Forests in the Appalachian region of the U.S. are threatened by a variety of short- and long-term pressures, including climate change, invasive species, and resource extraction. Surface mining for coal is one of the most important drivers of land-use change in the region, reducing native forest cover, causing forest fragmentation, eliminating intact soil, and affecting water resources. The Forestry Reclamation Approach (FRA) has been demonstrated as a successful best practice for restoring forests on mine-impacted landscapes, but little information exists on how the practice will affect hydrologic processes. A study was initiated to examine soil-water movement, as in-situ saturated hydraulic conductivity (Ksat), combined with soil porosity to quantify the potential influence on streamflow of reclaimed mines relative to an unmined, forested control site in eastern Kentucky. We compared different reclamation techniques and time since reclamation to determine the extent to which hydrologic function can be restored. We also simulated evapotranspiration at the watershed scale as a function of reclamation technique for both historical and projected (2050) climate. Results indicate that conventional grassland reclamation critically changes how soil water transitions to streamflow, primarily due to Ksat variability that exceeds that measured for intact and FRA soils. Sites reclaimed using FRA exhibited a soil-water environment that was more similar to the unmined control. However, all reclaimed mine soils were thinner, retained and stored less soil water, and thus could provide less plant-available water during the growing season. The plant-available water stored in reclaimed landscapes may not be sufficient to support forest health and this is exacerbated by projected climate conditions. However, soil development under a combination of FRA techniques has the potential to mitigate this limitation.
Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18,903 wells; 287,272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities.
The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km2. Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-020-08403-1","usgsCitation":"Van Metre, P.C., Qi, S.L., Deacon, J.R., Dieter, C., Driscoll, J.M., Fienen, M.N., Kenney, T.A., Lambert, P.M., Lesmes, D.P., Mason, C., Mueller-Solger, A., Musgrove, M., Painter, J.A., Rosenberry, D.O., Sprague, L.A., Tesoriero, A.J., Windham-Myers, L., and Wolock, D.M., 2020, Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey: Environmental Modeling & Assessment, v. 192, 458, 17 p., https://doi.org/10.1007/s10661-020-08403-1.","productDescription":"458, 17 p.","ipdsId":"IP-114496","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456145,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-020-08403-1","text":"Publisher Index Page"},{"id":436898,"rank":0,"type":{"id":30,"text":"Data 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Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called “green-wave surfing” [3-5]. Yet, general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of green-up facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n=1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2020.06.032","usgsCitation":"Aikens, E., Mysterud, A., Merkle, J., Cagnacci, F., Rivrud, I.M., Hebblewhite, M., Hurley, M., Peters, W., Bergen, S., De Groeve, J., Dwinnell, S.P., Gehr, B., Heurich, M., Mark Hewison, A.J., Jarnemo, A., Kjellander, P., Kroschel, M., Licoppe, A., Linnell, J., Merrill, E.H., Middleton, A.D., Morellet, N., Neufeld, L., Ortega, A.C., Parker, K.L., Pedrotti, L., Proffitt, K., Said, S., Sawyer, H., Scurlock, B.M., Signer, J., Stent, P., Sustr, P., Szkorupa, T., Monteith, K., and Kauffman, M., 2020, Wave-like patterns of plant phenology determine ungulate movement tactics: Current Biology, v. 30, no. 17, p. 3444-3449, 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‘New Narratives for Nature: operationalizing the IPBES Nature Futures Scenarios’ was organised by the IPBES task force on scenarios and models and hosted by the Institute for Global Environmental Strategies (IGES), with support from the research team on “Predicting and Assessing Natural Capital and Ecosystem Services through an Integrated Social-Ecological Systems Approach (PANCES)” based at the University of Tokyo, the Research Institute for Humanity and Nature (RIHN), and the United Nations University, with generous financial support from the Ministry of the Environment of Japan. \n\nDue to the COVID-19 virus outbreak, most task force members participated through virtual means, with a subset of task force members meeting in person in Japan.\n\nThe aim of the workshop was to build on the Nature Futures Framework (NFF) and on the ‘nature futures’ participatory scenario-development work initiated by the IPBES expert group on scenarios and models in the first IPBES work programme. This workshop aims to further elaborate the pre-workshop scenario narratives and to enrich discussions on the NFF. The workshop also served to start working on a more detailed task force work plan.\n\nThese aims were achieved through:\n• Task force sessions on the further formulation of the Nature Futures narratives.\n• Task force sessions on the cross-comparison of draft narratives and the further elaboration of the historical-present narrative.\n• Organisational sessions to begin the drafting of sub-deliverable-specific work plans.\n• In parallel to the task force workshop, collaborative sessions between the task force and Japanese researchers took place to discuss the application of the Nature Futures Framework at the national scale, using existing national level scenarios from Japan.\n• A public seminar, in Japan, for a wider audience introducing the scenarios and models task force’s work, the concept of the Nature Futures Framework, and fostered discussions on the concept of transformative change.\n\nSummary of outputs of the workshop in Japan\n• 6 NEW scenario narratives drafts – an evolution of the pre-workshop work using the narrative templates, into a more coherent set of narratives fitting their locations in the Nature Futures Framework, including some illustrative visualisations. \n• A cross-comparison table – to identify the core similarities and differences across the 6 new narratives (including single narrative-between-narrative comparisons).\n• A discussion on how to continue further development, requiring identifying pathways to complete the 6 new narratives.\n• 1 historical-to-present narrative draft – also an evolution of work done prior to the workshop. The task force has yet to synthesize and shorten this draft, ensuring linkages with topics detailed in the 6 new narratives into a more digestible level.\n• Elaboration of a follow-up plan for further development of the narratives, post-workshop, through a “buddy” system of in-depth online discussions per and between narratives. \n• 1 Japan case study – on fitting national level scenarios into the Nature Futures Framework. A summary will be shared by the team who worked closely on this with the PANCES partners, which we expect will give interesting insights to the cross-scale application of the Nature Futures Framework.\n• Detailed work plan implementation drafts (ongoing post workshop in sub-groups).","language":"English","publisher":"PBL Netherlands Environmental Assessment Agency","collaboration":"Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; And Institute for Global Environmental Strategies (IGES), University of Tokyo, Research Institute for Humanity and Nature (RIHN), United Nations University, Ministry of the Environment of Japan","usgsCitation":"Schoolenberg, M., Okayasu, S., Alkemade, R., Krijgsman, A., Dutra de Aguiar, A.P., Hashimoto, S., Lundquist, C.J., Pereira, L., Peterson, G., Armenteras, D., Cheung, W.W., Diaw, M.C., Duran, A.P., Gasalla, M., Halouani, G., Harrisson, P., Karlsson-Vinkhuyzen, S., Kim, H., Kuiper, J.J., Miller, B., Takahashi, Y., and Pichs, R., 2020, Report on the workshop ‘Next Steps in Developing Nature Futures’, i, 37 p.","productDescription":"i, 37 p.","ipdsId":"IP-118520","costCenters":[{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":376289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376282,"type":{"id":15,"text":"Index Page"},"url":"https://www.pbl.nl/en/publications/report-on-the-workshop-%E2%80%98new-narratives-for-nature-operationalizing-the-ipbes-nature-futures-scenarios%E2%80%99"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolenberg, Machteld","contributorId":228931,"corporation":false,"usgs":false,"family":"Schoolenberg","given":"Machteld","email":"","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okayasu, Sana","contributorId":228932,"corporation":false,"usgs":false,"family":"Okayasu","given":"Sana","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alkemade, Rob 0000-0001-8761-1768","orcid":"https://orcid.org/0000-0001-8761-1768","contributorId":202614,"corporation":false,"usgs":false,"family":"Alkemade","given":"Rob","email":"","affiliations":[{"id":36496,"text":"PBL Netherlands Environmental Assessment Agency","active":true,"usgs":false}],"preferred":false,"id":792559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krijgsman, Amanda","contributorId":228933,"corporation":false,"usgs":false,"family":"Krijgsman","given":"Amanda","email":"","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dutra de Aguiar, Ana Paula","contributorId":228934,"corporation":false,"usgs":false,"family":"Dutra de Aguiar","given":"Ana","email":"","middleInitial":"Paula","affiliations":[],"preferred":false,"id":792554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hashimoto, Shizuka","contributorId":228935,"corporation":false,"usgs":false,"family":"Hashimoto","given":"Shizuka","email":"","affiliations":[],"preferred":false,"id":792555,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundquist, Carolyn J.","contributorId":213140,"corporation":false,"usgs":false,"family":"Lundquist","given":"Carolyn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":792556,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pereira, Laura","contributorId":228936,"corporation":false,"usgs":false,"family":"Pereira","given":"Laura","email":"","affiliations":[],"preferred":false,"id":792557,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peterson, Garry","contributorId":228937,"corporation":false,"usgs":false,"family":"Peterson","given":"Garry","email":"","affiliations":[],"preferred":false,"id":792558,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Armenteras, Dolors","contributorId":228938,"corporation":false,"usgs":false,"family":"Armenteras","given":"Dolors","email":"","affiliations":[],"preferred":false,"id":792560,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cheung, William W. L.","contributorId":221038,"corporation":false,"usgs":false,"family":"Cheung","given":"William","email":"","middleInitial":"W. L.","affiliations":[],"preferred":false,"id":792561,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Diaw, Mariteuw Chimere","contributorId":228939,"corporation":false,"usgs":false,"family":"Diaw","given":"Mariteuw","email":"","middleInitial":"Chimere","affiliations":[],"preferred":false,"id":792563,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Duran, America Paz","contributorId":228940,"corporation":false,"usgs":false,"family":"Duran","given":"America","email":"","middleInitial":"Paz","affiliations":[],"preferred":false,"id":792564,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gasalla, Maria","contributorId":228941,"corporation":false,"usgs":false,"family":"Gasalla","given":"Maria","email":"","affiliations":[],"preferred":false,"id":792565,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Halouani, Ghassen","contributorId":228942,"corporation":false,"usgs":false,"family":"Halouani","given":"Ghassen","email":"","affiliations":[],"preferred":false,"id":792566,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Harrisson, Paula","contributorId":228943,"corporation":false,"usgs":false,"family":"Harrisson","given":"Paula","email":"","affiliations":[],"preferred":false,"id":792567,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Karlsson-Vinkhuyzen, Sylvia","contributorId":228944,"corporation":false,"usgs":false,"family":"Karlsson-Vinkhuyzen","given":"Sylvia","email":"","affiliations":[],"preferred":false,"id":792568,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Kim, HyeJin","contributorId":228945,"corporation":false,"usgs":false,"family":"Kim","given":"HyeJin","email":"","affiliations":[],"preferred":false,"id":792569,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kuiper, Jan J.","contributorId":222013,"corporation":false,"usgs":false,"family":"Kuiper","given":"Jan","email":"","middleInitial":"J.","affiliations":[{"id":40465,"text":"Stockholm Resilience Centre, Stockholm University","active":true,"usgs":false}],"preferred":false,"id":792570,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Miller, Brian W. 0000-0003-1716-1161 bwmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":195418,"corporation":false,"usgs":true,"family":"Miller","given":"Brian W.","email":"bwmiller@usgs.gov","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":792571,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Takahashi, Yasuo","contributorId":221515,"corporation":false,"usgs":false,"family":"Takahashi","given":"Yasuo","email":"","affiliations":[],"preferred":false,"id":792572,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Pichs, Ramon","contributorId":228957,"corporation":false,"usgs":false,"family":"Pichs","given":"Ramon","email":"","affiliations":[],"preferred":false,"id":792595,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70216109,"text":"70216109 - 2020 - Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA","interactions":[],"lastModifiedDate":"2020-11-05T14:41:53.835194","indexId":"70216109","displayToPublicDate":"2020-07-02T08:03:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA","docAbstract":"The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1–6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help decision-makers select thresholds and communicate how these values protect aquatic life use goals.
We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain‐derived magnitudes for the largest two Ridgecrest events to the real‐time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real‐time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190385","usgsCitation":"Farghal, N.S., Barbour, A.J., and Langbein, J., 2020, The potential of using dynamic strains in earthquake early warning applications: Seismological Research Letters, v. 91, no. 5, p. 2817-2827, https://doi.org/10.1785/0220190385.","productDescription":"11 p.","startPage":"2817","endPage":"2827","ipdsId":"IP-112135","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":377141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.345703125,\n 32.76880048488168\n ],\n [\n -114.345703125,\n 34.34343606848294\n ],\n [\n -120.05859375,\n 39.232253141714885\n ],\n [\n -120.14648437499999,\n 41.96765920367816\n ],\n [\n -119.61914062499999,\n 48.83579746243093\n ],\n [\n -123.96972656249999,\n 49.38237278700955\n ],\n [\n -126.3427734375,\n 49.866316729538674\n ],\n [\n -127.08984375000001,\n 48.22467264956519\n ],\n [\n -124.8486328125,\n 47.39834920035926\n ],\n [\n -124.76074218749999,\n 44.74673324024678\n ],\n [\n -125.33203125,\n 41.343824581185686\n ],\n [\n -124.01367187499999,\n 38.238180119798635\n ],\n [\n -121.728515625,\n 35.28150065789119\n ],\n [\n -119.66308593749999,\n 33.7243396617476\n ],\n [\n -117.158203125,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Farghal, Noha Sameh Ahmed 0000-0001-8423-5066","orcid":"https://orcid.org/0000-0001-8423-5066","contributorId":237040,"corporation":false,"usgs":true,"family":"Farghal","given":"Noha","email":"","middleInitial":"Sameh Ahmed","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J 0000-0001-6473-5493 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0001-6473-5493","contributorId":237041,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","email":"abarbour@usgs.gov","middleInitial":"J","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langbein, John 0000-0002-7821-8101","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":212735,"corporation":false,"usgs":true,"family":"Langbein","given":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228337,"text":"70228337 - 2020 - Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","interactions":[],"lastModifiedDate":"2022-02-09T22:45:53.971165","indexId":"70228337","displayToPublicDate":"2020-07-01T16:38:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","docAbstract":"Coastal marsh loss, combined with expected sea-level rise, will cause inundation and extensive shifts to vegetation and salinity regimes that may affect the bird species dependent on coastal ecosystems worldwide. Within coastal marsh habitats, birds provide key targets for coastal management goals. However, limited information on bird-habitat relationships within coastal marshes inhibits the development of restoration projects targeted to bird species. We surveyed birds bi-monthly within Barataria Basin, LA from July 2014 to December 2015 to compare their use between fresh and saline coastal marshes. Additionally, we examined habitat use at finer spatial scales to assess preference for marsh edge microhabitats. Edge habitat supported 1.8 times more bird species (guild) richness than emergent and open water habitat. We concluded that future modelling efforts would be improved if models incorporate edge effects for birds in coastal marshes that extend 20 m from emergent vegetation into open water, with a reduced effect if marsh types convert from fresh to saline. Our data will be useful to simulate the effects of changes in marsh type, area, and edge on habitat quality for birds in coastal Louisiana and will inform habitat restoration and management decisions aimed at optimizing bird use.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01324-2","usgsCitation":"Patton, B., Nyman, J.A., and La Peyre, M., 2020, Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA: Wetlands, v. 40, p. 2041-2054, https://doi.org/10.1007/s13157-020-01324-2.","productDescription":"14 p.","startPage":"2041","endPage":"2054","ipdsId":"IP-098169","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499826,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/602","text":"External Repository"},{"id":395743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.8349609375,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 28.714678586705976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":833828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":833829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211872,"text":"70211872 - 2020 - Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","interactions":[],"lastModifiedDate":"2020-12-15T20:23:40.067951","indexId":"70211872","displayToPublicDate":"2020-07-01T16:07:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6000,"text":"The Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","docAbstract":"The eastern Snake River Plain (ESRP) is a northeast-trending topographic basin interpreted to be the result of the time-transgressive track of the North American plate above the Yellowstone hotspot. The track is defined by the age progression of silicic volcanic rocks exposed along the margins of the ESRP. However, the bulk of these silicic rocks are buried under 1 to 3 kilometers of younger basalts. Here, silicic volcanic rocks recovered from boreholes that penetrate below the basalts, including INEL-1, WO-2 and new deep borehole USGS-142, are correlated with one another and to surface exposures to assess various models for ESRP subsidence. These correlations are established on U/Pb zircon and 40Ar/39Ar sanidine age determinations, phenocryst assemblages, major and trace element geochemistry, δ18O isotopic data from selected phenocrysts, and initial εHf values of zircon. These data suggest a correlation of: (1) the newly documented 8.1 ± 0.2 Ma rhyolite of Butte Quarry (sample 17KS03), exposed near Arco, Idaho to the upper-most Picabo volcanic field rhyolites found in borehole INEL-1; (2) the 6.73 ± 0.02 Ma East Arco Hills rhyolite (sample 16KS02) to the Blacktail Creek Tuff, which was also encountered at the bottom of borehole WO-2; and (3) the 6.42 ± 0.07 Ma rhyolite of borehole USGS-142 to the Walcott Tuff B encountered in deep borehole WO-2. These results show that rhyolites found along the western margin of the ESRP dip ~20º south-southeast toward the basin axis, and then gradually tilt less steeply in the subsurface as the axis is approached. This subsurface pattern of tilting is consistent with a previously proposed crustal flexural model of subsidence based only on surface exposures, but is inconsistent with subsidence models that require accommodation of ESRP subsidence on either a major normal fault or strike-slip fault.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.57.3.241","usgsCitation":"Schusler, K.L., Pearson, D.M., McCurry, M.J., Bartholomay, R.C., and Anders, M.H., 2020, Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity: The Mountain Geologist, v. 57, no. 3, p. 241-270, https://doi.org/10.31582/rmag.mg.57.3.241.","productDescription":"30 p.","startPage":"241","endPage":"270","ipdsId":"IP-112371","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":377936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.18002319335938,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.41302868475145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schusler, Kyle L.","contributorId":237858,"corporation":false,"usgs":false,"family":"Schusler","given":"Kyle","email":"","middleInitial":"L.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearson, David M.","contributorId":237860,"corporation":false,"usgs":false,"family":"Pearson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCurry, Michael J.","contributorId":237861,"corporation":false,"usgs":false,"family":"McCurry","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartholomay, Roy C. 0000-0002-4809-9287 rcbarth@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-9287","contributorId":1131,"corporation":false,"usgs":true,"family":"Bartholomay","given":"Roy","email":"rcbarth@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anders, Mark H.","contributorId":237862,"corporation":false,"usgs":false,"family":"Anders","given":"Mark","email":"","middleInitial":"H.","affiliations":[{"id":39266,"text":"St. Lawrence University","active":true,"usgs":false}],"preferred":false,"id":795488,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210894,"text":"sir20205064 - 2020 - A summary of water-quality monitoring in San Francisco Bay in water year 2017","interactions":[],"lastModifiedDate":"2020-07-01T21:11:28.152523","indexId":"sir20205064","displayToPublicDate":"2020-07-01T12:33:01","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5064","displayTitle":"A Summary of Water-Quality Monitoring in San Francisco Bay in Water Year 2017","title":"A summary of water-quality monitoring in San Francisco Bay in water year 2017","docAbstract":"This report summarizes the activities of the U.S. Geological Survey (USGS) San Francisco Bay Water-Quality Monitoring and Sediment Transport Project during water year 2017, including an explanation of methods employed, stations operated, and a graphical summary of data for the period of record for stations operational in water year 2017. In cooperation with partner agencies, the USGS maintains a network of sensors that continuously and autonomously measures water-quality parameters in San Francisco Bay including water temperature, specific conductance, turbidity, and suspended-sediment concentration. Data are collected at several locations in the estuary by a network of water-quality sondes sampled at 15-minute intervals. Methods of data collection are presented along with documentation of the regression models utilized to estimate suspended-sediment concentration from observed turbidity, a commonly utilized surrogate to estimate suspended-sediment concentration. The goals of the data collection effort are to (1) obtain long-term, high-frequency, and high-quality data to describe San Francisco Bay water quality; (2) make the data publicly available on the USGS National Water Information System data portal; and (3) help improve understanding of the spatial and temporal variability of water quality in the estuary, informing management decisions regarding restoration, water supply, navigation, and ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205064","usgsCitation":"Livsey, D., and Downing-Kunz, M., 2020, A summary of water-quality monitoring in San Francisco Bay in water year 2017: U.S. Geological Survey Scientific Investigations Report 2020–5064, 78 p., https://doi.org/10.3133/sir20205064.","productDescription":"Report: vi, 78 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-104269","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376068,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"National Water Information System"},{"id":376066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5064/coverthb.jpg"},{"id":376067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5064/sir20205064.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62390136718749,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 37.35050947036205\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
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Hydrographic features derived from U.S. Geological Survey (USGS) 3D Elevation Program data, and collected for use by the USGS, must meet the specifications described in this document. The specifications described herein pertain to the final product delivered to the USGS, not to methods used to derive the hydrographic features. The specifications describe the collection area, spatial reference system, attribute table structure, feature codes and values, delineation of hydrographic features, topology, positional assessment, metadata, and delivery formats. A companion document, Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules, defines the fields, domains, and minimum feature collection requirements for hydrography features derived from elevation data. Hydrographic features collected to this specification will be suitable for using as breaklines to hydroflatten digital elevation models, processing for preconflation of features to the National Hydrography Dataset, and using for hydroenforcement of digital elevation models.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B11","usgsCitation":"Terziotti, S., and Archuleta, C.M., 2020, Elevation-Derived Hydrography Acquisition Specifications: U.S. Geological Survey Techniques and Methods, book 11, chap. B11, 74 p., https://doi.org/10.3133/tm11B11.","productDescription":"Report: vii, 74 p.; Companion Report","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-111691","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":376021,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/tm11B12","text":"T&M 11–B12","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B12","linkHelpText":"— Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules"},{"id":376020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b11/tm11b11.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B11"},{"id":376019,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b11/coverthb.jpg"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Playa wetlands in the Great Plains, USA support a wide variety of plant species not found elsewhere in this agriculturally-dominated region due to the ephemeral presence of standing water and hydric soils within playas. If longer dry periods occur due to climate change or if changes in surrounding land use alter sediment accumulation rates and water storage capacity in playas, plant communities could experience decreased diversity, with lasting effects on ecosystem services provided by playas in the Great Plains and at a continental-level in North America. We quantified potential changes in playa wetland plant community composition associated with predicted changes in precipitation and land use in the Great Plains through the end of the 21st century. We conducted two six-month greenhouse experiments mimicking field conditions using intact mesocosms collected from playas in Nebraska and Texas. In the precipitation experiment, treatments derived from historical precipitation observations and three future moderate emissions (CMIP5 RCP4.5) downscaled climate projections were applied to mesocosms. For the land use experiment, treatments were simulated by nitrogen (N) applications to soil ranging from 0 to 100 mg-N L-1 with each precipitation event under historical rainfall patterns, representing increasing and decreasing area in agricultural use in playa watersheds. Plant communities tended to shift toward more native species under projected future climate conditions, but as N runoff increased, native species richness decreased. Agricultural land-use surrounding playas may have a greater effect on wetland plant communities than future alterations to hydrology based on climate change in the Great Plains; thus, efforts to reduce nutrient runoff into playas would likely mitigate loss in ecosystem function in the coming decades.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envexpbot.2020.104039","usgsCitation":"Owen, R.K., Webb, E.B., Haukos, D.A., and Goyne, K.W., 2020, Projected climate and land use changes drive plant community composition in agricultural wetlands: Environmental and Experimental Botany, v. 175, p. 1-12, https://doi.org/10.1016/j.envexpbot.2020.104039.","productDescription":"104039, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-111000","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envexpbot.2020.104039","text":"Publisher Index Page"},{"id":395691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska, Texas","otherGeospatial":"Rainwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.01953125,\n 40.01078714046552\n ],\n [\n -96.51489257812499,\n 40.01078714046552\n ],\n [\n -96.51489257812499,\n 41.77950486590359\n ],\n [\n -100.01953125,\n 41.77950486590359\n ],\n [\n -100.01953125,\n 40.01078714046552\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.07373046875,\n 32.02670629333614\n ],\n [\n -103.040771484375,\n 31.44741029142872\n ],\n [\n -98.668212890625,\n 31.475524020001806\n ],\n [\n -98.7451171875,\n 34.15272698011818\n ],\n [\n -98.85498046875,\n 34.19817309627726\n ],\n [\n -99.041748046875,\n 34.252676117101515\n ],\n [\n -99.151611328125,\n 34.20725938207231\n ],\n [\n -99.283447265625,\n 34.42503613021332\n ],\n [\n -99.42626953125,\n 34.46127728843705\n ],\n [\n -99.42626953125,\n 34.38877925439021\n ],\n [\n -99.60205078124999,\n 34.42503613021332\n ],\n [\n -99.66796875,\n 34.38877925439021\n ],\n [\n -99.986572265625,\n 34.58799745550482\n ],\n [\n -99.97558593749999,\n 36.518465989675875\n ],\n [\n -103.062744140625,\n 36.50963615733049\n ],\n [\n -103.07373046875,\n 32.02670629333614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Owen, Rachel K.","contributorId":273204,"corporation":false,"usgs":false,"family":"Owen","given":"Rachel","email":"","middleInitial":"K.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goyne, Keith W.","contributorId":204931,"corporation":false,"usgs":false,"family":"Goyne","given":"Keith","email":"","middleInitial":"W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833948,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228141,"text":"70228141 - 2020 - Defining the need for genetic stock assignment when describing stock demographics and dynamics: An example using Lake Whitefish in Lake Michigan","interactions":[],"lastModifiedDate":"2022-02-04T16:50:01.408572","indexId":"70228141","displayToPublicDate":"2020-07-01T10:39:55","publicationYear":"2020","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":"Defining the need for genetic stock assignment when describing stock demographics and dynamics: An example using Lake Whitefish in Lake Michigan","docAbstract":"Genetic stock assignment is not routinely used when describing the dynamics and demographics of individual stocks supporting mixed-stock fisheries, and capture location and timing are often used as alternative assignment methods. However, variation in stock demographics and dynamics may not be accounted for if stock assignments based on capture location or timing do not accurately reflect genetic assignments. We used Lake Whitefish Coregonus clupeaformis in Lake Michigan as a model fishery to determine whether stock mixing could undermine efforts to describe stock status when using October capture location as a proxy for genetic stock assignment. Accuracy of stock assignments based on October capture location ranged from 54% to 100% among management zones. Metrics describing length and age distributions, weight at length, fecundity, and growth varied among genetic stocks. Stock-specific metrics were typically similar between stock assignment methods (capture location versus genetics) because only one or two genetic stocks were collected in most locations and the majority of those fish were from spatially proximal stocks with similar metrics. However, more extensive mixing of Lake Whitefish stocks has been documented; thus, using capture location for stock assignment could result in incorrect conclusions regarding stock status and harvest management depending on stock composition. Ambiguity in genetic stock assignments was a problem in two management zones, where between 23% and 42% of Lake Whitefish did not assign to a specific stock with a probability of at least 0.70. In the future, using genomic techniques rather than microsatellites may provide different conclusions regarding genetic stock structure; these differences could affect the accuracy of using capture location for stock assignment. Use of capture location as a proxy for genetic stock assignment may not be warranted for all mixed-stock fisheries but may be appropriate when stock mixing is limited or is restricted to stocks with consistently similar characteristics.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10235","usgsCitation":"Isermann, D.A., Belnap, M.J., Turnquist, K.N., Sloss, B., VanDeHey, J.A., Hansen, S.P., and Caroffino, D.C., 2020, Defining the need for genetic stock assignment when describing stock demographics and dynamics: An example using Lake Whitefish in Lake Michigan: Transactions of the American Fisheries Society, v. 149, no. 4, p. 398-413, https://doi.org/10.1002/tafs.10235.","productDescription":"16 p.","startPage":"398","endPage":"413","ipdsId":"IP-105758","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": 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Leetown","active":true,"usgs":true}],"preferred":true,"id":833201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belnap, Matthew J.","contributorId":274657,"corporation":false,"usgs":false,"family":"Belnap","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":833202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turnquist, Keith N.","contributorId":139517,"corporation":false,"usgs":false,"family":"Turnquist","given":"Keith","email":"","middleInitial":"N.","affiliations":[{"id":12787,"text":"Molecular Conservation Genetics Laboratory, University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":833203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sloss, Brian L.","contributorId":9754,"corporation":false,"usgs":true,"family":"Sloss","given":"Brian L.","affiliations":[],"preferred":false,"id":833204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"VanDeHey, Justin A.","contributorId":50800,"corporation":false,"usgs":true,"family":"VanDeHey","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":833205,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hansen, Scott P.","contributorId":79837,"corporation":false,"usgs":true,"family":"Hansen","given":"Scott","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":833206,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caroffino, David C.","contributorId":181527,"corporation":false,"usgs":false,"family":"Caroffino","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":833207,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70214490,"text":"70214490 - 2020 - A new data set of granitic rock strength values from Yosemite Valley, California: Applications to rock fall assessment","interactions":[],"lastModifiedDate":"2020-09-30T15:29:40.87055","indexId":"70214490","displayToPublicDate":"2020-07-01T10:26:05","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A new data set of granitic rock strength values from Yosemite Valley, California: Applications to rock fall assessment","docAbstract":"To explore connections between rock strength and rock falls, we undertook a comprehensive rock mechanics testing program for six granitic rock types in Yosemite Valley (California, USA) where rock falls are a common geomorphic and sometimes hazardous process. We collected samples from boulders located at the base of cliffs, with the inherent assumption that the intact boulders should provide reasonable estimates of full-strength values. Our testing program included unconfined compressive strength tests, triaxial compressive strength tests, Brazilian tensile strength tests, and Mode I fracture toughness strength testing using two different types of samples – chevron bend (CB) and cracked chevron notched Brazilian disk (CCNBD). Our results, consisting of 88 individual tests, provide the most detailed evaluation of rock strength in Yosemite Valley to date. These results provide the data needed to evaluate the various failure modes (e.g., shear failure of wedge instabilities, tensile failure of overhangs) that might be expected for rock falls from cliffs in Yosemite. We expect that these data will provide an important resource for the evaluation of rock falls and other geomorphological studies in Yosemite National Park.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"54th US Rock Mechanics/Geomechanics Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"American Rock Mechanics Association","collaboration":"National Park Service, University of Lausanne, École Polytechnique Fédérale de Lausanne – EPFL","usgsCitation":"Collins, B.D., Sandrone, F., Gastaldo, L., Stock, G.M., and Jaboyedoff, M., 2020, A new data set of granitic rock strength values from Yosemite Valley, California: Applications to rock fall assessment, in 54th US Rock Mechanics/Geomechanics Symposium, 7 p.","productDescription":"7 p.","ipdsId":"IP-116600","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":378919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378800,"type":{"id":15,"text":"Index Page"},"url":"https://www.onepetro.org/conference-paper/ARMA-2020-1412"}],"country":"United States","state":"California","otherGeospatial":"Yosemite Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.11627197265624,\n 37.60335225883687\n ],\n [\n -118.92974853515624,\n 37.60335225883687\n ],\n [\n -118.92974853515624,\n 38.151837403006766\n ],\n [\n -120.11627197265624,\n 38.151837403006766\n ],\n [\n -120.11627197265624,\n 37.60335225883687\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":799727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sandrone, Federica","contributorId":225125,"corporation":false,"usgs":false,"family":"Sandrone","given":"Federica","email":"","affiliations":[{"id":27718,"text":"Ecole Polytechnique Federale de Lausanne","active":true,"usgs":false}],"preferred":true,"id":799728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gastaldo, Laurent","contributorId":225126,"corporation":false,"usgs":false,"family":"Gastaldo","given":"Laurent","email":"","affiliations":[{"id":27718,"text":"Ecole Polytechnique Federale de Lausanne","active":true,"usgs":false}],"preferred":true,"id":799729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stock, Greg M.","contributorId":202873,"corporation":false,"usgs":false,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":799730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaboyedoff, Michel","contributorId":205586,"corporation":false,"usgs":false,"family":"Jaboyedoff","given":"Michel","affiliations":[{"id":37117,"text":"University of Lausanne (Switzerland)","active":true,"usgs":false}],"preferred":false,"id":799731,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217905,"text":"70217905 - 2020 - The Cenozoic evolution of crustal shortening and left‐lateral shear in the central East Kunlun Shan: Implications for the uplift history of the Tibetan Plateau","interactions":[],"lastModifiedDate":"2021-02-10T14:05:13.86214","indexId":"70217905","displayToPublicDate":"2020-07-01T08:02:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"The Cenozoic evolution of crustal shortening and left‐lateral shear in the central East Kunlun Shan: Implications for the uplift history of the Tibetan Plateau","docAbstract":"The timing of crustal shortening and strike‐slip faulting along the East Kunlun Shan provides insight into the history of surface uplift and may constrain the time at which the Tibetan Plateau reached high elevations. We investigate a series of extensional basins and restraining bends along the Xidatan strand of the Kunlun strike‐slip fault, which provide an ideal setting to unravel the tectonic history of the northern plateau margin. We present new apatite (U‐Th)/He, apatite fission track, and zircon (U‐Th)/He ages and QTQt thermal modeling, 40Ar/39Ar fault gouge dating, and structural mapping from the central East Kunlun Shan. Our data suggest that the East Kunlun Shan experienced slow to negligible exhumation until late Cretaceous time, followed by an increase in rate by 65–50 Ma. Along with a ~47 Ma fault gouge age, we posit that the Paleocene–early Eocene was a time of crustal shortening along the northern plateau. Rapid exhumation along transpressional portions of the Xidatan fault initiated by 23–20 Ma, which we interpret as the local onset of strike‐slip faulting. An early Miocene transition from north‐south crustal shortening to left‐lateral shear along the East Kunlun Shan, the onset of normal and strike‐slip faulting in central and southern Tibet by 18 Ma, and lower crustal flow in eastern Tibet by 13 Ma suggest the establishment of orogen‐wide east‐west oriented extension and extrusion by the middle Miocene. The plateau‐wide shift in stress accommodation implies that high gravitational potential energy, and likely high elevation, was attained by the middle Miocene.
Careful design of a wildlife population monitoring strategy is necessary to obtain accurate and precise results whether the purpose of the survey is development of habitat suitability models, to estimate abundance, or assess site occupancy. Important characteristics to consider in survey design are sources of elevated variability, particularly within‐subject variability, which increases the amount of data needed to achieve statistical certainty either in terms of population trend analysis, hypothesis testing, or statistical power. However, alternative objectives, such as associating counts with habitat characteristics, may benefit from increased variation among counts when differences covary with habitat measures. This difference can result in competing needs when developing survey protocols. We investigated the relative precision of differing gamebird monitoring protocols to identify methods with the greatest statistical efficiency. We assessed call‐count transects using standard Breeding Bird Survey protocols (Passive call‐counts) and modified by including longer survey periods and call playback (Active call‐counts), autonomous recording units with supervised call detection (ARU‐recorded calls), camera traps, and roadside covey‐counts for Gambel's quail (Callipepla gambelii) in the Mojave Desert (CA, USA) during the spring of 2016. Active call‐counts had the lowest within‐site variation relative to estimated population index values, but Passive call‐count transects may be more efficient for some purposes because more survey stations can be completed within a single survey timeframe. The ARU‐recorded calls may provide a suitable alternative despite larger sample size needs, especially for occupancy surveys because multiple units can be deployed concurrently. The ultimate sample size required will depend on specific study objectives and scope of interest, but camera traps and breeding‐season covey counts are not likely to meet objectives in desert environments.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1105","usgsCitation":"Overton, C.T., Casazza, M.L., Connelley, D., and Gardner, S.C., 2020, Gambel’s quail survey variability and implications for survey design in the Mohave Desert: Wildlife Society Bulletin, v. 44, no. 3, p. 493-501, https://doi.org/10.1002/wsb.1105.","productDescription":"9 p.","startPage":"493","endPage":"501","ipdsId":"IP-104330","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436903,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SVPK0N","text":"USGS data release","linkHelpText":"Comparisons of Gambel's quail survey methods conducted in 2016 within the Mohave Desert of California with results and summaries"},{"id":380834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.597900390625,\n 33.8247936182649\n ],\n [\n -114.378662109375,\n 33.8247936182649\n ],\n [\n -114.378662109375,\n 35.576916524038616\n ],\n [\n -116.597900390625,\n 35.576916524038616\n ],\n [\n -116.597900390625,\n 33.8247936182649\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connelley, Daniel","contributorId":245293,"corporation":false,"usgs":false,"family":"Connelley","given":"Daniel","email":"","affiliations":[{"id":49140,"text":"Pheasants Forever, California","active":true,"usgs":false}],"preferred":false,"id":805781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":805782,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263639,"text":"70263639 - 2020 - California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps","interactions":[],"lastModifiedDate":"2025-02-19T16:21:00.999385","indexId":"70263639","displayToPublicDate":"2020-07-01T00:00:00","publicationYear":"2020","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":"California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps","docAbstract":"Historical seismic intensity data are useful for myriad reasons, including assessment of the performance of Probabilistic Seismic Hazard Assessment (PSHA) models and corresponding hazard maps by comparing their predictions to a dataset of historically observed intensities in the region. To assess PSHA models for California, a long and consistently interpreted intensity record is necessary. For this purpose, the California Historical Intensity Mapping Project (CHIMP) has compiled a dataset that combines and reinterprets intensity information that has been stored in disparate and sometimes hard-to-access locations. The CHIMP dataset also includes new observations of intensity from archival research and oral history collection. Version 1 of the dataset includes 46,502 intensity observations for 62 earthquakes with estimated magnitudes ranging from 4.7 to 7.9. The 162 years of shaking data show observed shaking lower than expected from seismic hazard models. This discrepancy is reduced, but persists, if historical intensity data for the largest earthquakes are smoothed to reduce the effects of spatial under-sampling. Possible reasons for this discrepancy include other limitations of the CHIMP dataset, the hazard models, and the possibility that California seismicity throughout the historical period has been lower than the long-term average. Some of these issues may also explain similar discrepancies observed for Italy and Japan.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200065","usgsCitation":"Salditch, L., Gallahue, M.M., Lucas, M.C., Neely, J.S., Hough, S.E., and Stein, S., 2020, California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps: Seismological Research Letters, v. 91, no. 5, p. 2631-2650, https://doi.org/10.1785/0220200065.","productDescription":"20 p.","startPage":"2631","endPage":"2650","ipdsId":"IP-119084","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"91","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Salditch, Leah","contributorId":263445,"corporation":false,"usgs":false,"family":"Salditch","given":"Leah","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallahue, Molly M.","contributorId":263448,"corporation":false,"usgs":false,"family":"Gallahue","given":"Molly","email":"","middleInitial":"M.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Madeleine C.","contributorId":263451,"corporation":false,"usgs":false,"family":"Lucas","given":"Madeleine","email":"","middleInitial":"C.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neely, James S.","contributorId":263454,"corporation":false,"usgs":false,"family":"Neely","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927642,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":927643,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212898,"text":"70212898 - 2020 - Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU: January 2019 - December 2019","interactions":[],"lastModifiedDate":"2020-09-01T23:43:54.567337","indexId":"70212898","displayToPublicDate":"2020-06-30T18:43:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU: January 2019 - December 2019","docAbstract":"The portion of the Snake River fall Chinook salmon Oncorhynchus tshawytscha evolutionary significant unit (ESU) that spawns upstream of Lower Granite Dam transitioned from low to high abundance during 19922019 in association with U.S. Endangered Species Act recovery efforts and other federally mandated actions. This annual report focuses on (1) numeric and habitat use responses by natural- and hatchery-origin spawners, (2) phenotypic and numeric responses by natural-origin juveniles, and (3) use of a small unmanned aerial system (sUAS) to search for fall Chinook salmon redds and carcasses. Spawners have located and used most of the available spawning habitat and that habitat is gradually approaching redd capacity. Timing of spawning and fry emergence have been relatively stable, but effects of density dependence are evident in juvenile life stages. Apparent abundance of juvenile fall Chinook salmon has increased and we noted the following responses: parr dispersal from riverine rearing habitat into Lower Granite Reservoir has become earlier; growth rate (g/d) and dispersal size of parr declined; and passage timing of smolts from the two Snake River reaches has become earlier and downstream movement rate faster. These findings coupled with stock-recruitment analyses presented in this report provide evidence for density-dependence in the Snake River reaches and in Lower Granite Reservoir that was influenced by the expansion of the recovery program. The long-term goal is to use this information in a comprehensive modeling effort to conduct action-effectiveness and uncertainty research and to inform Fish Population, Hydrosystem, Harvest, Hatchery, and Predation and Invasive Species Management Research, Monitoring, and Evaluation (RM&E) progams.
In 2019, the U.S. Geological Survey (USGS) shifted survey efforts in the Snake River toward deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses because all unmanned aerial system (UAS) activities were suspended by the Department of the Interior two weeks into the spawning season. We counted 81 deepwater redds at 17 of the 29 sites surveyed. Redd depths averaged 3.6 m. We collected a total of 123 live fall Chinook salmon from 16 unique geographic locations that spanned 55 river kilometers. Forty-six fish were recovered at Eureka Bar (rkm 307.1) and Kirby Creek (rkm 352.0), which accounted for 37% of all collected fish in 2019. Most (73 fish) post-spawned salmon were collected from early to mid-November just after peak spawning. A summary of 2019 PBT results can be found in Appendix A.1.
In 2019, we PIT tagged subyearling fall Chinook salmon in both the Snake and Clearwater rivers. In the Snake River, we tagged 410 fish with 8-mm tags, 666 fish with 9-mm tags, and 1,082 fish 12-mm tags. During seining, our recapture rate of previously tagged fish was slightly higher in the lower reach at 11.4% than in the upper reach at 10.6%. In an effort to represent more of the population through tagging, we tagged fish as small as 45 mm with 8-mm tags at one site in the upper reach. This allowed us to increase the number of fish tagged in that reach by 17.7%. An additional 10.6% of collected fish could have been tagged in the lower reach had we used 8-mm tags in that reach. In the Clearwater River, we tagged 2,451 subyearlings and recaptured 260 (10.6%) fish in the river and 66 fish (48 tagged by USGS, 18 tagged by the Nez Perce Tribe) at Lower Granite Dam during October to provide information for growth estimation. Within riverine habitats, growth in both length and mass were higher for fish tagged with 8-mm tags than with 9- and 12-mm tags. Estimated growth in length and mass of subyearlings was higher in Lower Granite Reservoir than in riverine habitats.
We adapted existing statistical models used to estimate abundance of steelhead and spring/summer Chinook salmon for fall Chinook salmon passing Lower Granite Dam. Run reconstruction efforts to date at Lower Granite Dam for Snake River fall Chinook salmon have provided estimates of the number of returning adults but with no measure of uncertainty about the estimates. The objective of this study was to estimate the abundance, with uncertainty, of marked (coded-wire tagged CWT or adipose clipped) and unmarked fall Chinook salmon past Lower Granite Dam for return years 20032018. Estimating uncertainty is important for informing the state-space life cycle model (Chapter 5), which incorporates both observation and process uncertainty into parameter estimates. The coefficient of variation (CV) for log- abundance was 1.0% or less in all years, whereas the CV for abundance averaged 4.2% and ranged from 1.4% to 10.4% among years.
Over the past five years, we have been developing a two-stage state-space life-cycle model for naturally produced fall Chinook salmon in the Snake River basin. Initial efforts focused on generating juvenile and adult abundance estimates, with estimates of uncertainty, for informing the life-cycle model. In this report we 1) describe the statistical life-cycle model and improvements made since our last report to the Independent Scientific Advisory Board (ISAB), 2) estimate the effects of covariates on key demographic parameters, and 3) use the fitted life- cycle model to simulate population trajectories under hydrosystem actions proposed for the NOAA 2020 Biological Opinion (hereafter, the Proposed Action). Major recent advancements to the model include revised juvenile abundance estimates, the ability to estimate smolt-to-adult return rates (SAR) separately for subyearling and yearling juvenile fall Chinook salmon, and improvements in the observation model for estimating age, sex, and outmigration structure in adult returns. We examined the effect of numerous environmental, hydrosystem, and ocean covariates on key demographic parameters but only a few covariates were significant. For the adult-to-juvenile transition, we found the maximum weekly river flows during the winter egg- incubation period had a significant negative effect on the juvenile outmigrant abundance from that brood year. For subyearling outmigrants, percent spill during the summer had a significant positive effect on SAR and the mean winter PDO (Pacific Decadal Oscillation) had a significant negative effect on SAR. For yearling outmigrants, NPGO (North Pacific Gyre Oscillation) had a significant positive effect on SAR. We used the fitted model to simulate population trajectories under the Proposed Action, and the median 10-year geometric mean abundance was 8,222 female spawners (interquartile range: 2,592 26,714). Overall, the probability of quasi- extinction (probability of falling below 50 female spawners for 4 consecutive years) was low, with only 1.6% of all simulations having a quasi-extinction probability >0.95. Although quasi-extinction probability was low, we did not assess the additional effect of climate change, which would be expected to increase quasi-extinction probability.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"2020, Research, monitoring, and evaluation of emerging issues and measures to recover the Snake River Fall Chinook Salmon ESU: January 2019 - December 2019, v, 126 p.","productDescription":"v, 126 p.","ipdsId":"IP-119421","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":378076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378055,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/DocumentViewer/P176701/84776-1.pdf"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.9375,\n 41.983994270935625\n ],\n [\n -113.31298828125,\n 41.983994270935625\n ],\n [\n -113.31298828125,\n 48.004625021133904\n ],\n [\n -120.9375,\n 48.004625021133904\n ],\n [\n -120.9375,\n 41.983994270935625\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":220176,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797793,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797794,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70213311,"text":"70213311 - 2020 - A science business model for answering important questions","interactions":[],"lastModifiedDate":"2020-09-17T17:55:22.37147","indexId":"70213311","displayToPublicDate":"2020-06-30T12:48:18","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"A science business model for answering important questions","docAbstract":"Perhaps the biggest question in science is how to do better science. Many ecologists, including this book’s editors and authors, have succeeded under the current science “business model” and, from our perspective, the status quo works well enough. But science business models are under increased scrutiny. For instance, since 2012, at least nine papers have critiqued government-sponsored biomedical research, with the most-suggested (self-serving) solution being to spend more government funds on science (Pickett et al. 2015). To get more funding, scientists might consider first improving their return on investment. To increase return on investment, ecologists (and scientists in general) could rethink training programs, reproducibility, funding distribution, synthesis, publication models, and evaluation metrics.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Unsolved Problems in Ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Princeton University Press","usgsCitation":"Lafferty, K.D., 2020, A science business model for answering important questions, chap. of Unsolved Problems in Ecology, p. 357-373.","productDescription":"17 p.","startPage":"357","endPage":"373","ipdsId":"IP-077013","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":378527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":799010,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222472,"text":"70222472 - 2020 - 2023 Coastal master plan: Model improvement plan, ICM-wetlands, vegetation, and soil","interactions":[],"lastModifiedDate":"2021-09-08T15:43:58.264089","indexId":"70222472","displayToPublicDate":"2020-06-30T10:39:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":9334,"text":"Coastal Master Plan","active":true,"publicationSubtype":{"id":4}},"title":"2023 Coastal master plan: Model improvement plan, ICM-wetlands, vegetation, and soil","docAbstract":"As part of the model improvement effort for the 2023 Coastal Master Plan, the wetland processes captured by the morphology and vegetation models used during previous master plans were reevaluated to assess how Integrated Compartment Model (ICM) subroutines could be improved. This process considered technical reviews, comments, and suggested improvements provided by model developers, advisory groups, and other experts during previous master plan cycles. The availability of new data and information that could be used to make model improvements was also considered. In many cases, the team considered and tested multiple options or approaches. As a result of this effort, recommended improvements are provided here.
The improvements recommended to be included in the 2023 Coastal Master Plan include: adjusting marsh collapse thresholds, refining organic matter accretion calculations, developing an unstructured grid for modeling vegetation, improving flotant marsh and forested wetlands algorithms, creating and applying an updated map of existing vegetation, adjusting model code, and updating the submerged aquatic vegetation (SAV) module.
This report describes the team’s work through a series of 7 distinct activities to identify and test options for model improvements to ensure the updated ICM used for the 2023 Coastal Master Plan appropriately captures ecological and morphological processes observed in Coastal Louisiana. As appropriate, relevant literature and data are discussed. Test runs to evaluate how changes influence model outputs are also documented. A final list of recommended updates, taking into account consideration of all options and results from test runs, is summarized at the end of the report. A later report will describe the final ICM-LAVegMod and ICM-Morph subroutines for the 2023 Coastal Master Plan, detailing the updates that have been incorporated.
","language":"English","publisher":"Coastal Protection and Restoration Authority","usgsCitation":"Baustian, M., Reed, D., Visser, J., Duke-Sylvester, S.M., Snedden, G., Wang, H., DeMarco, K., Foster-Martinez, M.R., Sharp, L.A., McGinnis, T., and Jarrell, E., 2020, 2023 Coastal master plan: Model improvement plan, ICM-wetlands, vegetation, and soil: Coastal Master Plan, 155 p.","productDescription":"155 p.","ipdsId":"IP-117713","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":388949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388947,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://coastal.la.gov/wp-content/uploads/2021/04/ICM-Wetlands-Veg-Soils-Model-Improvement-Report_June2020.pdf"},{"id":387563,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/technical-resources/"}],"country":"United 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E.","contributorId":149677,"corporation":false,"usgs":false,"family":"McGinnis","given":"Tommy E.","affiliations":[{"id":17778,"text":"Coastal Protection and Restoration Authority of Louisiana","active":true,"usgs":false}],"preferred":false,"id":820167,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jarrell, Elizabeth","contributorId":261556,"corporation":false,"usgs":false,"family":"Jarrell","given":"Elizabeth","email":"","affiliations":[{"id":13608,"text":"Louisiana Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":820168,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236801,"text":"70236801 - 2020 - Automated extraction of areal extents for GNIS Summit features using the eminence core method","interactions":[],"lastModifiedDate":"2022-09-19T14:49:55.9415","indexId":"70236801","displayToPublicDate":"2020-06-30T09:47:34","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Automated extraction of areal extents for GNIS Summit features using the eminence core method","docAbstract":"An important objective of the U.S. Geological Survey (USGS) is to enhance the Geographic Names Information System (GNIS) by automatically associating boundaries with terrain features that are currently spatially represented as two-dimensional points. In this paper, the discussion focuses on experiments for mapping GNIS Summit features using the eminence core region-growing method, which maps the area between a peak and its key col (saddle). A secondary goal of this project is to improve the positional accuracy of GNIS Summit features, since those locations were derived long ago and need to be snapped to local morphometric peaks detected from analysis of the highest-resolution digital elevation models (DEMs). The eminence cores delineated for a subset of GNIS Summit features were compared visually against basemaps and manually digitized polygons created by USGS staff. The comparisons revealed substantial differences between the computationally derived eminence cores and the manually generated polygons. Results clearly suggest that the default core delineation method tested must be modified to “roll back” or truncate growth of unreasonably large cores to smaller extents that would match people’s intuitive expectations. However, these results are far more encouraging than any method tested previously, since this method guarantees a 1-1 correspondence between polygons and GNIS Summit features.
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