jason","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":829946,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kanno, Y.","contributorId":214290,"corporation":false,"usgs":false,"family":"Kanno","given":"Y.","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":829947,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rash, Jacob","contributorId":202482,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":829948,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Morgan II, Raymond P.","contributorId":261509,"corporation":false,"usgs":false,"family":"Morgan II","given":"Raymond P.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":829949,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Habera, Jim","contributorId":270746,"corporation":false,"usgs":false,"family":"Habera","given":"Jim","affiliations":[{"id":56206,"text":"TWRA","active":true,"usgs":false}],"preferred":false,"id":829950,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Henegar, Jason","contributorId":236865,"corporation":false,"usgs":false,"family":"Henegar","given":"Jason","email":"","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":829951,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weathers, T. Casey","contributorId":270747,"corporation":false,"usgs":false,"family":"Weathers","given":"T. Casey","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":829952,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sell, Matthew T.","contributorId":261510,"corporation":false,"usgs":false,"family":"Sell","given":"Matthew","email":"","middleInitial":"T.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829953,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rabern, Anthony","contributorId":270748,"corporation":false,"usgs":false,"family":"Rabern","given":"Anthony","email":"","affiliations":[{"id":56207,"text":"GA Dept Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829954,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rankin, Dan","contributorId":270749,"corporation":false,"usgs":false,"family":"Rankin","given":"Dan","email":"","affiliations":[{"id":56208,"text":"SC Dept Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829955,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"King, Tim L.","contributorId":236903,"corporation":false,"usgs":false,"family":"King","given":"Tim L.","affiliations":[],"preferred":false,"id":829956,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70229087,"text":"70229087 - 2022 - Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data","interactions":[],"lastModifiedDate":"2022-02-28T14:44:11.931261","indexId":"70229087","displayToPublicDate":"2021-10-03T08:37:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data","docAbstract":"Submerged aquatic vegetation (SAV) creates highly productive habitats in coastal areas, providing support for many important species of fish and wildlife. Despite the importance and documented loss of SAV across fresh to marine habitats globally, we lack consistent baseline data on estuarine SAV resources, particularly in the northern Gulf of Mexico (NGOM) estuaries. To understand SAV distribution in the NGOM, SAV biomass and species identity were collected at 384 sites inter-annually (June–September; 2013–2015) from Mobile Bay, Alabama, to San Antonio Bay, Texas, USA. Coastwide, SAV distribution and biomass were consistent across years, covering an estimated 87,000 ha, and supporting approximately 16 ± 1% total cover with an average biomass of 24.5 ± 1.9 g m−2. Differences in hydrology (i.e., precipitation, freshwater input, water depth) and exposure (i.e., wave and wind energy) manifested in unique SAV assemblages and biomass distributions across the region (i.e., Coastal Mississippi-Alabama, Mississippi River Coastal Wetlands, Chenier Plain, Texas Mid-Coast) and estuarine gradient (i.e., marsh zones defined as fresh, intermediate, brackish, saline). Descriptive cluster analyses identified indicator SAV species, known as medoid observations that represented combined salinity, turbidity, and depth conditions unique to different region and marsh zone combinations. While the presence of SAV is often used as an indicator of ecological health, identifying a medoid-based SAV indicator species in aquatic habitats can be used to describe estuarine conditions in more detail and develop aquatic habitat zones. Exploration and the use of this type of field data could be developed as a means to track, manage, and define aquatic habitats across regional and estuarine gradients and further develop ecosystem-based assessment and restoration activities. Identifying aquatic zones through a representative medoid associates SAV species with locations defined by both long-term salinity and salinity variability, water depth, and exposure, which is a powerful potential tool for managers and restoration decision-makers.
","language":"English","publisher":"Springer Link","doi":"10.1007/s12237-021-00958-7","usgsCitation":"DeMarco, K., Hillmann, E., Nyman, J.A., Couvillion, B., and La Peyre, M., 2022, Defining aquatic habitat zones across northern Gulf of Mexico estuarine gradients through submerged aquatic vegetation species assemblage and biomass data: Estuaries and Coasts, v. 45, p. 148-167, https://doi.org/10.1007/s12237-021-00958-7.","productDescription":"20 p.","startPage":"148","endPage":"167","ipdsId":"IP-121908","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":500008,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/598","text":"External Repository"},{"id":396544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.822265625,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 26.60817437403311\n ],\n [\n -87.47314453125,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 31.034108344903512\n ],\n [\n -97.822265625,\n 26.60817437403311\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2021-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"DeMarco, K. E.","contributorId":287038,"corporation":false,"usgs":false,"family":"DeMarco","given":"K. E.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hillmann, E. R.","contributorId":287039,"corporation":false,"usgs":false,"family":"Hillmann","given":"E. R.","affiliations":[{"id":28058,"text":"Southeastern Louisiana University","active":true,"usgs":false}],"preferred":false,"id":836447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyman, J. A.","contributorId":275213,"corporation":false,"usgs":false,"family":"Nyman","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":836449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":836448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836450,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226957,"text":"70226957 - 2022 - Estimating urban air pollution contribution to South Platte River nitrogen loads with National Atmospheric Deposition Program data and SPARROW model","interactions":[],"lastModifiedDate":"2021-12-22T13:00:51.139878","indexId":"70226957","displayToPublicDate":"2021-10-01T06:57:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating urban air pollution contribution to South Platte River nitrogen loads with National Atmospheric Deposition Program data and SPARROW model","docAbstract":"Air pollution is commonly disregarded as a source of nutrient loading to impaired surface waters managed under the Clean Water Act per states’ 303(d) list programs. The contribution of air pollution to 2017–2018 South Platte River nitrogen (N) loads was estimated from the headwaters to the gage at Weldona, Colorado, USA (100 km downstream of Denver), using data from the National Atmospheric Deposition Program (NADP) and the SPAtially Referenced Regressions On Watershed attributes (SPARROW) model. The NADP offers wet-deposition raster created by spatial interpolation of data collected from regionally representative monitoring sites, excluding the influences from urban site data. For this study, NADP wet-deposition data obtained from sites within the Denver-Boulder, Colorado, urban corridor were included and excluded in new spatial interpolations of wet-deposition raster, which were used as input for SPARROW to model the influence of urban air pollution sources on South Platte River loads. Because urban air pollution is already incorporated into the NADP Total Deposition modeling methodology, dry N deposition was held constant for each SPARROW modeling scenario when dry deposition was included. By including the urban wet-deposition data in the model, estimated N loading to the South Platte River at Denver increased by 9–11 percent. Factoring in dry deposition at a 1:1.8 dry:wet ratio obtained from the results, urban air pollution was estimated to contribute as much as 20 percent of the nitrate Total Maximum Daily Load for Segment 14 of the South Platte River.
Invasive Lake Trout Salvelinus namaycush have altered the once-pristine Yellowstone Lake ecosystem through top-down effects by consuming Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri. To conserve Yellowstone Cutthroat Trout and restore the ecosystem, a Lake Trout gillnetting program was implemented to suppress the invasive population. We evaluated the spatial structure of Lake Trout in Yellowstone Lake with the intent of increasing suppression efficiency. Specifically, we addressed questions related to adult Lake Trout aggregation and movement during summer and autumn (spawning) periods and how Lake Trout used locations in the context of suppression efforts. We tracked 373 Lake Trout (>500 mm TL) during the summer and autumn of 2016 and 2017. Based on kernel density estimates, Lake Trout were highly aggregated at 9 locations during summer and 22 locations during the spawning period. Using a novel metric, individual days (product of mean individuals per survey and mean length of stay), five summer locations and five spawning locations had at least 30 individual days. These locations are suggested as priority areas for targeting Lake Trout suppression. Lake Trout were less aggregated and moved less during the summer, making them less vulnerable to a passive gear in the summer than during the autumn spawning period. Lake Trout exhibited low spawning site fidelity compared to populations elsewhere, possibly due to decades of intensive gill netting at spawning locations. Given the aggregation and movement patterns observed in Yellowstone Lake, continuing to target adult Lake Trout during the spawning period is the most cost-effective approach to Lake Trout suppression.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10712","usgsCitation":"Williams, J.R., Guy, C.S., Bigelow, P.E., and Koel, T., 2022, Quantifying the spatial structure of invasive lake trout in Yellowstone Lake to improve suppression efficacy: North American Journal of Fisheries Management, v. 42, no. 1, p. 50-62, https://doi.org/10.1002/nafm.10712.","productDescription":"13 p.","startPage":"50","endPage":"62","ipdsId":"IP-127835","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449687,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.montana.edu/xmlui/handle/1/15150","text":"External Repository"},{"id":397259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58700561523436,\n 44.27765451038982\n ],\n [\n -110.159912109375,\n 44.27765451038982\n ],\n [\n -110.159912109375,\n 44.581664700316146\n ],\n [\n -110.58700561523436,\n 44.581664700316146\n ],\n [\n -110.58700561523436,\n 44.27765451038982\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Jacob R.","contributorId":288679,"corporation":false,"usgs":false,"family":"Williams","given":"Jacob","email":"","middleInitial":"R.","affiliations":[{"id":61825,"text":"Montana Fish","active":true,"usgs":false}],"preferred":false,"id":838215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":838214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bigelow, Patricia E.","contributorId":288680,"corporation":false,"usgs":false,"family":"Bigelow","given":"Patricia","email":"","middleInitial":"E.","affiliations":[{"id":36976,"text":"U.S. National Park Service","active":true,"usgs":false}],"preferred":false,"id":838216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koel, Todd M.","contributorId":288681,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838217,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225571,"text":"70225571 - 2022 - Sampling design workflows and tools to support adaptive monitoring and management","interactions":[],"lastModifiedDate":"2022-03-15T16:05:06.528066","indexId":"70225571","displayToPublicDate":"2021-09-30T05:43:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Sampling design workflows and tools to support adaptive monitoring and management","docAbstract":"On the Ground
• Adaptive land management requires monitoring of resource conditions, which requires choices about where and when to monitor a landscape.
• Designing a sampling design for a monitoring program can be broken down in to eight steps: identifying questions, defining objectives, selecting reporting units, deciding data collection methods, defining the sample frame, selecting an appropriate design type, deciding stratification and allocation, and identifying the required sampling effort.
• Here, we provide descriptions of each step in the process and identify tools and resources to complete each step.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.08.005","usgsCitation":"Stauffer, N.G., Duniway, M.C., Karl, J.W., and Nauman, T.W., 2022, Sampling design workflows and tools to support adaptive monitoring and management: Rangelands, v. 44, no. 1, p. 8-16, https://doi.org/10.1016/j.rala.2021.08.005.","productDescription":"9 p.","startPage":"8","endPage":"16","ipdsId":"IP-125327","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449689,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.08.005","text":"Publisher Index Page"},{"id":390944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stauffer, Nelson G.","contributorId":267942,"corporation":false,"usgs":false,"family":"Stauffer","given":"Nelson","email":"","middleInitial":"G.","affiliations":[{"id":55531,"text":"United States Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":825648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":825649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":825650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":825651,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226171,"text":"70226171 - 2022 - Populations using public-supply groundwater in the conterminous U.S. 2010; Identifying the wells, hydrogeologic regions, and hydrogeologic mapping units","interactions":[],"lastModifiedDate":"2021-11-16T13:07:12.267368","indexId":"70226171","displayToPublicDate":"2021-09-28T07:04:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Populations using public-supply groundwater in the conterminous U.S. 2010; Identifying the wells, hydrogeologic regions, and hydrogeologic mapping units","docAbstract":"Most Americans receive their drinking water from publicly supplied sources, a large portion of it from groundwater. Mapping these populations consistently and at a high resolution is important for understanding where the resource is used and needs to be protected. The results show that 269 million people are supplied by public supply, 107 million are supplied by groundwater and 162 million are supplied by surface water. The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across populated census blocks, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations. In addition, a two-dimensional polygon dataset was created for the conterminous U.S. that identifies 177 unique Hydrogeologic Mapping Units (HMUs) with similar hydrogeologic characteristics. The HMUs do not overlap, but they can delineate areas where stacked hydrogeologic regions (HRs) contribute drinking water from below the surface. HRs are waterbearing geologic regions identified as either a principal aquifers (PA) or secondary hydrogeologic regions (SHR). Within each HMU, the wells were used to determine the proportion of each HR that is providing groundwater to the HMU. In 63% of the HMUs, a single HR is providing water to the public supply wells located within it, while the rest of the HMUs show that the wells are tapping up to a maximum of four stacked HRs. In total, groundwater from 108 HRs provide drinking water for public supply, six of which provide more than 50% of the groundwater used for public supply drinking water. The aquifer serving the largest number of equivalent people (>17 million) is the glacial aquifer. The HR providing the greatest number of people per km2 is the Biscayne aquifer in Florida at nearly 453 people per km2.
We provide detailed rearing methods and describe green sunfish (Lepomis cyanellus) gonadal development and histological differentiation for both sexes. Developing in-depth aquaculture protocols and describing the gonadal differentiation of green sunfish could facilitate strategies to control nuisance populations, enhance stocking programs, and provide information for this species' use in bioassay trials or toxicology studies. Our methods resulted in consistent year-round production of green sunfish and allowed us to identify the timing of their gonadal differentiation through histological assessment. Our spawning methods provided year-round volitional spawns from green sunfish broodstock. Our rearing methods involved weaning larval green sunfish off live nauplii and onto only artificial diets by 37 days post-hatch (dph). Most of the offspring generation reached sexual maturity by 213 dph. Green sunfish are gonochoristic, with testes and ovaries differentiating directly from undifferentiated gonads. Ovaries begin to differentiate by 39 dph and testes begin to differentiate by 69 dph. This information can provide biologists consistent means to produce this Centrachid and understand their gonadal development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquaculture.2021.737515","usgsCitation":"Teal, C., Schill, D., Fogelson, S.B., Roberts, C.M., Fitzsimmons, K., and Bonar, S.A., 2022, Development of aquaculture protocols and gonadal differentiation of green sunfish (Lepomis cyanellus): Aquaculture, v. 547, 737515, 10 p., https://doi.org/10.1016/j.aquaculture.2021.737515.","productDescription":"737515, 10 p.","ipdsId":"IP-130691","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"547","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Teal, Chad N.","contributorId":288576,"corporation":false,"usgs":false,"family":"Teal","given":"Chad N.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":838102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schill, Daniel J.","contributorId":288577,"corporation":false,"usgs":false,"family":"Schill","given":"Daniel J.","affiliations":[{"id":61802,"text":"Fisheries Management Solutions","active":true,"usgs":false}],"preferred":false,"id":838103,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fogelson, Susan B.","contributorId":288578,"corporation":false,"usgs":false,"family":"Fogelson","given":"Susan","email":"","middleInitial":"B.","affiliations":[{"id":61804,"text":"Fishhead Labs","active":true,"usgs":false}],"preferred":false,"id":838104,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, Colby M.","contributorId":288579,"corporation":false,"usgs":false,"family":"Roberts","given":"Colby","email":"","middleInitial":"M.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":838105,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzsimmons, Kevin","contributorId":288580,"corporation":false,"usgs":false,"family":"Fitzsimmons","given":"Kevin","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":838106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838101,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231621,"text":"70231621 - 2022 - Improved fire severity mapping in the North American boreal forest using a hybrid composite method","interactions":[],"lastModifiedDate":"2022-05-18T13:50:20.667432","indexId":"70231621","displayToPublicDate":"2021-09-27T08:56:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Improved fire severity mapping in the North American boreal forest using a hybrid composite method","docAbstract":"Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high-intensity wildfires. Satellite-derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite-inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per-pixel basis within the cloud-computing environment of Google Earth Engine. It constructs the post-fire image from satellite data composited from all valid images (i.e., clear-sky and snow-free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired-scene and composite approaches where the post-fire time period is from the growing season 1 year after fire. Validation statistics based on field-derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost-effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.
","language":"English","publisher":"Zoological Society of London","doi":"10.1002/rse2.238","usgsCitation":"Holsinger, L.M., Parks, S., Saperstein, L., Loehman, R.A., Whitman, E., Barnes, J.L., and Parisien, M., 2022, Improved fire severity mapping in the North American boreal forest using a hybrid composite method: Remote Sensing in Ecology and Conservation, v. 8, no. 2, p. 222-235, https://doi.org/10.1002/rse2.238.","productDescription":"14 p.","startPage":"222","endPage":"235","ipdsId":"IP-129945","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":449694,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.238","text":"Publisher Index Page"},{"id":400697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n 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M.","contributorId":187607,"corporation":false,"usgs":false,"family":"Holsinger","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":843143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parks, Sean","contributorId":205458,"corporation":false,"usgs":false,"family":"Parks","given":"Sean","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":843144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saperstein, Lisa","contributorId":218974,"corporation":false,"usgs":false,"family":"Saperstein","given":"Lisa","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":843145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":false,"id":843146,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Ellen","contributorId":225737,"corporation":false,"usgs":false,"family":"Whitman","given":"Ellen","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":843147,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Jennifer L.","contributorId":167357,"corporation":false,"usgs":false,"family":"Barnes","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":843148,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parisien, Marc-André","contributorId":187428,"corporation":false,"usgs":false,"family":"Parisien","given":"Marc-André","affiliations":[],"preferred":false,"id":843149,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70234324,"text":"70234324 - 2022 - Developing landslide chronologies using landslide-dammed lakes in the Oregon Coast Range","interactions":[],"lastModifiedDate":"2022-08-09T13:09:03.8276","indexId":"70234324","displayToPublicDate":"2021-09-24T07:57:09","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5478,"text":"Geological Society of America Field 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Searching for evidence of potentially coseismic mass wasting is incredibly difficult, particularly when historical observations are limited. Landslide-dammed lakes with submerged “ghost forests” in the Oregon Coast Range present the unique opportunity to establish landslide chronologies with subannual accuracy when dendrochronology is applied. This field guide will visit the unique landslide-dammed Klickitat Lake and explore a drowned ‘ghost forest’ to discuss methods used to establish a prehistoric landslide chronology in western Oregon, USA. After exploring the lake and exposing its geomorphic secrets, the guide will end with a stop on Marys Peak, a mafic volcanic intrusion composed of gabbroic dikes and pillow basalt that forms the highest point in the Oregon Coast Range. With the landscape of western Oregon laid out before us, we will discuss short- and long-term geomorphic evolution of the Oregon Coast Range and Willamette Valley.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"GSA field guide: From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest","largerWorkSubtype":{"id":15,"text":"Monograph"},"conferenceTitle":"GSA Connects 2021","conferenceDate":"2021","conferenceLocation":"Portland, Oregon, United States","language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.0062(01)","usgsCitation":"Wetherell, L., Struble, W., and LaHusen, S.R., 2022, Developing landslide chronologies using landslide-dammed lakes in the Oregon Coast Range, chap. 1 of GSA field guide: From terranes to terrains: Geologic field guides on the construction and destruction of the Pacific Northwest: Geological Society of America Field Guides, v. 62, p. 1-18, https://doi.org/10.1130/2021.0062(01).","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-129899","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":404994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.04663085937499,\n 46.27863122156088\n ],\n [\n -124.09057617187499,\n 45.54483149242463\n ],\n [\n -124.068603515625,\n 45.32897866218559\n ],\n [\n -124.27734374999999,\n 44.24519901522129\n ],\n [\n -124.23339843749999,\n 43.98491011404692\n ],\n [\n -124.46411132812499,\n 43.34116005412307\n ],\n [\n -124.56298828125001,\n 43.068887774169625\n ],\n [\n -124.68383789062499,\n 42.87596410238256\n ],\n [\n -124.51904296875,\n 42.58544425738491\n ],\n [\n -124.486083984375,\n 42.16340342422401\n ],\n [\n -124.26635742187501,\n 42.00032514831621\n ],\n [\n -122.25585937500001,\n 42.00848901572399\n ],\n [\n -122.23388671874999,\n 45.57560020947802\n ],\n [\n -122.58544921875,\n 45.583289756006316\n ],\n [\n -122.684326171875,\n 45.65244828675087\n ],\n [\n -122.73925781250001,\n 45.767522962149876\n ],\n [\n -122.86010742187499,\n 46.07323062540835\n ],\n [\n -123.101806640625,\n 46.20264638061019\n ],\n [\n -123.277587890625,\n 46.172222978455395\n ],\n [\n -123.49731445312499,\n 46.27103747280261\n ],\n [\n -124.04663085937499,\n 46.27863122156088\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Booth, Adam M. 0000-0002-7339-0594","orcid":"https://orcid.org/0000-0002-7339-0594","contributorId":241907,"corporation":false,"usgs":false,"family":"Booth","given":"Adam","email":"","middleInitial":"M.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":848668,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Grunder, Anita L.","contributorId":194549,"corporation":false,"usgs":false,"family":"Grunder","given":"Anita","middleInitial":"L.","affiliations":[],"preferred":false,"id":848669,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Wetherell, Logan 0000-0002-6716-3790","orcid":"https://orcid.org/0000-0002-6716-3790","contributorId":294676,"corporation":false,"usgs":false,"family":"Wetherell","given":"Logan","email":"","affiliations":[{"id":26935,"text":"Central Washington University","active":true,"usgs":false}],"preferred":false,"id":848566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":848567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":848568,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256758,"text":"70256758 - 2022 - Lake sturgeon seasonal movements in regulated and unregulated Missouri River tributaries","interactions":[],"lastModifiedDate":"2024-09-04T16:29:24.600175","indexId":"70256758","displayToPublicDate":"2021-09-23T11:27:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Lake sturgeon seasonal movements in regulated and unregulated Missouri River tributaries","docAbstract":"Spatio-temporal movement patterns of aquatic organisms drive many ecological processes. However, dams block migrations and alter the hydrologic and thermal regimes influencing movement behaviour of freshwater fishes. In North America, many recovering southern Lake Sturgeon populations occur in rivers with hydroelectric dams, but few studies have examined the impact of hydrologic alteration on their seasonal movements. We conducted a 3-year telemetry study of 96 adult and subadult Lake Sturgeon to compare their migratory responses to temperature and hydrology in adjacent regulated and unregulated tributaries of the Missouri River. Many other populations of Lake Sturgeon use tributaries primarily for spring spawning; however, in our study, Lake Sturgeon used Missouri River tributaries during 78% of the year. Differences in river size, hydrologic and thermal regimes in the regulated Osage River may have contributed to the greater year-round residency, later initiation, more frequent directional changes and longer duration of spring migrations compared to the unregulated Gasconade River. Lake Sturgeon made spring upstream migrations at temperatures of 13–19°C and elevated discharges in both rivers. However, Osage River migrants responded less to changes in discharge or temperature during spring migrations, especially those that overwintered at upstream locations. Fall tributary migrations occurred in the Osage River at rising or high discharges but were uncommon in the Gasconade River. Our identification of the influences of abiotic variables on the timing, duration and extent of Lake Sturgeon seasonal migrations can help guide management of habitat and hydrology in regulated rivers to recover migratory fishes globally.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2362","usgsCitation":"Moore, M., Paukert, C.P., Brooke, B., and Moore, T., 2022, Lake sturgeon seasonal movements in regulated and unregulated Missouri River tributaries: Ecohydrology, v. 15, no. 1, e2362, 17 p., https://doi.org/10.1002/eco.2362.","productDescription":"e2362, 17 p.","ipdsId":"IP-130803","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, M.J.","contributorId":341714,"corporation":false,"usgs":false,"family":"Moore","given":"M.J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooke, B.","contributorId":341723,"corporation":false,"usgs":false,"family":"Brooke","given":"B.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, T.","contributorId":257287,"corporation":false,"usgs":false,"family":"Moore","given":"T.","affiliations":[],"preferred":false,"id":908886,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237101,"text":"70237101 - 2022 - Imaging the next Cascadia earthquake: Optimal design for a seafloor GNSS- A network","interactions":[],"lastModifiedDate":"2022-09-29T15:02:37.35773","indexId":"70237101","displayToPublicDate":"2021-09-21T09:57:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Imaging the next Cascadia earthquake: Optimal design for a seafloor GNSS- A network","docAbstract":"The Cascadia subduction zone in the Pacific Northwest of the United States of America capable of producing magnitude ∼9 earthquakes, likely often accompanied by tsunamis. An outstanding question in this region is the degree and spatial extent of interseismic strain accumulation on the subduction megathrust. Seafloor geodetic methods combining GNSS and underwater acoustic ranging (GNSS-A) are capable of imaging this strain accumulation on the offshore portion of the subduction zone and therefore anticipating the potential size and rupture pattern of a future earthquake. However, the high cost of seafloor geodesy means that only a limited number of stations may be deployed and monitored. To facilitate expansion of current geodetic networks offshore, we develop a quantitative recommendation of optimal locations for future seafloor geodetic observations, based on the amount of new information provided by that observation. The optimal network depends on the problem that one is trying to solve with those observations (mapping subduction locking rates, coupling rates, constraining total moment rate, etc.), and on a number of modelling and data uncertainty assumptions. In particular, data uncertainty assumptions will change over time, as more position observations reduce velocity uncertainties. We find that near-trench observations on the megathrust hangingwall, distributed along-strike, consistently provide significant reduction in differential entropy over a large suite of assumptions, and that a well-placed seafloor observation can provide up to ∼30 times the information gain of the most optimal onshore observation.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab360","usgsCitation":"Evans, E., Minson, S.E., and Chadwell, D., 2022, Imaging the next Cascadia earthquake: Optimal design for a seafloor GNSS- A network: Geophysical Journal International, v. 228, no. 2, p. 944-957, https://doi.org/10.1093/gji/ggab360.","productDescription":"14 p.","startPage":"944","endPage":"957","ipdsId":"IP-130354","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132,\n 38.13455657705411\n ],\n [\n -120,\n 38.13455657705411\n ],\n [\n -120,\n 51.508742458803326\n ],\n [\n -132,\n 51.508742458803326\n ],\n [\n -132,\n 38.13455657705411\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Eileen L. 0000-0002-7290-5269","orcid":"https://orcid.org/0000-0002-7290-5269","contributorId":297103,"corporation":false,"usgs":false,"family":"Evans","given":"Eileen L.","affiliations":[{"id":36305,"text":"CSU Northridge","active":true,"usgs":false}],"preferred":false,"id":853343,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853344,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chadwell, David 0000-0002-9741-6656","orcid":"https://orcid.org/0000-0002-9741-6656","contributorId":297105,"corporation":false,"usgs":false,"family":"Chadwell","given":"David","email":"","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":853345,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225169,"text":"70225169 - 2022 - A stable isotope record of late Quaternary hydrologic change in the northwestern Brooks Range, Alaska (eastern Beringia)","interactions":[],"lastModifiedDate":"2023-03-24T17:01:40.985278","indexId":"70225169","displayToPublicDate":"2021-09-21T07:54:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2437,"text":"Journal of Quaternary Science","active":true,"publicationSubtype":{"id":10}},"title":"A stable isotope record of late Quaternary hydrologic change in the northwestern Brooks Range, Alaska (eastern Beringia)","docAbstract":"A submillennial-resolution record of lake water oxygen isotope composition (δ18O) from chironomid head capsules is presented from Burial Lake, northwest Alaska. The record spans the Last Glacial Maximum (LGM; ~20–16k cal a bp) to the present and shows a series of large lake δ18O shifts (~5‰). Relatively low δ18O values occurred during a period covering the LGM, when the lake was a shallow, closed-basin pond. Higher values characterize deglaciation (~16–11.5k cal a bp) when the lake was still closed but lake levels were higher. A rapid decline between ~11 and 10.5k cal a bp indicates that lake levels rose to overflowing. Lake δ18O values are interpreted to reflect the combined effects of changes in lake hydrology, growing season temperature and meteoric source water as well as large-scale environmental changes impacting this site, including opening of the Bering Strait and shifts in atmospheric circulation patterns related to ice-sheet dynamics. The results indicate significant shifts in precipitation minus evaporation across the late Pleistocene to early Holocene transition, which are consistent with temporal patterns of vegetation change and paludification. This study provides new perspectives on the paleohydrology of eastern Beringia concomitant with human migration and major turnover in megafaunal assemblages.
Measurements of the natural radiocarbon content of methane (14C-CH4) dissolved in seawater and freshwater have been used to investigate sources and dynamics of methane. However, during investigations along the Atlantic, Pacific, and Arctic Ocean Margins of the United States, as well as in the North American Great Lakes, some samples revealed highly elevated 14C-CH4 values, as much as 4–5 times above contemporary atmospheric 14C-CH4 levels. Natural production of the 14CH4 isotopologue is too low to cause these observations nor can it explain the variations in location and depth. Numerous lab and field validation tests and blanks, as well as the relatively small number of samples that display these elevated values, all suggest that these signals are not derived from an unknown procedural issue. Here we suggest that the byproducts of nuclear power generation include localized discharges of the 14CH4 isotopologue into marine and aquatic environments, severely altering the measured 14C-CH4 isotopic signals. Since several of our sample sites are distant from on-land nuclear powerplants, we conduct further calculations concluding that the most elevated anomalies in 14C-CH4 likely originate with discharge from nuclear-powered vessels.
Lake sturgeon (Acipenser fulvescens) can migrate long distances to spawn, but many populations currently spawn in systems where the length of accessible riverine migratory habitat has been greatly reduced by dam construction. With the increased prevalence of shortened rivers, focusing on migratory dynamics in short rivers (<30 km) is beneficial to understanding the migratory needs of lake sturgeon populations. Here we document male lake sturgeon movements during the spawning period in the Winooski River, Vermont, USA; a river with only 17 km to the first natural upstream barrier. Male lake sturgeon were acoustically tagged (n = 25, 1215–1470 mm TL) and tracked using five to nine stationary receivers from 2017 to 2019. River discharge, temperature, the lagged effect of temperature (3-day), and time of day were significant factors describing upstream movements of tagged fish. Migrating male lake sturgeon (n = 10 in 2017, n = 18 in 2018, and n = 17 in 2019) displayed general movement patterns during the spawning period that included a single run upstream to the spawning site (60%), upstream and downstream movements throughout the river during the season (20%), or multiple runs made up the entire length of the spawning tributary to the spawning site (20%). No multi-run males were observed during 2018 when discharge was less flashy (i.e., fewer steep increases and declines in discharge) than in 2017 and 2019. These results suggest that the prevalence of multi-run spawning behavior of male lake sturgeon is related to flow conditions.
","language":"English","doi":"10.1016/j.jglr.2021.06.012","collaboration":"Vermont Department of Fish and Wildlife","usgsCitation":"Parrish, D.L., Izzo, L., and Zydlewski, G.B., 2022, Multi-run migratory behavior of adult male lake sturgeon in a short river: Journal of Great Lakes Research, v. 47, no. 5, p. 1400-1409, https://doi.org/10.1016/j.jglr.2021.06.012.","productDescription":"10 p.","startPage":"1400","endPage":"1409","ipdsId":"IP-126224","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Winooski River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.29116821289062,\n 44.395032825316115\n ],\n [\n -72.95951843261719,\n 44.395032825316115\n ],\n [\n -72.95951843261719,\n 44.56014191304745\n ],\n [\n -73.29116821289062,\n 44.56014191304745\n ],\n [\n -73.29116821289062,\n 44.395032825316115\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parrish, Donna L. 0000-0001-9693-6329 dparrish@usgs.gov","orcid":"https://orcid.org/0000-0001-9693-6329","contributorId":138661,"corporation":false,"usgs":true,"family":"Parrish","given":"Donna","email":"dparrish@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":837761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izzo, Lisa K.","contributorId":288330,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":837762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Gayle Barbin","contributorId":288331,"corporation":false,"usgs":false,"family":"Zydlewski","given":"Gayle","email":"","middleInitial":"Barbin","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837763,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224540,"text":"70224540 - 2022 - Targeted and non-targeted analysis of young-of-year smallmouth bass using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry","interactions":[],"lastModifiedDate":"2021-10-06T16:04:22.992337","indexId":"70224540","displayToPublicDate":"2021-09-16T10:03:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Targeted and non-targeted analysis of young-of-year smallmouth bass using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry","docAbstract":"Smallmouth bass in the Susquehanna River Basin, Chesapeake Bay Watershed, USA, have been exhibiting clinical signs of disease and reproductive endocrine disruption (e.g., intersex, male plasma vitellogenin) for over fifteen years. Previous histological and targeted chemical analyses have identified infectious agents and pollutants in fish tissues including organic contaminants, mercury, and perfluorinated compounds, but a common causative link for the observed signs of disease across this widespread area has not been determined. This study examines 146 young-of-year smallmouth bass collected from 14 sampling sites in the Susquehanna River Basin, Pennsylvania, USA with varying levels of disease prevalence. Whole fish were extracted by a recently developed modification to the quick, easy, cheap, effective, rugged, and safe extraction method and analyzed by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. A targeted analysis was conducted to identify the presence and quantity of 127 known contaminants, including polychlorinated biphenyls, brominated diphenyl ethers, organochlorinated pesticides, and pharmaceutical and personal care products. A non-targeted analysis was conducted on the same data set to identify analytes of interest not included on routine target compound lists. Chromatographic alignment through Statistical Compare (ChromaTOF GC) was followed by Fisher ratio and principal component analysis to reduce the data set from thousands of peaks per sample to a final data set of 65 analytes of interest. Comparisons of these 65 compounds between Normal (no observed health anomalies) and Lesioned (observed health anomaly at time of collection) fish revealed increased levels of three chemical families in Lesioned fish including esters, ketones, and nitrogen containing compounds.
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vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":823987,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dorman, Frank L","contributorId":236876,"corporation":false,"usgs":false,"family":"Dorman","given":"Frank","email":"","middleInitial":"L","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":823988,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236513,"text":"70236513 - 2022 - The 6 May 1947 Milwaukee, Wisconsin, earthquake","interactions":[],"lastModifiedDate":"2022-09-09T11:58:51.284302","indexId":"70236513","displayToPublicDate":"2021-09-15T06:56:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The 6 May 1947 Milwaukee, Wisconsin, earthquake","docAbstract":"The State of Wisconsin is not known for earthquake activity. The authoritative public‐facing U.S. Geological Survey Comprehensive Catalog of earthquakes includes only three small (magnitude < 2) earthquakes in the state, all instrumentally recorded. Although other catalogs include more events in Wisconsin, experience has shown that many types of events, such as explosions and cryoseisms, have made their way into earthquake catalogs in this region. In this short report, I summarize available information about an earthquake that was felt in eastern Wisconsin at 15:27 local time on 6 May 1947. As what appears to be the largest historical earthquake in the State of Wisconsin, it is of public interest, its modest size notwithstanding. It appears that no useful instrumental records exist, due in part to a teleseismic event that occurred approximately 3 min later, generating surface waves that were recorded on early long‐period instruments in the region. Instrumental data may exist for this event but have not been found. Comparing the felt area with information from recent earthquakes in the region, I estimate an intensity magnitude of 3.8 for the event, with a subjectively estimated uncertainty range 3.5–4.1. Relatively strong effects, including reports of broken dishes in Milwaukee, and shaking described as short but especially sharp, suggest that the event may have been among the sprinkling of shallow earthquakes now known to occur in the upper Great Lakes region.
Information on the annual variability in abundance and growth of juvenile groundfish can be useful for predicting fisheries stocks, but is often poorly known owing to difficulties in sampling fish in their first year of life. In the Western Gulf of Alaska (WGoA) and Eastern Bering Sea (EBS) ecosystems, three species of puffin (tufted and horned puffin, Fratercula cirrhata, Fratercula corniculata, and rhinoceros auklet, Cerorhinca monocerata, Alcidae), regularly prey upon (i.e., “sample”) age-0 groundfish, including walleye pollock (Gadus chalcogramma, Gadidae) and Pacific cod (Gadus microcephalus, Gadidae). Here, we test the hypothesis that integrating puffin dietary data with walleye pollock stock assessment data provides information useful for fisheries management, including indices of interannual variation in age-0 abundance and growth. To test this hypothesis, we conducted cross-correlation and regression analyses of puffin-based indices and spawning stock biomass (SSB) for the WGoA and EBS walleye pollock stocks. For the WGoA, SSB leads the abundance of age-0 fish in the puffin diet, indicating that puffins sample the downstream production of the WGoA spawning stock. By contrast, the abundance and growth of age-0 fish sampled by puffins lead SSB for the EBS stock by 1–3 years, indicating that the puffin diet proxies incoming year class strength for this stock. Our study indicates connectivity between the WGoA and EBS walleye pollock stocks. Integration of non-traditional data sources, such as seabird diet data, with stock assessment data appears useful to inform information gaps important for managing US fisheries in the North Pacific.